Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala, India

Abstract

Siruvani watershed with a surface area of 205.54 km 2 (20,554 hectare), forming a part of the Western Ghats in Attapady valley, Kerala, was chosen for testing RUSLE methodology in conjunction with remote sensing and GIS for soil loss prediction and identifying areas with high erosion potential. The RUSLE factors (R, K, LS, C and P) were computed from local rainfall, topographic, soil classification and remote sensing data. This study proved that the integration of soil erosion models with GIS and remote sensing is a simple and effective tool for mapping and quantifying areas and rates of soil erosion for the development of better soil conservation plans. The resultant map of annual soil erosion shows a maximum soil loss of 14.917 t h -1 year -1 and the computations suggest that about only 5.76% (1,184 hectares) of the area comes under the severe soil erosion zone followed by the higherosion zone (11.50% of the total area). The dominant high soil erosion areas are located in the central and southern portion of the watershed and it is attributed to the shifting cultivation, and forest degradation along with the combined effect of K, LS and C factor. The RUSLE model in combination with GIS and remote sensing techniques also enables the assessment of pixel based soil erosion rate.

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

“…Information on factors leading to soil erosion can be utilized as a guide for formulating appropriate soil conservation and land management plans. The Revised Universal Soil Loss Equation (RUSLE) is frequently used to estimate the magnitude of soil erosion loss from watershed areas, the spatial distribution of soil erosion severity, and delimiting sites vulnerable to soil erosion for both agricultural and forested watersheds [30,[42][43][44][45][46][47][48]. Finally, the RUSLE model has several advantages: 1) it is easy to implement and understand from a functional perspective [32], 2) is compatible with the Geographic Information System (GIS), and 3) the data requirements to implement the model are not too complex or unattainable especially in a developing country [49].…”

Integrated Use of GCM, RS, and GIS for the Assessment of Hillslope and Gully Erosion in the Mushi River Sub-Catchment, Northeast China

“…Spatial and quantitative information on soil erosion on a watershed scale contributes significantly to soil conservation management, erosion control, and catchment environment management [21]. Previous studies showed that assessment of soil erosion methods mainly included empirical models [22,23], physically based models [15,[24][25][26], nuclear tracing [27], and spatially distributed multivariate models [28,29].…”

Risk assessment of soil erosion by application of remote sensing and GIS in Yanshan Reservoir catchment, China

“…This approach has proved to be a successful and inexpensive method for erosion risk assessment (Tian et al 2009;Zhang et al 2010). The quantitative methods are mainly model building (e.g., USLE, RUSLE, CORINE, PESERA and WEPP) (Panagos et al 2014;Prasannakumar et al 2011;Silva et al 2012;Tsara et al 2006). The Universal Soil Loss Equation (USLE) is a commonly used and simple soil erosion model, which estimates long-term average annual soil loss per unit area based on erosion factors (Laflen and Moldenhauer 2003), and the Revised Universal Soil Loss Equation (RUSLE) is a revised method of USLE methodology incorporating additional data (Renard et al 1991).…”