Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin

Abstract

Numerical classification methods may provide an alternative to manual landform delineation using aerial photographs, a subjective process that requires much knowledge of the landscape in question. Continuous classification (fuzzy set) methods and unsupervised (ISODATA) classification techniques were used to classify the landscape of a study area in southwestern Wisconsin, USA. Each pixel of a 10-m resolution digital elevation model (DEM) was grouped according to its membership in a continuous landform class. These classes were determined by the natural clustering of the data in attribute space. Attributes used for the classification were elevation, slope, profile and tangent (related to plan) curvature, compound topographic (wetness) index, and incident solar radiation. The ISODATA classification assigned pixels to one, and only one, landform class while the continuous classification allocated relative class memberships to each pixel. The resulting classifications roughly follow subjective manual delineation lines but give more detailed results. These classification methods may prove useful for statistical analyses and determination of sample schemes.