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
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摘要: 本文基于Landsat 遥感影像,引入归一化山地植被指数(NDMVI),改进RUSLE模型中的植被管理因子C,得到改进的RUSLE模型,并利用其对湖南省龙山县2000~2020 年的土壤侵蚀进行估算,旨在快速、科学地评价研究区水土流失变化情况,为以龙山县为代表的西南土石山区水土流失治理提供科学依据。2000 年的NDMVI 数值范围较均一化植被指数(NDVI)增加了0.3158,2020 年的NDMVI 数值较NDVI 增加了0.2076,增加幅度均较大,这说明NDMVI 区分地物的能力更强,具有较强地消除复杂地形影响的能力。通过影像对比,可以看出NDMVI 区分地物的能力要优于NDVI,提取城镇用地、水体等地物的精度更高,尤其在地形起伏地区以及山坡的阴影地区,能更好地反演植被覆盖管理因子。基于山地植被指数修正的植被覆盖管理因子C 可以更准确地区分地物,尤其是在地形起伏和山坡阴影地区。该方法能有效地运用于西南土石山区的水土流失监测和评价,实现动态变化的快速定量监测。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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