Integrating terrain and vegetation indices for identifying potential soil erosion risk area

Sharma, Ankita

doi:10.1007/s11806-010-0342-6

Cited by 70 publications

(43 citation statements)

References 7 publications

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“…Suitable cropping pattern and crop rotation practice may be commenced and community based soil erosion management program should be introduced to ease the soil loss in the study site. Conservation tillage, in conservation planning, contour bandings or contour hedgerows should be given to agronomic measures of soil conservation (Sharma 2010). On the other hand, dense forests play an important role in soil conservation.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, dense forests play an important role in soil conservation. Therefore, the afforestation program in this region is essential and should be implemented strictly (Sharma 2010;Bhunia and Shit 2013;Jiang et al 2015). The calculated R factor for the study area is shown in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…In the humid tropics of Asia, farmers grow subsistence crops in sloping land through highly erosive practices. An average rate of soil loss for Asia is 138 t ha -1 year -1 (Chen et al 2010;Sharma 2010). An estimated 175 Mha of land in India, constituting about 53 % suffers from deleterious effect of soil erosion and other forms of land degradation.…”

Section: Introductionmentioning

confidence: 99%

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Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Shit¹,

Nandi

Bhunia³

2015

Model. Earth Syst. Environ.

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An appraisal of spatial and quantitative information on soil erosion is a major challenge for human sustainability. The present study demonstrates the prognostic modeling capabilities of geo-spatial technology based on soil erosion potential model to assess the effects of implementing land use changes within sub-tropical region in India. The Revised Universal Soil Loss Equation (RUSLE) integrated with geo-spatial technology was used to produce predictive soil erosion map. Rainfall pattern, soil type, topography, crop system and management practices and geo-spatial data were used to predict the soil erosion model. Results shows 74.77 % of the study area is marked as low potential zone for soil erosion, whereas 5.58 % ([10 t h -1 year -1 ) is very high erosion risk zone. The present information may help recognizing areas that are vulnerable to soil loss and the proposed method will be used for generalized planning and assessment purposes for supervision and preserve the soil erosion.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Shit¹,

Nandi

Bhunia³

2015

Model. Earth Syst. Environ.

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“…The use of remote sensing and GIS techniques is very useful in estimation of total soil loss (Prasannakumar et al 2011;Koloa and Samanta 2013) and soil erosion rate spatially and temporally with minimum costs and better accuracy in larger areas (Rahman et al 2009). Revised universal soil loss equation (RUSLE) is one of the most familiar soil loss estimation process based on ground based observation and remote sensing and GIS technology (Pandey et al 2007;Sharma 2010;Pal and Samanta 2011). Other empirical model is named as E 30 to calculate the rate of soil erosion in any region (Honda et al 1996;Hazarika and Honda 2001;Bagyaraj et al 2014) where hydrological, meteorological, geographical and river morphological data are limited (Udayakumara et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Estimation of potential soil erosion rate using RUSLE and E30 model

Samanta

Koloa

Pal

et al. 2016

Model. Earth Syst. Environ.

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This research established an empirical methodology to estimate potential soil erosion rate based on revised universal soil loss equation (RUSLE) and E 30 model. The study was conducted on a highly precipitated, rugged, tropical forested with steep slope watershed during 1992 to 2009. The fourth (4th) largest river of Papua New Guinea, and its catchment area was considered for this research. Lots of commercial mining and logging activities are the ongoing processes in the upper catchment area without proper conservation measures. Digital elevation model (DEM), landsat satellite images, average annual rainfall, soil texture data base were used to derived mandatory input factors into the RUSLE and E 30 model. Raster calculator of ArcGIS spatial analyst was used to generate all input factors and final pixel-by-pixel based computation of soil loss pattern. The average potential soil erosion rate were calculated in the range of 20.34 mm/year to 23.70 mm/year through RSULE model and in the other hand the rate varies from 21.07 mm/year to 26.78 mm/year through E 30 model during 1992 to 2009 respectively. The erosion rate through both model indicates extremely severe rate of erosion in the upper catchment area are required immediate attention of soil conservation practices.

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“…Among this 93.68M ha of land is influenced by hydrologically controlled soil erosion (Narayan and Babu 1983;Anon 2008Anon , 2009Singh et al 1992;Pandey et al2007). In recent years, as part of environment and land degradation assessment policy for sustainable agriculture and development, soil erosion is increasingly being recognized as a hazard which is more serious in mountain areas (Millward and Mersey, 1999;Angima et al, 2003;Jasrotia and Singh, 2006;Dabral et al, 2008;Sharma, 2010). Soil erosion causes siltation of reservoirs, which ultimately reduces the life of the project and affects generation of hydroelectric power (Jasmin and Ravichandran, 2006).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Annual Average Soil Loss, Based on Rusle Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India

Rahaman

Aruchamy

Jegankumar

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation-RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h -1 / y -1 . Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.

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Integrating terrain and vegetation indices for identifying potential soil erosion risk area

Cited by 70 publications

References 7 publications

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Estimation of potential soil erosion rate using RUSLE and E30 model

Estimation of Annual Average Soil Loss, Based on Rusle Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India

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