Assessment of Soil Loss of the Dhalai River Basin, Tripura, India Using USLE

Ghosh, Kapil; De, Sunil Kumar; Bandyopadhyay, Sunando; Saha, Sushmita

doi:10.4236/ijg.2013.41002

Cited by 38 publications

(15 citation statements)

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“…Soil erosion estimation and its spatial distribution can be calculated using combination of many techniques such as remote sensing, Geographic Information System (GIS) (Ghosh et al 2013;Haile & Fetene 2012;Jiang et al 2012;Khanal et al 2013;Kim et al 2012;Mhangara et al 2012;Ozsoy et al 2012;Patel & Kathwas 2011). In the literature, it is possible to see a wide application of universal soil loss equation (USLE) in soil erosion modelling in larger areas with acceptable accuracy (Millward & Mersey 1999;Wang et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Khosrokhani

Pradhan

2013

Geomatics, Natural Hazards and Risk

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The main purpose of this paper is to assess and predict soil erosion and its dynamism observation at Kuala Lumpur metropolitan city using universal soil loss equation (USLE). To acquire USLE factors, multi-source datasets such as rainfall data of the past 20 years, DEM map, SPOT5 image and geological map of the study area were used to calculate spatial soil erosion of Kuala Lumpur metropolitan city. Results indicated that the highest spatial coverage (61.9%) occurred in very low and the lowest spatial coverage (3.8%) in severe soil loss class. Furthermore, a landslide inventory map of the same area was overlaid on the resultant soil erosion layer to demonstrate their correlation. A frequency ratio (FR) method was applied to determine the relationship between occurred landslides and each soil erosion class. The results indicated that the highest and the lowest FR equal to 0.8 and 2.1 and observed in very low and severe soil loss classes, respectively. Temporal assessment of soil erosion indicated that there is a decrement of soil loss content in severe class from 2000 to 2010. The result of the prognosis analysis showed that the spatial coverage of the study area in severe soil loss class equals to 48%. This result proved the significance of land use/land cover to curb soil erosion. The resultant maps may help the planners and managers to take proper decision for the conservation of soil as a noteworthy natural resource.

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Section: Introductionmentioning

confidence: 99%

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Khosrokhani

Pradhan

2013

Geomatics, Natural Hazards and Risk

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“…Based on salient characteristics of different soil types a detailed table has been prepared and calculated K values of surface soil was also computed. (Ghosh et al 2013) and NBSS and LUP, Bangalore…”

Section: Soil Erodibility Factor (K)mentioning

confidence: 99%

Estimation of Soil loss by USLE Model using GIS and Remote Sensing techniques: A case study of Muhuri River Basin, Tripura, India

Bera¹

2017

Eurasian Journal of Soil Science (Ejss)

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“…Numerous streams and rivulets that originate from the neighbouring hills debouch into the Dhalai river (figure 2). The terrain is dissected by a hilly topography with patches of the plain region formed by deposition of sediment, and exhibit a wide array of sedimentary rocks characteristics of marine-mixed-fluvial type origin (Ghosh et al 2013). These sediments were deposited in the Surma basin during the late Cenozoic time in a wide range of environmental conditions governed by the local tectonic movement (SER 2002).…”

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confidence: 99%

Identification of drought in Dhalai river watershed using MCDM and ANN models

et al. 2017

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An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

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Assessment of Soil Loss of the Dhalai River Basin, Tripura, India Using USLE

Abstract:
Soil erosion is one of the most important environmental problems, and it remains as a major threat to the land use of hilly regions of Tripura. The present study aims at estimating potential and actual soil loss (t·h^-^{Show more}

Cited by 38 publications

References 37 publications

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Estimation of Soil loss by USLE Model using GIS and Remote Sensing techniques: A case study of Muhuri River Basin, Tripura, India

Identification of drought in Dhalai river watershed using MCDM and ANN models

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Assessment of Soil Loss of the Dhalai River Basin, Tripura, India Using USLE

Abstract: Soil erosion is one of the most important environmental problems, and it remains as a major threat to the land use of hilly regions of Tripura. The present study aims at estimating potential and actual soil loss (t·h- Show more

Cited by 38 publications

References 37 publications

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Estimation of Soil loss by USLE Model using GIS and Remote Sensing techniques: A case study of Muhuri River Basin, Tripura, India

Identification of drought in Dhalai river watershed using MCDM and ANN models

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Abstract:
Soil erosion is one of the most important environmental problems, and it remains as a major threat to the land use of hilly regions of Tripura. The present study aims at estimating potential and actual soil loss (t·h^-^{Show more}