Volume 5, Issue 4, August 2017, Page: 30-42
Mapping of Soil Erosion Risk in the Diarha Watershed Using Rusle, RS and GIS
Ibrahima Thiaw, Laboratory of Hydrology and Morphology, Cheikh Anta Diop University, Dakar, Senegal
Honoré Dacosta, Faculty of Arts and Social Sciences, Department of Geography, Cheikh Anta Diop University, Dakar, Senegal
Received: Dec. 19, 2017;       Accepted: Jan. 2, 2018;       Published: Jan. 20, 2018
DOI: 10.11648/j.ajrs.20170504.11      View  1364      Downloads  118
Mapping of erosive risks is a prerequisite in an erosion control approach. It makes it possible to locate the sectors most vulnerable to erosive processes. The establishment of the erosive risk map results from the spatialization of the Revised Universal Soil Loss Equation (Rusle). This equation is combined with Geographic Information Systems (GIS) and Remote Sensing (RS) techniques to estimate and map average rates of soil loss. If it is possible to significantly reduce soil water erosion through adapted farming techniques such as crop rotation, milling, banding and mulching, it is first necessary to target strong erosion requiring priority intervention. This study was conducted in the Diarha watershed and its sub-basins to assess potential soil losses and map the main factors involved in soil erosion processes. The results show that the erosive risks vary according to climatic and topographic gradients but also soil characteristics of the watershed. Potential soil losses vary between 0 and 1873 t/ha/year depending on the sector. The assessment yielded an average of 36.4t/ha/year and a standard deviation of 105.3t/ha/year. Annual soil losses in the entire Diarha catchment area are estimated at 31882t/year; with a specific degradation of 42t/km2/year. The results will be compared to those of the Gambia watershed in Kedougou station which is contiguous to it.
Diarha, GIS and RS, Rusle, Watershed
To cite this article
Ibrahima Thiaw, Honoré Dacosta, Mapping of Soil Erosion Risk in the Diarha Watershed Using Rusle, RS and GIS, American Journal of Remote Sensing. Vol. 5, No. 4, 2017, pp. 30-42. doi: 10.11648/j.ajrs.20170504.11
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