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MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY
Citation:
Tran, L. T., S T. Jarnagin, C. G. Knight, AND L. Baskaran. MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY. Chapter 13, Ross Lunetta & John Lyon (ed.), Remote Sensing and GIS Accuracy Assessment. Taylor & Francis Books, Inc., Boca Raton, FL, (2004).
Impact/Purpose:
The primary objectives of this research are to:
Develop methodologies so that landscape indicator values generated from different sensors on different dates (but in the same areas) are comparable; differences in metric values result from landscape changes and not differences in the sensors;
Quantify relationships between landscape metrics generated from wall-to-wall spatial data and (1) specific parameters related to water resource conditions in different environmental settings across the US, including but not limited to nutrients, sediment, and benthic communities, and (2) multi-species habitat suitability;
Develop and validate multivariate models based on quantification studies;
Develop GIS/model assessment protocols and tools to characterize risk of nutrient and sediment TMDL exceedence;
Complete an initial draft (potentially web based) of a national landscape condition assessment.
This research directly supports long-term goals established in ORDs multiyear plans related to GPRA Goal 2 (Water) and GPRA Goal 4 (Healthy Communities and Ecosystems), although funding for this task comes from Goal 4. Relative to the GRPA Goal 2 multiyear plan, this research is intended to "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors." Relative to the Goal 4 Multiyear Plan this research is intended to (1) provide states and tribes with an ability to assess the condition of waterbodies in a scientifically defensible and representative way, while allowing for aggregation and assessment of trends at multiple scales, (2) assist Federal, State and Local managers in diagnosing the probable cause and forecasting future conditions in a scientifically defensible manner to protect and restore ecosystems, and (3) provide Federal, State and Local managers with a scientifically defensible way to assess current and future ecological conditions, and probable causes of impairments, and a way to evaluate alternative future management scenarios.
Description:
The accuracy of thematic map products is not spatially homogenous, but instead variable across most landscapes. Properly analyzing and representing the spatial distribution (pattern) of thematic map accuracy would provide valuable user information for assessing appropriate applications for land-cover (LC) maps and other derived products (i.e., landscape metrics). However, current thematic map accuracy measures, including the confusion or error matrix (Story and Congalton, 1986), Kappa coefficient of agreement (Congalton and Green, 1999), are inadequate in analyzing the spatial variation of thematic map accuracy. They are not able to answer several important scientific and application-oriented questions related to thematic map accuracy. For example, are errors distributed randomly across space? Do different cover types have the same spatial accuracy pattern? How do spatial accuracy patterns affect products derived from thematic maps? Within this context, methods for displaying and analyzing the spatial accuracy of thematic maps and bringing the spatial accuracy information into other calculations, such as deriving landscape indicators from thematic maps, are important issues to advance scientifically appropriate applications remotely sensed image information.
Our study objective was to use fuzzy set approach to examine and display the spatial accuracy pattern of thematic LC maps and to combine uncertainty with the computation of landscape indicators (metrics) derived from thematic maps. The chapter is organized by (i) current methods for analyzing and mapping thematic map accuracy, (ii) presentation of our methodology for constructing fuzzy LC maps, and (iii) deriving landscape indicators from fuzzy maps.