DocumentCode
2706705
Title
Estimation of the USLE cover and management factor C using satellite remote sensing: A review
Author
Zhang, Weiwei ; Zhang, Zengxiang ; Liu, Fang ; Qiao, Zhuping ; Hu, Shunguang
Author_Institution
Inst. of Remote Sensing Applic., Chinese Acad. Sci., Beijing, China
fYear
2011
fDate
24-26 June 2011
Firstpage
1
Lastpage
5
Abstract
Soil erosion has been one of the worldwide environmental disasters which severely threaten the sustainable development of socio-economic, natural resources, and the environment. The Universal Soil Loss Equation (USLE) is the most widely used model to quantify soil erosion. The cover and management factor C is perhaps the most important USLE factor because it represents conditions that can most easily be managed to reduce erosion. Satellite remote sensing can contribute through providing spatial data to assessment of C factor. Thus, many studies have been launched during the past 40 years. The paper mainly discusses the spatial data that is extracted from remote sensing images for estimating C factor: (1) land cover classification map, (2) image bands or ratios, (3) vegetation indices, (4) vegetation coverage. It is concluded that satellite remote sensing has been indispensible in C factor studies and its application need to penetrate deeply in future.
Keywords
disasters; erosion; hydrological techniques; soil; terrain mapping; vegetation mapping; USLE cover estimation; USLE factor assessment; USLE management factor C; environmental disasters; image bands; image ratios; land cover classification map; remote sensing images; satellite remote sensing; soil erosion quantification; spatial data; sustainable development; universal soil loss equation; vegetation coverage; vegetation indices; Agriculture; Earth; Mathematical model; Remote sensing; Satellites; Soil; Vegetation mapping; C factor; USLE; semote Sensing; vegetation coverage; vegetation index;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoinformatics, 2011 19th International Conference on
Conference_Location
Shanghai
ISSN
2161-024X
Print_ISBN
978-1-61284-849-5
Type
conf
DOI
10.1109/GeoInformatics.2011.5980735
Filename
5980735
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