Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RS

Water reservoirs are facing universal sedimentation problems worldwide. Land covers, whether natural or manmade, eventually change, and the vegetation cover and rainfall have a great effect on the sediment load. Traditional techniques for analysing this problem are time-consuming and spatially limit...

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Main Authors: Mahboob Alam, Raja Rizwan Hussain, A.B.M. Saiful Islam
Format: Article
Language:English
Published: Taylor & Francis Group 2016-03-01
Series:Geomatics, Natural Hazards & Risk
Online Access:http://dx.doi.org/10.1080/19475705.2014.942387
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spelling doaj-3ec88e4593bd4e2a84dbac6833e14a562020-11-25T02:52:55ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132016-03-017266767910.1080/19475705.2014.942387942387Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RSMahboob Alam0Raja Rizwan Hussain1A.B.M. Saiful Islam2COMSATS Institute of Information TechnologyKing Saud UniversityUniversity of MalayaWater reservoirs are facing universal sedimentation problems worldwide. Land covers, whether natural or manmade, eventually change, and the vegetation cover and rainfall have a great effect on the sediment load. Traditional techniques for analysing this problem are time-consuming and spatially limited. Remote sensing (RS) provides a convenient way to observe land cover changes, and geographic information system (GIS) provides tools for geographic analysis. This study demonstrates a GIS-based methodology for calculating the impact of vegetation and rainfall on the sediment load using remotely sensed data. Moderate resolution imaging spectroradiometer data were used to observe temporal changes in the vegetation-cover area of the watershed surface. The total drainage area for the reservoir was calculated from shuttle radar topographic mission data. The annual rainfall amount was used to compute the annual available rainwater for the watershed, and the impact of the annual available rainwater on the vegetation-covered area was determined. In addition, areas that were adding sedimentation to the reservoir were identified. An inverse relationship between the rainfall and vegetation cover was observed, clearly showing the triggering of erosion.http://dx.doi.org/10.1080/19475705.2014.942387
collection DOAJ
language English
format Article
sources DOAJ
author Mahboob Alam
Raja Rizwan Hussain
A.B.M. Saiful Islam
spellingShingle Mahboob Alam
Raja Rizwan Hussain
A.B.M. Saiful Islam
Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RS
Geomatics, Natural Hazards & Risk
author_facet Mahboob Alam
Raja Rizwan Hussain
A.B.M. Saiful Islam
author_sort Mahboob Alam
title Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RS
title_short Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RS
title_full Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RS
title_fullStr Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RS
title_full_unstemmed Impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by GIS and RS
title_sort impact assessment of rainfall-vegetation on sedimentation and predicting erosion-prone region by gis and rs
publisher Taylor & Francis Group
series Geomatics, Natural Hazards & Risk
issn 1947-5705
1947-5713
publishDate 2016-03-01
description Water reservoirs are facing universal sedimentation problems worldwide. Land covers, whether natural or manmade, eventually change, and the vegetation cover and rainfall have a great effect on the sediment load. Traditional techniques for analysing this problem are time-consuming and spatially limited. Remote sensing (RS) provides a convenient way to observe land cover changes, and geographic information system (GIS) provides tools for geographic analysis. This study demonstrates a GIS-based methodology for calculating the impact of vegetation and rainfall on the sediment load using remotely sensed data. Moderate resolution imaging spectroradiometer data were used to observe temporal changes in the vegetation-cover area of the watershed surface. The total drainage area for the reservoir was calculated from shuttle radar topographic mission data. The annual rainfall amount was used to compute the annual available rainwater for the watershed, and the impact of the annual available rainwater on the vegetation-covered area was determined. In addition, areas that were adding sedimentation to the reservoir were identified. An inverse relationship between the rainfall and vegetation cover was observed, clearly showing the triggering of erosion.
url http://dx.doi.org/10.1080/19475705.2014.942387
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AT rajarizwanhussain impactassessmentofrainfallvegetationonsedimentationandpredictingerosionproneregionbygisandrs
AT abmsaifulislam impactassessmentofrainfallvegetationonsedimentationandpredictingerosionproneregionbygisandrs
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