Runoff and soil loss estimation using hydrological models, remote sensing and GIS in Shivalik foothills: a review

2016 ◽  
Vol 15 (3) ◽  
pp. 205 ◽  
Author(s):  
Abrar Yousuf ◽  
M. J. Singh
Keyword(s):  
Remote Sensing ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Loss Estimation ◽  
Hydrological Models ◽  
Runoff And Soil Loss

scholarly journals Terrain Characterization and Soil Erosion Risk Assessment for Watershed Prioritization Using Remote Sensing and GIS (A case study of Nawagaon Maskara Rao Watershed, Saharanpur, India)

2009 ◽  
Vol 23 (1) ◽  
pp. 86
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Conservation ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Conservation Measures

Soil erosion is crucial problem in India where more than 70% of land in degraded. This study is to establish conservation priorities of the sub watersheds across the entire terrain, and suggest suitable conservation measures. Soil conservation practices are not only from erosion data both qualitative SES (Soil Erosion Status) model and quantitative MMF (Morgan, Morgan and Finney) model erosion, but we have to consider LCC (Land Capability Classification) and LULC (Land Use Land Cover). Study demonstrated the use of RS (Remote Sensing) and GIS (Geographic Information System) in soil erosion risk assessment by deriving soil and vegetation parameters in the erosion models. Sub-watersheds were prioritized based on average soil loss and the area falls under various erosion risk classes for conservation planning. The annual rate of soil loss based on MMF model was classified into five soil erosion risk classes for soil conservation measures. From 11 sub watersheds, for the first priority of the watershed is catchment with the small area and the steep slope. Recommendation for steep areas (classes VI, VII, and VIII) land use allocation should be made to maintain forest functions.

Assessment of Soil Loss using Revised Universal Soil Loss Equation (RUSLE): A Remote Sensing and GIS Approach

2018 ◽  
Vol 2 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ajaykumar Kadam ◽  
B. N. Umrikar ◽  
R. N. Sankhua
Keyword(s):  
Remote Sensing ◽  
Soil Loss ◽  
Remote Sensing Data ◽  
Tropical Rainfall Measuring Mission ◽  
Remote Sensing And Gis ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Gis Environment ◽  
Soil Loss Equation

A comprehensive methodology that combines Revised Universal Soil Loss Equation (RUSLE), Remote Sensing data and Geographic Information System (GIS) techniques was used to determine the soil loss vulnerability of an agriculture mountainous watershed in Maharashtra, India. The spatial variation in rate of annual soil loss was obtained by integrating raster derived parameter in GIS environment. The thematic layers such as TRMM [Tropical Rainfall Measuring Mission] derived rainfall erosivity (R), soil erodibility (K), GDEM based slope length and steepness (LS), land cover management (C) and factors of conservation practices (P) were calculated to identify their effects on average annual soil loss. The highest potential of estimated soil loss was 688.397 t/ha/yr. The mean annual soil loss is 1.26 t/ha/yr and highest soil loss occurs on the main watercourse, since high slope length and steepness. The spatial soil loss maps prepared with RUSLE method using remote sensing and GIS can be helpful as a lead idea in arising plans for land use development and administration in the ecologically sensitive hilly areas.

Soil erosion analysis by RUSLE and sediment yield models using remote sensing and GIS in Kelantan state, Peninsular Malaysia

Soil Research ◽  
2018 ◽  
Vol 56 (4) ◽  
pp. 356 ◽  
Author(s):  
M. T. Anees ◽  
K. Abdullah ◽  
M. N. M. Nawawi ◽  
N. A. N. Norulaini ◽  
M. I. Syakir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Sediment Yield ◽  
Soil Loss ◽  
High Probability ◽  
Peninsular Malaysia ◽  
Remote Sensing And Gis ◽  
Agricultural Activities ◽  
Steep Slopes ◽  
Very High

