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Title Temporal Land Use Change Analysis of Saleran Dam Catchment in Shivalik Foot-hills of Punjab using Geospatial Techniques
Author Name Vishnu Prasad
Abstract

Mapping and monitoring of land use change in Shivalik foot-hills of north-west India is important for development, planning, and management of various catchments situated in this region. The present study was thus undertaken to monitor changes in land use of Saleran dam catchment located in Shivalik foot-hills of Punjab for the periods of 1999- 2009, 2009-2019, and 1999-2019 using remote sensing and GIS. The satellite imageries from Landsat-5 and Landsat-8 were used for change analysis. The supervised classification technique with maximum likelihood algorithm was applied to prepare land use maps of the catchment. Four major land use classes viz. mixed forest, degraded land, streams, and water bodies were identified. The accuracy of classified maps was assessed using high-resolution Google earth images, and ground realities were verified and determined through field observations. The results obtained from the accuracy assessment of classified images indicated the overall accuracy of 93.65%, 92.06%, and 92.86% with corresponding kappa values of 0.85, 0.89, and 0.84 for the classified image of the years 1999, 2009 and 2019, respectively. The results demonstrated significant changes in the catchment land use during the period of past 20 years (1999-2019), indicating significant decrease in mixed forests from 552.07 ha (74.87%) to 483.17 ha (65.53%), whereas an increase in degraded land from 142.39 ha (19.31%) to 221.1 ha (29.99%) was observed. The area under water bodies and mixed forest decreased by 36.49% and 12.48%, respectively, while the area under streams increased by 14.01 per cent. The study highlights the important policy implications for the sustainable land use management in the Saleran dam catchment of Shivalik foot-hills. land use and climate change in relation to soil

Keyword Shivalik foot-hills , catchment , land use change , remote sensing and GIS , supervised classification ,
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