Grade agricultural soil loss amount by integrating GIS and USLE in upstream of Yangtze River, China

Shi-bo Fang; Shi-liang Liu; Zhiqiang Gao

doi:10.1117/12.614936

1 September 2005 Grade agricultural soil loss amount by integrating GIS and USLE in upstream of Yangtze River, China

Shi-bo Fang, Shi-liang Liu, Zhiqiang Gao

Proceedings Volume 5884, Remote Sensing and Modeling of Ecosystems for Sustainability II; 58841B (2005) https://doi.org/10.1117/12.614936
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

Natural Forest Conservation Program (NFCP) was established in China in 1998. As a nationwide engineering project, one of the most important tasks was to mandate conversion of marginal farmlands to forestland or grassland (reforestation or afforestation in farmlands) where topographical slope steepness was more than 25 degree. In order to quantitatively grade agriculture soil loss amount and assess spatio-temporal variation of soil erosion volume, as well as to guide the process of the conversion of farmlands to forest in upstream of Yangtze Rivers. The research analyzed how to calculate K, S, L, C and P factors of Universal Soil Loss Equation (USLE) on the base of considering terrain, climate and characters of agricultural soil using GIS technology. The methods can adapt to the variation terrain of the dry valley region in upstream of Yangtze Rivers. As believed the reforestation was a long-term engineering, the planning of reforest farmlands should be a temporal decision process, which should be considered that where should be afforested at once and where should afforested in abeyance on the basis of grading soil loss amount and the others conditions of farmland. Grading agricultural soil loss amount would be beneficial to program a feasible plan in the conversation of farmlands to forest.

Citation Download Citation

Shi-bo Fang, Shi-liang Liu, and Zhiqiang Gao "Grade agricultural soil loss amount by integrating GIS and USLE in upstream of Yangtze River, China", Proc. SPIE 5884, Remote Sensing and Modeling of Ecosystems for Sustainability II, 58841B (1 September 2005); https://doi.org/10.1117/12.614936

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