Broad area mapping of monthly soil erosion risk using fuzzy decision tree approach: integration of multi-source data within GIS

Abstract

Soil erosion poses a serious problem for sustainable agriculture and the environment. There is a need to develop a simple and practical approach for broad area mapping of soil erosion risk that uses the uncertain but available information as input data within the constraints of reasonable cost and time. In this work, a predictive approach for conducting analytical erosion risk assessment across broad areas is developed, which combines a fuzzy decision tree (FDT), remote sensing and Geographic Information System (GIS). This approach is applicable to situations with a limited amount of input data and can easily adjust assessment factors according to actual need. In this study, four dominating factors affecting soil erosion were considered: soil, topography, land cover and climate. GIS thematic layers of these factors were constructed followed by fuzzified analysis through trapezoidal shaped membership functions. Based on subdivided erosion response units (ERUs), an optimal FDT was determined to classify monthly soil erosion risk into five levels. High-risk and very high-risk soil erosion in the study area is mainly concentrated from June to August, with July and August showing the highest risk covering more than 80% of the study area. November to March is dominated by low risk over more than 90% of the study area, while medium risk is dominant in April, May, September and October. Compared with field survey data, the fuzzy decision erosion risk assessment approach was shown to be applicable and economical for rapidly identifying and locating soil erosion risk with limited input data by means of remote sensing and GIS.