Soil-landscape modelling 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 data derived from a digital elevation model to represent transition zones in the soil-landscape. The conventional geographic model used for soil-landscape description is not able to properly deal with these. Fuzzy c-means clustering was applied to a hillslope within a small drainage basin in southern Spain. Cluster validity evaluation was based on the coefficient of determination of regressing topsoil clay data on membership grades. The resulting clusters occupied spatially contiguous areas. 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 characteristics. Therefore we concluded that the coefficient of determination of regressing soil sample data on membership grades efficiently supports deciding upon the optimum fuzzy c-partition. The study confirms that fuzzy c-means clustering of terrain attribute data enhances conventional soil-landscape modelling, as it allows representation of fuzziness inherent to soil-landscape units.