Asep Yusup Saptari
Geodesy and Geomatic Department of Earth Science Faculty – ITB, Indonesia . Surveying and Cadaster Research Group - ITB

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Journal : Indonesian Journal of Geospatial

Remote Sensing Analysis In RUSLE Erosion Estimation Asep Yusup Saptari; Supriadi A; Ketut Wikantika; Darmawan S
Indonesian Journal of Geospatial Vol 4 No 1 (2015)
Publisher : Indonesian Journal of Geospatial

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Abstract

ABTRACT. Soil erosion is a major issue in various hemispheres. It is because erosion affects the survival of ecosystem. Diverse human actions, e.g., bushes burning and illegal logging, play a role in accelerating erosion. Climate factor such as rain intensity has also an influence in the release of soil particles. Therefore, a regular identification of those factors that affect erosion processes is highly needed in order to keep an environmental sustainable. Different areas in Indonesia have different erosion variable characteristics. One of the characteristics is indicated by the varieties of vegetation cover, where a loose vegetation cover causes soil surfaces open for a long time period.  Till now, researches dealing with the modeling of erosions with wide area coverage are few, since erosion observations have always been conducted by direct observations in the field, hence time consuming. Therefore, an erosion mapping model that is applied in a wide coverage area and the up to date of data is needed. Spatially, erosions can be depicted in a form of spatial information system model describing their potential class levels. There are several erosion models that can be used to find out the erosion occurring on a land, among others Universal Soil Loss Equation (USLE) model or its modification Revised Universal Soil Loss Equation (RUSLE). RUSLE erosion model consists of rainfall, soil erodibility, vegetation cover, slope gradient and length, and support practice factors. Recent technology in remote sensing allowed vegetation cover to be analysed from satellite imagery, make the possibility of erosion analysis in large area in shorter time. KEY WORD:  Erosion, Vegetation, Models, Remote Sensing, RUSLE