DocumentCode :
1956838
Title :
Mapping Spatial Distribution of Soil Loss in the Upper Basin of Miyun Reservoir
Author :
Chen, Tao ; Niu, Rui-qing ; Li, Ping-xiang ; Zhang, Liang-pei
Author_Institution :
Inst. of Geophys. & Geomatics, China Univ. of Geosci., Wuhan, China
fYear :
2010
fDate :
10-16 Feb. 2010
Firstpage :
62
Lastpage :
67
Abstract :
This paper used the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss in the upper basin of Miyun reservoir in China. Among the soil erosion factors, the vegetative cover or C factor has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on a multivariate regression analysis, Back Propagation (BP) neural network and the results were compared with the values measured in the filed. 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 these factor maps in 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% lower, 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 basin of Miyun reservoir in China can be identified.
Keywords :
backpropagation; geographic information systems; geophysics computing; neural nets; regression analysis; reservoirs; soil; visual databases; C factor; Miyun reservoir; backpropagation neural network; broad geographic areas; correlation coefficient; geographic information system; multivariate regression analysis; pixel-based computing; revised universal soil loss equation; soil erosion factors; spatial distribution mapping; upper basin reservoir; vegetative cover; water erosion distribution; Area measurement; Distributed computing; Equations; Geographic Information Systems; Multivariate regression; Neural networks; Reservoirs; Soil measurements; Water conservation; Water resources; GIS; Miyun Reservior; RUSLE; Soil loss;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Geographic Information Systems, Applications, and Services (GEOPROCESSING), 2010 Second International Conference on
Conference_Location :
St. Maarten
Print_ISBN :
978-1-4244-5809-7
Type :
conf
DOI :
10.1109/GEOProcessing.2010.17
Filename :
5437976
Link To Document :
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