Environmental Monitoring and Assessment

, Volume 179, Issue 1–4, pp 605–617 | Cite as

Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques

  • Tao Chen
  • Rui-qing Niu
  • Yi Wang
  • Ping-xiang Li
  • Liang-pei Zhang
  • Bo Du


Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), vegetation cover (C), and support practice factor (P), the vegetative cover or C factor, which represents the effects of vegetation canopy and ground covers in reducing soil loss, has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on back propagation neural network and the results were compared with the values measured in the field. The correlation coefficient (r) obtained was 0.929. Then the C factor and the other factors were used as the input to RUSLE model. By integrating the six factor maps in geographical information system (GIS) through pixel-based computing, the spatial distribution of soil loss over the upper basin of Miyun reservoir was obtained. The results showed that the annual average soil loss for the upper basin of Miyun reservoir was 9.86 t ha − 1 ya − 1 in 2005, and the area of 46.61 km2 (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.9% very low, 21.89% low, 6.18% moderate, 2.89% severe, and 1.84% very severe. Thus, by using RUSLE in a GIS environment, the spatial distribution of water erosion can be obtained and the regions which susceptible to water erosion and need immediate soil conservation planning and application over the upper watershed of Miyun reservoir in China can be identified.


Soil loss RUSLE BP neural network GIS Remote sensing Miyun reservoir 


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Tao Chen
    • 1
  • Rui-qing Niu
    • 1
  • Yi Wang
    • 1
  • Ping-xiang Li
    • 2
  • Liang-pei Zhang
    • 2
  • Bo Du
    • 3
  1. 1.Institute of Geophysics and GeomaticsChina University of GeosciencesWuhanChina
  2. 2.State Key Lab of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  3. 3.Computer SchoolWuhan UniversityWuhanChina

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