DC-19 - Ground Verification and Accuracy Assessment
Spatial products such as maps of land cover, soil type, wildfire, glaciers, and surface water have become increasingly available and used in science and policy decisions. These maps are not without error, and it is critical that a description of quality accompany each product. In the case of a thematic map, one aspect of quality is obtained by conducting a spatially explicit accuracy assessment in which the map class and reference class are compared on a per spatial unit basis (e.g., per 30m x 30m pixel). The outcome of an accuracy assessment is a description of quality of the end-product map, in contrast to conducting an evaluation of map quality as part of the map production process. The accuracy results can be used to decide if the map is of adequate quality for an intended application, as input to uncertainty analyses, and as information to improve future map products.
FC-27 - Thematic Accuracy Assessment
Geographic Information System (GIS) applications often involve various analytical techniques and geographic data to produce thematic maps for gaining a better understanding of geospatial situations to support spatial decisions. Accuracy assessment of a thematic map is necessary for evaluating the quality of the research results and ensuring appropriate use of the geographic data. Thematic accuracy deals with evaluating the accuracy of the attributes or labels of mapped features by comparing them to a reference that is assumed to be true. The fundamental practice presents the remote sensing approach to thematic accuracy assessment as a good guidance. For instance, the accuracy of a remote sensing image can be represented as an error matrix when the map and reference classification are conducted based on categories. This entry introduces basic concepts and techniques used in conducting thematic accuracy with an emphasis on land cover classification based on remote sensing images. The entry first introduces concepts of spatial uncertainty and spatial data quality standards and further gives an example of how spatial data quality affects thematic accuracy. Additionally, the entry illustrates the techniques that can be used to access thematic accuracy as well as using spatial autocorrelation in thematic accuracy sampling design.