You are here:
MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY
Citation:
Tran, L. T., S T. Jarnagin, J D. Wickham, AND L. Baskaran. MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY. Presented at Remote Sensing and GIS Accuracy Assessment Symposium, Las Vegas, NV, December 11-13, 2001.
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:
This paper presents a fuzzy set-based method of mapping spatial accuracy of thematic map and computing several ecological indicators while taking into account spatial variation of accuracy associated with different land cover types and other factors (e.g., slope, soil type, etc.). It is evident that the pattern and degree of spatial variation of thematic map accuracy need to be understood and represented properly. Furthermore, such information should be accounted for in other analyses using thematic maps as source of data (e.g., derivation of ecological indicators from thematic maps). However there are not many studies on this issue except some focusing on map area estimation (e.g., Woodcock and Gopal, 2000). In this paper we formulate a concept of degree of agreement (DA) with respect to different land cover types at each grid point on a thematic map using fuzzy sets. On the other hand, information from reference source are also represented by fuzzy sets as they are not always certain in many cases (Yang et al., 2001), a feature not seen in other studies. Our aim is to use the spatial accuracy of thematic maps to compute DA at each grid point with respect to different land cover types.
First we construct a matrix of degree of similarity (DS) between different land cover types (e.g., DS between low and high density residential is 0.6; 0.0 for DS between water and evergreen forest). Then the accuracy assessment is carried out by comparing the agreement between the assigned land cover type on thematic map with information at reference source, using fuzzy intersection operation between fuzzy sets representing "true" land cover type and the assigned land cover type's DS values. Degree of accuracy is then interpolated from the reference points to the whole map via Kriging. The derived spatial accuracy map in turns is used with information at reference source to formulate a set of fuzzy if-then rules representing relationship between spatial accuracy, land cover type and DA with respect to different land cover types. For example, a single fuzzy rule can be stated as follows: "if spatial accuracy is low and the assigned land cover on the thematic map is type A then the degree of agreement at that grid point for type A is 0.5; for type B is 0.3; type C 0.1, etc.." The overall DA at a particular location is a combination of the fuzzy rule responses via weighted-sum combination method. Information on DA is then used in further calculations of several ecological indicators, including percent forest cover, forest fragmentation, forest and agricultural land cover along streams. For illustration purpose, we apply the method to a portion of the Mid-Atlantic region. The method is found not only to provide valuable information on the spatial distribution of map accuracy but also to implement a viable tool to include degree of accuracy into other calculations/analyses using information from thematic maps.