title: [Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data: a review].
abstract: 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.
authors.affiliation: Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Zhejiang. wcg1129@163.com
authors.forename: Chang-Guang
authors.initials: CG
authors.lastname: Wu
authors.forename: Sheng
authors.initials: S
authors.lastname: Li
authors.forename: Hua-Dong
authors.initials: HD
authors.lastname: Ren
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Yao
authors.forename: Zi-Jie
authors.initials: ZJ
authors.lastname: Huang
mesh_terms: D003247:Conservation of Natural Resources / Q000379:methods*; D017753:Ecosystem; D004784:Environmental Monitoring; D008962:Models, Theoretical; D063245:Plant Development; D058998:Remote Sensing Technology; D012987:Soil / Q000737:chemistry*
chemical_list: D012987:Soil