Wu Chang-Guang, Li Sheng, Ren Hua-Dong, Yao Xiao-Hua, Huang Zi-Jie
Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Zhejiang.
Ying Yong Sheng Tai Xue Bao. 2012 Jun;23(6):1728-32.
Soil loss prediction models such as universal soil loss equation (USLE) and its revised universal soil loss equation (RUSLE) are the useful tools for risk assessment of soil erosion and planning of soil conservation at regional scale. To make a rational estimation of vegetation cover and management factor, the most important parameters in USLE or RUSLE, is particularly important for the accurate prediction of soil erosion. The traditional estimation based on field survey and measurement is time-consuming, laborious, and costly, and cannot rapidly extract the vegetation cover and management factor at macro-scale. In recent years, the development of remote sensing technology has provided both data and methods for the estimation of vegetation cover and management factor over broad geographic areas. This paper summarized the research findings on the quantitative estimation of vegetation cover and management factor by using remote sensing data, and analyzed the advantages and the disadvantages of various methods, aimed to provide reference for the further research and quantitative estimation of vegetation cover and management factor at large scale.
诸如通用土壤流失方程（USLE）及其修订版通用土壤流失方程（RUSLE）等土壤流失预测模型，是区域尺度土壤侵蚀风险评估和土壤保持规划的有用工具。合理估算植被覆盖度和管理因子（USLE或RUSLE中最重要的参数），对于准确预测土壤侵蚀尤为重要。基于实地调查和测量的传统估算方法耗时、费力且成本高昂，无法在宏观尺度上快速提取植被覆盖度和管理因子。近年来，遥感技术的发展为大面积估算植被覆盖度和管理因子提供了数据和方法。本文总结了利用遥感数据定量估算植被覆盖度和管理因子的研究成果，分析了各种方法的优缺点，旨在为大规模植被覆盖度和管理因子的进一步研究及定量估算提供参考。