The present study used pixel-based soil erosion analysis through Revised Universal Soil Loss Equation (RUSLE) and a sediment yield model. The main motive of this study is to find soil erosion probability zones and accordingly prioritise watersheds using remote sensing and Geographic Information System (GIS) techniques in Kelantan state, Peninsular Malaysia. The catchment was divided into 82 watersheds and soil loss of the catchment was calculated. Soil loss and sediment yield were divided into five categories ranging from very low to very high. Maximum area of the very high soil-loss category was observed in uncultivated land and the maximum area of very low soil-loss category was in forest. Soil erosion probability zones were also divided into five categories in which 36.1% of the area experienced zero soil erosion and 20.1% and 17.8% represented very high and high probability zones respectively. The maximum very high and high probability zones were 61.6% and 28.5% of the watershed area respectively. Prioritisation was according to the area covered by very high and high soil erosion probability zones, which showed that out of 82 watersheds, two had the very high and high priority categories respectively. The overall results indicate that high rainfall and agricultural activities enhanced the soil erosion rate on steep slopes in the catchment. Pixel-based soil erosion analysis through remote sensing and GIS was a very effective tool in finding accurate causes of soil erosion. Furthermore, it was suggested that agricultural activities and deforestation should be stopped on steep slopes because of their contribution in increasing soil erosion.

scholarly journals Estimation of soil loss by USLE Model using Remote Sensing and GIS Techniques - A Case study of Coastal Odisha, India

2019 ◽  
Vol 8 (4) ◽  
pp. 321-328 ◽  
Author(s):  
Ramasamy Srinivasan ◽  
Surendra Kumar Singh ◽  
Dulal Chandra Nayak ◽  
Rajendra Hegde ◽  
Muniasami Ramesh
Keyword(s):  
Remote Sensing ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Gis Techniques

Soil loss estimation using RUSLE model, GIS and remote sensing techniques: A case study from the Dembecha Watershed, Northwestern Ethiopia

2017 ◽  
Vol 11 ◽  
pp. 28-36 ◽  
Author(s):  
Yaser Ostovari ◽  
Shoja Ghorbani-Dashtaki ◽  
Hossein-Ali Bahrami ◽  
Mehdi Naderi ◽  
Jose Alexandre Melo Dematte
Keyword(s):  
Remote Sensing ◽  
Soil Loss ◽  
Loss Estimation ◽  
Gis And Remote Sensing ◽  
Rusle Model ◽  
Remote Sensing Techniques

scholarly journals Assessment of Soil Erosion in the Watershed of Upper Lake, Bhopal using Remote Sensing and GIS

2019 ◽  
Vol 8 (6) ◽  
pp. 456-462 ◽  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Preventive Measures ◽  
Remote Sensing And Gis ◽  
Significant Advance ◽  
Study Region ◽  
Gis And Remote Sensing ◽  
P Factor ◽  
Factor Maps

Recognizable proof of soil erosion territories and to propose or apply preventive measures is significant advance in the management of watershed. For structuring a watershed and to conserve it appropriately assessment of soil erosion plays a significant role. With the headway of innovation and advancement of GIS and Remote Sensing researchers and scientists can assess soil erosion using various developed model. In this study, Universal Soil Loss Equation (USLE) has been utilized to gauge soil disintegration inside the Upper Lake Bhopal, India. Catchment territory of Upper Lake, Bhopal has been partitioned into 24 sub zones and every one of them were organized according to the erosion occurring. The normal yearly soil misfortune guide has been acquired by coordinating R, K, LS, C and P factor maps and it fluctuates from 0.00 to 2735.45 t/ha/yr over the watershed. All the 24 sub watershed have been named as Krishna's sub watershed (KW). The average soil loss from sub-watersheds have been figured and changes between 1.26 (KW-21) t/ha/yr to 99.04 (KW-3) t/ha/yr. The total soil loss in the watershed is determined as 19.6 t/ha/yr All sub-watersheds have been arranged into five classes specifically extremely high, high, moderate, low and low classifications based on final priority. Watersheds going under exceptionally high need covers 30.51% zone of study region, high need covers 22.31% zone, moderate need covers 25.46% zone of study territory, low need covers 14.45% region of study region, goes under extremely low need which spreads 7.26% zone of study region.

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