Assessment of Soil Erosion Susceptibility Using Empirical Modeling

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  • Funds:

    Supported by the Special Project on Public Welfare of Forestry (200804020), China Meteorological Administration Special Public Welfare Research Fund (GYHY201206040), National Science and Technology Support Program of China (2008BAC40B02), National Natural Science Foundation of China (41171294 and 40901169), Chinese Academy of Meteorological Sciences Basic Research Project (2011Y002 and 2010Z002), and One Hundred Talents Program of the Chinese Academy of Sciences.

  • doi: 10.1007/s13351-013-0110-2

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  • Soil erosion is one of the most serious land degradation problems all over the world, causing irreversible land quality reduction. In this paper, we modify the Revised Universal Soil Loss Equation (RUSLE) model by replacing the factors of slope length and gradient with Sediment Transport Index (STI). The Digital Elevation Model, terrain parameters, Normalized Difference Vegetation Index (NDVI), and rainfall data are used as inputs to the model. Along with the application of remote sensing techniques and ground survey measurements, erosion susceptibility maps are produced. The revised models are then used to obtain the optimal estimate of soil erosion susceptibility at Alianello of southern Italy, which is prone to soil erosion. The soil loss estimated from the modified RUSLE model shows a large spatial variance, ranging from 10 to as much as 7000 ton ha?1 yr?1. The high erosion susceptible area constitutes about 46.8% of the total erosion area, and when classified by land cover type, 33% is “mixed bare with shrubs and grass”, followed by 5.29% of “mixture of shrubs and trees”, with “shrubs” having the lowest percentage of 0.06%. In terms of slope types, very steep slope accounts for a total of 40.90% and belongs to high susceptibility, whereas flat slope accounts for only 0.12%, indicating that flat topography has little effect on the erosion hazard. As far as the geomorphologic types are concerned, the type of “moderate steep–steep slopes with moderate to severe erosion” is most favorable to high soil erosion, which comprises about 9.34%. Finally, we validate the soil erosion map from the adapted RUSLE model against the visual interpretation map, and find a similarity degree of 71.9%, reflecting the efficiency of the adapted RUSLE model in mapping the soil erosion in this study area.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Assessment of Soil Erosion Susceptibility Using Empirical Modeling

  • 1. Institute of Atmospheric Composition Observation and Services,Chinese Academy of Meteorological Sciences, Beijing 100081,China;
    Institute of Atmospheric Composition Observation and Services,Chinese Academy of Meteorological Sciences, Beijing 100081,China;
    Institute of Geo-Information Science and Earth Observation of the University of Twente,7500 AA Enschede,the Netherlands;
    School of Resources and Environment,University of Electronic Science and Technology of China,Chengdu 611731,China;
    Shanxi Meteorological Observatory,Taiyuan 030006,China;
    Center for Earth Observation and Digital Earth,Chinese Academy of Sciences,Beijing 100094,China
Funds: Supported by the Special Project on Public Welfare of Forestry (200804020), China Meteorological Administration Special Public Welfare Research Fund (GYHY201206040), National Science and Technology Support Program of China (2008BAC40B02), National Natural Science Foundation of China (41171294 and 40901169), Chinese Academy of Meteorological Sciences Basic Research Project (2011Y002 and 2010Z002), and One Hundred Talents Program of the Chinese Academy of Sciences.

Abstract: Soil erosion is one of the most serious land degradation problems all over the world, causing irreversible land quality reduction. In this paper, we modify the Revised Universal Soil Loss Equation (RUSLE) model by replacing the factors of slope length and gradient with Sediment Transport Index (STI). The Digital Elevation Model, terrain parameters, Normalized Difference Vegetation Index (NDVI), and rainfall data are used as inputs to the model. Along with the application of remote sensing techniques and ground survey measurements, erosion susceptibility maps are produced. The revised models are then used to obtain the optimal estimate of soil erosion susceptibility at Alianello of southern Italy, which is prone to soil erosion. The soil loss estimated from the modified RUSLE model shows a large spatial variance, ranging from 10 to as much as 7000 ton ha?1 yr?1. The high erosion susceptible area constitutes about 46.8% of the total erosion area, and when classified by land cover type, 33% is “mixed bare with shrubs and grass”, followed by 5.29% of “mixture of shrubs and trees”, with “shrubs” having the lowest percentage of 0.06%. In terms of slope types, very steep slope accounts for a total of 40.90% and belongs to high susceptibility, whereas flat slope accounts for only 0.12%, indicating that flat topography has little effect on the erosion hazard. As far as the geomorphologic types are concerned, the type of “moderate steep–steep slopes with moderate to severe erosion” is most favorable to high soil erosion, which comprises about 9.34%. Finally, we validate the soil erosion map from the adapted RUSLE model against the visual interpretation map, and find a similarity degree of 71.9%, reflecting the efficiency of the adapted RUSLE model in mapping the soil erosion in this study area.

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