Integration of Remote Sensing and GIS for Evaluating Soil Erosion Risk in Northwestern Zhejiang, China

Abstract

Estimation of soil loss or evaluation of soil erosion risk has been an active research topic and has had much attention in the past three decades. The application of Revised Universal Soil Loss Equation (RUSLE) in large areas is a challenge because of data availability and quality. The RUSLE model was used in this article to evaluate soil erosion risk based on soil samples, a soil type map, digital elevation model (DEM) data, and Landsat Thematic Mapper (TM) images. Multiple regression analysis was used in order to identify major factors that influence the risks of soil erosion. A regression model based on DEM-derived slope gradient and TM-derived fractional soil and vegetation images was developed to map soil erosion risk distribution in a forest ecosystem in Zhejiang, China. The method that was developed has the potential to quickly examine the spatial distribution of the risk of soil erosion. This article provides a new insight for the evaluation of soil erosion risks in forest ecosystems with the integration of remote sensing and geographic information systems (GIS).