Watershed prioritization using remote sensing and geographical information system: a case study from Guhiya, India

Khan, Mohammad Asif; Gupta, Vaibhav; Moharana, P.C.

doi:10.1006/jare.2001.0797

Cited by 124 publications

(47 citation statements)

References 2 publications

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“…ERDAS spatial modeler was used to derive and apply this model. All reclassified themes were integrated in weighted overlay analysis using Equation (1).…”

Section: Integration Of Thematic Layers and Spatial Modelmentioning

confidence: 99%

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Prioritization of Watersheds across Mali Using Remote Sensing Data and GIS Techniques for Agricultural Development Planning

Gumma

Zemadim

Mohammed

et al. 2016

Water

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Implementing agricultural water management programs over appropriate spatial extents can have positive effects on water access and erosion management. Lack of access to water for domestic and agricultural uses represents a major constraint on agricultural productivity and perpetuates poverty and hunger in sub-Saharan Africa (SSA). This lack of access is the result of erratic precipitation, poor water management, limited knowledge of hydrological systems, and inadequate investment in water infrastructure. Water management programs should be made by multi-disciplinary teams that consider the interrelationship between hydraulic and anthropogenic factors. This paper proposes a method to prioritize watersheds for water management and agricultural development across Mali (Western Africa) using remote sensing data and GIS tools. The method involves deriving a set of relevant thematic layers from satellite imagery. Satellite images from Landsat ETM+ were used to generate thematic layers such as land use/land cover. Slope and drainage density maps were derived from Shuttle RADAR Topography Mission (SRTM) Digital Elevation Model (DEM) at 90 m spatial resolution. Population grids were available from the Global rural-urban mapping project (GRUMP) database for the year 2000 and mean rainfall maps were extracted from Tropical rainfall measuring mission (TRMM) grids for each year between 1988 and 2014. Each thematic layer was divided into classes that were assigned a rank for agriculture and livelihoods development provided by experts in the relevant field (e.g., Soil scientist ranking the soil classes) and published literature on those themes. Zones of priority were delineated based on the combination of high scoring ranks from each thematic layer. Five categories of priority zones ranging from "very high" to "very low" were determined based on total score percentages. Field verification was then undertaken in selected categories to check the priority assigned to each class using a random sampling method. Watershed boundaries were prepared at 1000 ha scale and overlaid on the priority map to identify watersheds that were in a very high priority zone. The importance and efficiency of using remote sensing to prioritize watershed interventions across countries is critical due to the limited technical and financial resources available in sub-Saharan Africa (SSA).

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“…ERDAS spatial modeler was used to derive and apply this model. All reclassified themes were integrated in weighted overlay analysis using Equation (1).…”

Section: Integration Of Thematic Layers and Spatial Modelmentioning

confidence: 99%

“…WSP P = Σ T GWˆFW (1) where WSP P is the Watershed priority score of pixel score in model output; T WS is the selected watershed priority theme; and F W is the Weightage factor of theme.…”

Section: Integration Of Thematic Layers and Spatial Modelmentioning

confidence: 99%

Prioritization of Watersheds across Mali Using Remote Sensing Data and GIS Techniques for Agricultural Development Planning

Gumma

Zemadim

Mohammed

et al. 2016

Water

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“…Morphometric analysis could be used for prioritization of sub-watersheds by computing linear and shape parameters (Biswas et al 1999). Several studies in the recent past have been done on prioritization of watersheds (Aher et al 2013;Allen et al 2001;Arun et al 2005;Bera and Bandyopadhyay 2013;Javed et al 2009;Katiyar et al 2006;Khan et al 2001;Martin and Saha 2007;NookaRatnam et al 2005;Rashid and Sahu 2014;Reddy et al 2004;Shrimali et al 2001;Singh et al 2014;Suresh et al 2004;Thakkar and Dhiman 2007;Tripathi et al 2013;Vittala et al 2008). For assessment of erosion, several empirical models based on the geomorphological parameter were developed in the past for quantifying the sediment yield (Chowdary et al 2013;Jose and Das 1982;Misra et al 1984).…”

Section: Introductionmentioning

confidence: 99%

“…Pandey et al (2007) divided the Karso watershed of Hazariabagh, Jharkhand State, India, into 200 9 200 m grid cells and average annual sediment yields were estimated for each cell of the watershed to identity the critically prone areas of watershed. Recent studies (Pandey et al 2007;Yoshino and Ishioka 2005;Sharma et al 2001;Khan et al 2001;Sidhu et al 1998) revealed that remote sensing (RS) and geographic information system (GIS) techniques are of great use in characterization and prioritization of watershed areas using land use/land cover methods (Kulkarni et al 2014;Waikar and Nilawar 2014;Warrier and Manjula 2014). Conventional method to drive morphological parameters is expensive, time consuming and more prone to error.…”

Section: Introductionmentioning

confidence: 99%

Geomorphological analysis and prioritization of sub-watersheds using Snyder’s synthetic unit hydrograph method

Singh

2014

Appl Water Sci

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The present study makes an attempt to prioritize sub-watersheds based on Snyder's synthetic unit hydrograph method. Snyder's method of synthetic unit hydrograph calculates peak discharge and lag time of the unit hydrograph for each sub-watersheds. Compound values of ranking are calculated from assigned rankings to parameters, viz. peak discharge and lag time. Depending on the range of the compound values sub-watersheds are classified as high, medium and low soil-erosive subwatersheds. The priorities obtained from Snyder's synthetic unit hydrograph method are compared with the methods of morphometric analysis and land use/land cover analysis. On comparison of priorities for Dangri River watershed, Panchkula District, Haryana (India), among the three methods it was found that the sub-watershed (SW1) has the same priority. Among all the three methods, Snyder's synthetic unit hydrograph is a better method, as it is easier to use and less data intensive.

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“…Khan et al (2001), for example, used spatial data on land-forms, land cover, and slope to estimate potential erosivity and sediment yield in India. In addition, Wu and colleagues (1997) used spatial data linked to a GIS coupled with Landsat TM land cover data to evaluate USDA Conservation Reserve Program (CRP) lands and related soil properties in the U.S. High Plains.…”

Section: Crop Water Managementmentioning

confidence: 99%

Remote Sensing of Cropland Agriculture

Nellis¹,

Price²,

Rundquist³

The SAGE Handbook of Remote Sensing

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Watershed prioritization using remote sensing and geographical information system: a case study from Guhiya, India

Cited by 124 publications

References 2 publications

Prioritization of Watersheds across Mali Using Remote Sensing Data and GIS Techniques for Agricultural Development Planning

Prioritization of Watersheds across Mali Using Remote Sensing Data and GIS Techniques for Agricultural Development Planning

Geomorphological analysis and prioritization of sub-watersheds using Snyder’s synthetic unit hydrograph method

Remote Sensing of Cropland Agriculture

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