A data fusion approach for soil erosion monitoring in the Upper Yangtze River Basin of China based on Universal Soil Loss Equation (USLE) model

Abstract

Soil losses and soil deposits in the Upper Yangtze River watershed have been increasing for decades, with serious negative effects on the agriculture along the Yangtze River and the Three Gorges Dam. The Chinese Government has carried out the Return Farmland into Trees (Grass) Project since the early 1990s. The effectiveness of the project has been estimated in some testing sites in the Upper Yangtze River watershed. In this paper, we carried out a test in the Minjiang River Valley, one of the largest river valleys in the Upper Yangtze River watershed. It was very difficult to calculate the amount of soil losses for the valley when a traditional sample method was used to collect the data for the Universal Soil Loss Equation (USLE model). In order to develop the capability to monitor soil losses in the Minjiang River Valley, we assessed the use of remote sensing data, DEM data, land use and land cover GIS data. The key point of the procedure was data fusion, which was based on an image pixel un-mixing technique. The values of parameters extracted from satellite sensor data for soil erosion USLE model calculation was based on pixels. In this way it improves the accuracy for every parameter calculation. Before making the final calculation, we carried out a field survey along the river valley in July 2000. There was ±5% soil erosion difference between our calculation results from Landsat Enhanced Thematic Mapper (ETM) data in 2000 and in situ statistical results in 1999. The method used in the Minjiang River Valley can be used as an example for soil loss calculation for other rivers in the Upper Yangtze River watershed.

Acknowledgments

This research was financially supported by Chinese Academy of Sciences (KZCX2-312) and Chinese National Key Project of Basic Research (G2000077904-2). Dr Yong Xue is also supported by the CAS Hundred Talents Program and the Initial Phase of the Knowledge Innovation Program funded by the Chinese Academy of Sciences (CAS).