Integrating RUSLE model with remote sensing and GIS for evaluation soil erosion in Telkwonz Watershed, Northwestern Ethiopia

2021 ◽  
pp. 100623
Author(s):  
Solomon Eniyew ◽  
Menberu Teshome ◽  
Endalkachew Sisay ◽  
Tamiru Bezabih
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Remote Sensing And Gis ◽  
Rusle Model

scholarly journals Assessment of Soil Erosion by RUSLE Model using Remote Sensing and GIS - A case study of Ziz Upper Basin Southeast Morocco

2020 ◽  
Vol XIX (2) ◽  
pp. 131-142
Author(s):  
Imad Fenjiro ◽  
Anis Zouagui ◽  
Mohamed Manaouch
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Remote Sensing And Gis ◽  
Rusle Model

Risk assessment of soil erosion in semi-arid mountainous watershed in Saudi Arabia by RUSLE model coupled with remote sensing and GIS

2014 ◽  
Vol 29 (8) ◽  
pp. 915-940 ◽  
Author(s):  
Javed Mallick ◽  
Yasser Alashker ◽  
Shams Al-Deen Mohammad ◽  
Mohd Ahmed ◽  
Mohd Abul Hasan
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Saudi Arabia ◽  
Soil Erosion ◽  
Remote Sensing And Gis ◽  
Rusle Model ◽  
Mountainous Watershed ◽  
Semi Arid

scholarly journals Impact of Land Cover Change on Soil Erosion Hazard in Northern Jordan Using Remote Sensing and GIS

2013 ◽  
Vol 19 ◽  
pp. 912-921 ◽  
Author(s):  
M.Minwer Alkharabsheh ◽  
T.K. Alexandridis ◽  
G. Bilas ◽  
N. Misopolinos ◽  
N. Silleos
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Land Cover ◽  
Land Cover Change ◽  
Remote Sensing And Gis ◽  
Erosion Hazard ◽  
Northern Jordan

Quantitative Determination of Soil Erosion and Prioritization of Micro-Watersheds Using Remote Sensing and GIS

2011 ◽  
Vol 39 (2) ◽  
pp. 181-192 ◽  
Author(s):  
Vipul Shinde ◽  
Arabinda Sharma ◽  
Kamlesh N. Tiwari ◽  
Manjushree Singh
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Quantitative Determination ◽  
Remote Sensing And Gis

Integration of Remote Sensing and GIS for Evaluating Soil Erosion Risk in Northwestern Zhejiang, China

2012 ◽  
Vol 78 (9) ◽  
pp. 935-946 ◽  
Author(s):  
Jianqin Huang ◽  
Dengsheng Lu ◽  
Jin Li ◽  
Jiasen Wu ◽  
Shiquan Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Remote Sensing And Gis ◽  
Erosion Risk ◽  
Soil Erosion Risk

Prioritization of catchments based on soil erosion using remote sensing and GIS

2015 ◽  
Vol 187 (6) ◽  
Author(s):  
Gajanan K. Khadse ◽  
Ritesh Vijay ◽  
Pawan K. Labhasetwar
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Remote Sensing And Gis

scholarly journals MODELLING AND MAPPING OF SOIL EROSION ON THE OUED EL MALLEH CATCHMENT USING REMOTE SENSING AND GIS

2013 ◽  
pp. 302-307
Author(s):  
Omar El Aroussi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Land Degradation ◽  
Soil Loss ◽  
Land Use Changes ◽  
Field Studies ◽  
Water Volume ◽  
Water Storage Capacity ◽  
Rusle Model

In Morocco, the spectacular expansion of erosive processes shows increasingly alarming aspects. Due to the considerable costs of detailed ground surveys for studying this phenomenon, remote sensing is an appropriate alternative for analyzing and evaluating the risks of the expansion of soil degradation. According to an FAO study (2001), Erosion threatens 13 million ha of cropland and rangeland in northern Morocco and induces an estimated average water storage capacity loss of 50 million m3 each year through dam silting. The lost water volume could potentially be used to irrigate 5000 to 6000 ha / year. This study analyses soil erosion on the Oued El Malleh catchment, a 34 km2 catchment located in the north of Fez (Morocco). This contribution aims at mapping the spatio-temporal evolution of land use and modelling the erosion and sedimentation processes using the well known RUSLE model. Land use changes were assessed using Landsat-5 TM and Landsat-7 ETM+ images, from the 1987-2011 periods which were validated by field studies. The images were first georeferenced and projected into the Moroccan coordinate system (Merchich North) then processed to evaluate soil loss through a GIS package (Idrisi Andes Software). These static assessments of soil loss were then used in a deposition/sedimentation algorithm to model soil loss propagation to the downstream. The soil loss averages determined by the model vary between 1.09 t/ha/yr as a minimum value for the reforested lands and 169.4 t/ha/yr as a maximum value for the uncultivated lands (badlands). The latter generally correspond to Regosols or low protected soils located on steep slopes. In comparison with RUSLE, the sedimentation model yields lower values of soil losses; only 97.3 t/h/year for the uncultivated lands, and -0.34 t/ha/year in the reforested land, indicating an on-going sedimentation process. By taking into account the temporal variability of erosion and deposition jointly lower values of soil erosion are calculated by the RUSLE model. However, despite this decline, land degradation problems are still important due to the combination of land use and local lithology. The results of this study were used to indentify areas where interventions are needed to limit land degradation processes.

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