Abstract
The Remote Sensing and GIS tools have opened new paths in land and water resource studies, presently. Satellite images are increasingly used in ground water exploration because of their utility in identifying various geomorphic features. In the present study, all the prepared thematic data layers such as slope, relief, soil, geology, geomorphology, drainage, land use and ‘NDVI’ are integrated using the Spatial Analyst Tool in Arc GIS 9.2 implying weighted overlay methods to delineate the Ground Water Potential Zones in Kangshabati Irrigation Command Area (KICA). In weighted overlay analysis rank value assigned for each class of all thematic data layers according to their influence on ground water hydrology and factor weighted values are assigned according to analytical hierarchy process (AHP). Finally, an accuracy study is being performed in ERDAS Imagine Software by ground truth verification of 30 training sites with GPS readings for major land use/land cover information which states the overall classification accuracy of the present study is 86.66%.
Highlights
Ground water is a dynamic and replenishing natural resource, which forms the core of the ecological system
In the present study of preparing the ground water potential mapping of Kangshabati Irrigation Command Area (KICA), various thematic maps namely slope, relief, soil, drainage, geology, geomorphology and land use/land cover were reclassified on the basis of weightage assigned and brought into the ‘raster calculatior’ function of spatial analyst tool for integration
Large part of the study area suffers from severe drought condition and the ground water zonation map of the Command Area will contribute a lot of help and knowledge about the hydrology to the concerned authorities engaged in Land use planning
Summary
Ground water is a dynamic and replenishing natural resource, which forms the core of the ecological system. In the present study of preparing the ground water potential mapping of Kangshabati Irrigation Command Area (KICA), various thematic maps namely slope, relief, soil, drainage, geology, geomorphology and land use/land cover were reclassified on the basis of weightage assigned and brought into the ‘raster calculatior’ function of spatial analyst tool for integration. After the extraction of different blocks of the study area from the satellite image (FCC) with the help of AOI tools in the ERDAS IMAGINE software a supervised classification is carried out to obtain various land use/land cover classes i.e., cropland, wet land, barren land, dense forest, degraded forests, and sandbank etc These pre-field classifications are made to plan the field survey for land use/ land cover data collection. Factors 1) NDVI 2) Relief 3) Soil 4) Slope 5) Drainage 6) Geology 7) Geomorphology 8) Land use Consistency Ratio-0.037
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