Effect of vegetation cover on soil erosion in a mountainous watershed

Abstract

Abstract We applied the Revised Soil Loss Equation (RUSLE) to assess levels of soil loss in a Geographic Information System (GIS). In this study, we used the k-NN technique to estimate vegetation cover by integrating Landsat ETM+ scenes and field data with optimal parameters. We evaluated the root mean square errors and significance of biases at the pixel level in order to determine the optimal parameters. The accuracy of vegetation cover estimation by the k-NN technique was compared to that predicted by a regression function using Landsat ETM+ bands and field measurements as well as to that predicted by the Normalized Difference Vegetation Index (NDVI). We used a regression equation to calculate the cover management (C) factor of the RUSLE from vegetation cover data. On the basis of the quantitative model of soil erosion, we explored the relationship between soil loss and its influencing factors, and identified areas at high erosion risk. The results showed that the k-NN method can predict vegetation cover more accurately for image pixels at the landscape level than can the other two methods examined in this study. Of those factors, the C-factor is one of the most important affecting soil erosion in the region. Scenarios with different vegetation cover on high-risk areas showed that greater vegetation cover can considerably reduce the loss of soil erosion. The k-NN technique provides a new method to estimate the C-factor for RUSLE erosion mapping. The quantitative model of different vegetation cover scenarios provides information on how vegetation restoration could reduce erosion.