AUTOMATED SOIL INFERENCE UNDER FUZZY-LOGIC

Soil information is essential to any terrestrial ecological modelling and management activity. Polygon soil maps produced from soil surveys are currently the major source of information on the spatial distribut ion of soil properties for a variety of land analysis and management a ctivity. However, there are some major problems regarding the use of c urrent soil maps in geographic analysis and especially in geographic i nformation systems (GIS). These problems include limited coverage at a fixed scale, locational errors, attribute errors, and insufficient in formation in the mapping units. Much of these problems are due to the crisp logic and cartographic techniques with which soil maps are produ ced. Under crisp logic standardly used in soil classification and mapp ing, an area belongs to one and only one soil mapping unit, and is sep arated from other mapping units by sharp boundary lines. However, soil in a landscape is a continuum and the discretization of such a contin uum into distinct spatial and categorical groups results in a signific ant loss of information. This paper presents a methodology to infer an d represent information on the spatial distribution of soil. The metho dology combines fuzzy logic with GIS and expert system development tec hniques to infer soil series from environmental conditions. The method ology for every point in an area produces a soil similarity vector (SS V) showing the similarity of the soil at the point to a prescribed set of soil series. The SSV produced from this methodology can be used to infer local soil properties at values intermediate to the typical or central values assigned to each possible series. Preliminary results f rom the methodology using a limited set of environmental variables for an experimental watershed in Montana are presented.