Mapping with Words: A New Approach to Automated Digital Soil Survey

Soil Survey (soil mapping) is based oil soil-landscape knowledge of soil scientists. Current Current automated approaches to soil survey cannot take such knowledge as direct input, because the knowledge is descriptive in nature. This paper presents it "mapping with words" solution by using fuzzy logic. Environmental variables used to describe landscape conditions are treated its linguistic variables. Each descriptive term used to characterize an environmental variable is treated its it fuzzy granule and is represented with a fuzzy membership function. Fuzzy membership functions are defined through gathering samples of expert perception on the landscape. Using the granule-fuzzy membership functions pairs its it dictionary. in inference call decode input descriptive knowledge accordingly and conduct soil inference. The proposed approach has been tested ill it case study ill Dane County. Wisconsin, USA via a soil inference approach (soil-land inference model, SoLIM). The mapping result shows that the mapping with word version of SoLIM has all 85% accuracy based on collected field points, better than a comparable earlier version (about 78%). Traditional soil survey maps usually have it mapping accuracy about 60%. The propose methodology call lie adapted to other knowledge-based natural resource mapping with slight modifications. (c) 2009 Wiley Periodicals. Inc.