摘要：The land use/land cover pattern of the region is an outcome of natural and socio-economic factors and their utilization by man in space and time; land becoming a scare resource due to immense pressure on agriculture and demographic. Hence information on land use/land cover and possibilities for their optimal use is essential for the selection, planning and implementation of land use schema to meet the increasing demands for basic human needs and welfare. Remote sensing techniques provide valuable and up to date information on natural resources. GIS with the capability of integration multi-layer information obtained from both Satellite remote sensing and other conventional sources has proved to be effective tools in planning and detecting land use/land cover changes for Land Use development. The High Resolution Satellite Data is IRS-P6 LISS-IV is used for getting quick and useful base line information on the parameters like land use/land cover, Drainage, Roads etc. The study area is taken for 10 km buffer from Centre of Suryapet town, Nalgonda District. The main objective of the study is to produce the Land Use map, detecting various units and make it use for future planning. About 78.63% of the total area is covered by agriculture; 6.64% under waste land; 7.74% is covered under water bodies; Mining and Industries by 1.42% and Suryapet town urban agglomeration is covered by 2.22%. The city will become a trade Centre in future as it is connected well by road network. NH-65 is passing through the town. The catchment of Musi reservoir’s left canal is covering on the northwestern side of the town. In future the industrial activity may be proposed in waste lands on the north side of the town as the Musi reservoir source water exists nearby. The sites for Waste dumping may be selected in future by using other ancillary data like wind direction etc. Further Infrastructure development may be proposed towards eastern side of the town. As it is proposed to be the district headquarters in near future, hence a detailed planning will be possible in projecting future land use/land cover scenarios using appropriate models.
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