A Landform Classification Approach to Fuzzy Logic Inference Mapping.

Fuzzy set theory is an established, well tested method for generating reliable predictive soil models and digital soil maps. Often time expert based soil knowledge is implemented as the guiding force behind fuzzy logic prediction, utilizing the soil scientist’s personal knowledge of soil properties, and their relationship with covariant landscape attributes, to generate decision based fuzzy membership values. Although these methods have the capability to adequately predict soil properties, the knowledge driven definition of rules and distributions are often left murky and under defined, making it difficult for other scientist to replicate the results. In order to predict soil properties for large regions, where one set of heuristic rules may not apply, it is vital to develop a method for fuzzy classification which limits the application of individual knowledge, allowing for automated rule threshold setting, reproducibility, and timeliness. For this study, we will explore an approach to fuzzy logic mapping which involves predefined hard classification based on landscape position, followed by the generation of fuzzy membership values, through automated rule setting, driven by the actual distribution of landscape attributes with a given class. The goal of this study is to generate a flexible model which can be implemented on most landscapes typically found to occur in the glaciated region of the Midwestern United States. Mapping of a farm field level site in Southeastern Indiana for functional soil classes and properties took place. Results will be presented.