Soil Degradation Risk Prediction Integrating RUSLE with Geo-information Techniques, the Case of Northern Shaanxi Province in China
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Copyright: © 2020 Mushtak T. Jabbar and Xiaoling Chen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This research integrated the Revised Universal Soil Loss Equation (RUSLE) with RS, GIS and GPS techniques to quantify soil erosion risk and the northern part of Shaanxi province in China was taken as a case. A system was established for rating soil erodibility, slope length/gradient, rainfall erosivity and conservation practices. The rating values served as inputs into a modified Revised Universal Soil Loss Equation (RUSLE) to calculate the risk for soil degradation processes, namely, soil water erosion. Two Landsat TM senses in 1987 and 1999, respectively, were used to produce land use/ cover maps of the study area based on the maximum likelihood classification method. These maps were then used to generate the conservation practice factor in the RUSLE. ERmapper and Arc/Info software were used to manage and manipulate thematic data, to process satellite images and tabular data source. In term of statistic analysis 3985.9 km2 (33.12%) of land area had slight to moderate soil erosion risk, 1583.5 km2 (13.16%) had moderately high soil erosion risk, 2941.4 km2 (24.44%) had high soil erosion risk and 3522.1 km2 (29.27%) of the total land area was in a very high soil erosion risk. The study area, in general, is exposed to high risk of soil water erosion.
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- Geoinformation Techniques
- Soil Erosion