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DIVISION S-5 - PEDOLOGY

Soil Mapping Using GIS, Expert Knowledge, and Fuzzy Logic

^a Department of Geography, University of Wisconsin-Madison, 550 North Park Street, Madison, WI 53706
^b Soil Survey Interpretations, Natural Resources Conservation Service, 100 Centennial Mall North, Lincoln, NE 68508
^c NRCS–USDA, 6515 Watts Road, Suite 200, Madison, WI 53719
^d NRCS–USDA, 1850 Bohmann Drive, Suite C, Richland Center, WI 53581

* Corresponding author (axing{at}geography.wisc.edu)

A geographical information system (GIS) or expert knowledge-based fuzzy soil inference scheme (soil-land inference model, SoLIM) is described. The scheme consists of three major components: (i) a model employing a similarity representation of soils, (ii) a set of inference techniques for deriving the similarity representation, and (iii) use of the similarity representation. The similarity representation allows the soil landscape to be considered as a continuum, and thereby overcomes the generalization of soils in conventional soil mapping. The set of inference techniques is based on the soil factor equation and the soil–landscape model. The soil–landscape concept contends that if one knows the relationships between each soil and its environment for an area, then one is able to infer what soil might be at each location on the landscape by assessing the environmental conditions at that point. Under the SoLIM, soil environmental conditions over an area are characterized using GIS or remote sensing techniques. The relationships between soils and their formative environmental conditions are extracted from local soil experts or from field observations using a set of artificial intelligence techniques. The characterized environmental conditions are then combined with the extracted relationships to derive a similarity representation of soils over an area. It is demonstrated through two case studies that the SoLIM for soil survey has many advantages over the conventional soil survey approach. Soil information products derived through the SoLIM are of high quality in terms of both level of spatial detail and degree of attribute accuracy. In addition, the scheme shows promise for improving the efficiency of soil survey and subsequent updates through reducing time and costs of conducting a survey. However, the degree of success of the SoLIM highly depends on the availability and quality of environmental data, and the quality of knowledge on soil–environmental relationships over the study area.

Abbreviations: ANN, artificial neural network • CBR, case-based reasoning • GIS, geographical information system • SoLIM, soil-land inference model

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