Digital Mapping of Soil Classes Using Decision Tree and Auxiliary Data in the Ardakan Region, Iran

Abstract

Digital soil mapping (DSM) involves acquisition of field soil observations and matching them with environmental variables that can explain the distribution of soils. The harmonization of these data sets, through computer-based methods, are increasingly being found to be as reliable as traditional soil mapping practices, but without the prohibitive costs. Therefore, the present research developed decision tree models for spatial prediction of soil classes in a 720 km2 area located in an arid region of central Iran, where traditional soil survey methods are difficult to undertake. Using the conditioned Latin hypercube sampling method, the locations of 187 soil profiles were selected, which were then described, sampled, analyzed, and allocated to six Great Groups according to the USDA Soil Taxonomy system. Auxiliary data representing the soil forming factors were derived from a digital elevation model (DEM), Landsat 7 ETM+ images, and a map of geomorphology. The accuracy of the decision tree models was evaluated using overall, user, and producer accuracy based on an independent validation data set. Our results showed some auxiliary variables had more influence on the prediction of soil classes which included: topographic wetness index, geomorphological map, multiresolution index of valley bottom flatness, elevation, and principal components of Landsat 7 ETM+ images. Furthermore, the results have confirmed the DSM model successfully predicted Great Groups with overall accuracy up to 67.5%. Our results suggest that the developed methodology could be used to predict soil classes in the arid region of Iran.