SOIL-LANDSCAPE MODELING USING FUZZY C-MEANS CLUSTERING OF ATTRIBUTE DATA DERIVED FROM A DIGITAL ELEVATION MODEL (DEM)

This study explores the use of fuzzy c-means clustering of attribute d ata derived from a digital elevation model to represent transition zon es in the soil-landscape. The conventional geographic model used for s oil-landscape description is not able to properly deal with these. Fuz zy c-means clustering was applied to a hillslope within a small draina ge basin in southern Spain. Cluster Validity evaluation was based on t he coefficient of determination of regressing topsoil clay data on mem bership grades. The resulting clusters occupied spatially contiguous a reas. We found a high degree of association with measured topsoil clay data (r(a)(2) =0.68) for three clusters and a weighting exponent of 2 .1. Location of the clusters coincided with observable terrain charact eristics. Therefore we concluded that the coefficient of determination of regressing soil sample data on membership grades efficiently suppo rts deciding upon the optimum fuzzy c-partition. The study confirms th at fuzzy c-means clustering of terrain attribute data enhances convent ional soil-landscape modelling, as it allows representation of fuzzine ss inherent to soil-landscape units. (C) 1998 Elsevier Science B.V.