文献类型：期刊文献

中文题名：USLE/RUSLE模型中植被覆盖管理因子的遥感定量估算研究进展

英文题名：Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data:A review

作者：吴昌广[1] 李生[1] 任华东[1] 姚小华[1] 黄子杰[2]

第一作者：吴昌广

机构：[1]中国林业科学研究院亚热带林业研究所;[2]湖北省林业勘测设计院

年份：2012

卷号：23

期号：6

起止页码：1728-1732

中文期刊名：应用生态学报

外文期刊名：Chinese Journal of Applied Ecology

收录：CSTPCD;;Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2011_2012】；PubMed;

基金：林业公益性行业科研专项(201004033);农业科技成果转化项目(2009GB24320475);中国林业科学研究院中央级公益性科研院所基本科研业务费专项(CAFYBB2007033)资助

语种：中文

中文关键词：土壤侵蚀;USLE/RUSLE;模型;植被覆盖管理因子;遥感

外文关键词：soil erosion; USLE/RUSLE model; vegetation cover and management factor; remote sensing.

分类号：S127

摘要：通用土壤流失方程(USLE)及其后续修正方程(RUSLE)是区域土壤侵蚀风险评估和水土保持规划的有效工具.植被覆盖管理因子作为USLE和RUSLE的重要参数之一,其合理估算对土壤侵蚀的准确预测尤为重要.基于野外实地调查和测量的传统估算法费时、费力且费用高,无法满足宏观尺度上植被覆盖管理因子的快速提取.近年来,遥感技术的发展为大尺度植被覆盖管理因子获取提供了丰富的数据和方法.本文基于国内外相关研究成果,综述了植被覆盖管理因子遥感定量估算方法的研究进展,评述了各类方法的优劣,以期为进一步开展大尺度植被覆盖管理因子的定量估算及拓展现有研究思路提供借鉴.
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.