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Estimation of Soil Loss Using Remote Sensing and Geographic Information System Techniques (case Study of Kaliaghai River Basin, Purba & Paschim Medinipur District, West Bengal, India)
Remote Sensing (RS) and Geographic Information Systems (GIS) are useful tools in hydrological analysis and natural resource management. The application of RS and GIS techniques lends to estimate soil loss based on different parameters. RUSLE (Revised Universal Soil Loss Equation) model is used for soil loss estimation. Different parameters, namely the rainfall and runoff factor (R), soil erodibility factor (K), slope length and steepness factor (LS), crop management factor (C) and conservation practice factor (P), that are the mandatory inputs to RUSLE, have been either derived from remote sensing data or through conventional data collection systems. These parameters are obtained from monthly and annual rainfall data, soil map of the region, Digital Elevation Model (DEM), RS techniques (with use of Normalized Difference Vegetation Index) and land use/land cover map, respectively. This experiential study is carried out on the Kaliaghai river basin under Purbo and Paschim Medinipur district of West Bengal. Soil loss is very high in the river basin area, calculated as 1927779 tons/year using RUSLE model. Thus, the RUSLE model integrated with RS and GIS technologies has great potential for producing accurate and inexpensive erosion and sediment yield assessment map in the Kaliaghai river basin.
Keywords
Remote Sensing, Geographical Information System, Soil Loss and RUSLE Model
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- Chaplot V, Darboux F, Bourennane H, Leguedois S, Silvera N and Phachomphon K (2006) Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorph. 77, 126-141.
- Coltelli M, Fornaro G, Franceschetti G, Lanari R, Migiaccio M, Moreira JR, Papathanassaou KP, Puglisi G, Riccio D and Schwabisch M (1996) SIR-C/X-SAR multifrequency multipass interferometry: A new tool for geological interpretation. J. Geophys. Res. 101, 127- 148.
- Dowding S, Kuuskivi T and LI X (2004) Void fill of SRTM elevation data – principles, processes and performance, In: images to decisions: remote sensing foundations for GIS applications, ASPRS, Fall Conf. Sep. 12-16, Kansas City, MO, USA.
- Fisher PF and Tate NJ (2006) Causes and consequences of error in digital elevation models. Prog. Phys. Geog. 30, 467-489.
- Gamache M (2004a) Free and low cost datasets for international mountain cartography. Documentation for the Alpine Mapping Guild, 42.
- Kandrika S and Venkataratnam L (2005) A Spatially distributed event based model to predict sediment yield. J. Spatial Hydrol. Spring. 5 (1), 1-19.
- Renard KG, Forster G, Weesies G, McCool D and Yoder D (1997) Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Agri. Handbook, 703, Washington DC.
- Robert PS (2000) Engineer, soil management/OMAFRA; Don Hilborn- Engineer, Byproduct Management /OMAFRA.
- UNEP (1997) World atlas of desertification. 2nd edition, Arnold London.
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