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2023 Vol. 39, No. 4
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LONG Si-Jia, TANG Yuan-Yuan, DAI Liang-Liang, QIAO Shuang, FAN Wang-Dong, SHE Xiong, KONG Wei-Wei. 2023. Evaluation of Soil and Water Loss in the Rocky Mountain Area of Southwest China Based on Improved RUSLE Model: Taking Longshan County, Hunan Province as an Example. South China Geology, 39(4): 704-712. doi: 10.3969/j.issn.2097-0013.2023.04.011
Citation: LONG Si-Jia, TANG Yuan-Yuan, DAI Liang-Liang, QIAO Shuang, FAN Wang-Dong, SHE Xiong, KONG Wei-Wei. 2023. Evaluation of Soil and Water Loss in the Rocky Mountain Area of Southwest China Based on Improved RUSLE Model: Taking Longshan County, Hunan Province as an Example. South China Geology, 39(4): 704-712. doi: 10.3969/j.issn.2097-0013.2023.04.011
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Evaluation of Soil and Water Loss in the Rocky Mountain Area of Southwest China Based on Improved RUSLE Model: Taking Longshan County, Hunan Province as an Example

  • 1. Changsha Natural Resources Comprehensive Survey Center, China Geological Survey, Changsha 410600, Hunan, China

  • 2. School of Earth Science and Resouces, China University of Geosciences(Beijing), Beijing 100083, China

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  • Corresponding author: TANG Yuan-Yuan  
Received Date:  08 August 2023
Revised Date:  05 October 2023
    Abstract
  • The RUSLE model, the vegetation management factor C of which is improved based on Landsat remote sensing images and the Normalized Mountain Vegetation Index (NDMVI), is used to estimate soil erosion from 2000 to 2020 in Longshan County, Hunan Province. The aim is to quickly and scientifically evaluate the changes in soil erosion in the study area, and provide a scientific basis for soil erosion control in southwestern rocky mountainous areas represented by Longshan County. The NDMVI value range in 2000 increased by 0.3158 compared to the Normalized Vegetation Index (NDVI), and the NDMVI value range in 2020 increased by 0.2076 compared to NDVI, with significant increases. This indicates that it is easier to use NDMVI to distinguish features and eliminate complex terrain impacts. Through image comparison, it can be seen that it is better to use NDMVI to distinguish land features than NDVI, and the accuracy of extracting urban land, water bodies, and other land features is higher, especially in areas with undulating terrain and shaded slopes, which can better invert vegetation cover management factors. The vegetation cover management factor C within mountain vegetation index correction can be used to more accurately distinguish land features, especially in areas with undulating terrain and shaded slopes. This method can be effectively applied to the monitoring and evaluation of soil and water loss in southwestern mountainous areas, achieving rapid and quantitative monitoring of dynamic changes.
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