Temporal Land-Use/Land-Cover Change Analysis in Kotla Sub-Watershed of Rupnagar District (Punjab) Using Remote Sensing and GIS

Land-use and land-cover (LU/LC) change is becoming a crucial component for managing the natural resources. Identification, delineation and mapping of LU/LC are very important for activities such as planning, study of earth’s features and natural resource management. Remote sensing technology has proven to be an effective tool to analyze LU/LC changes at watershed level. In this study, an attempt was made to detect LU/LC changes from the year 2009 to 2018 in Kotla sub-watershed which is situated in Anandpur Sahib block, Rupnagar district of Punjab. High-resolution satellite data of IRS-P6 LISS-IV for the years 2009 and 2018 were analyzed for LU/LC mapping using visual image interpretation technique. There were total ten LU/LC classes: agriculture, built-up, canal, degraded forest, dense forest, drainage, moderate dense forest, transport, wasteland and waterbody demarcated in the study area. The overlay analysis of 2009 over 2018 was done to analyze changes in the study area over the period of 10 years. Results of study show that area under agriculture, moderate dense forest and wasteland decreased by 2.80%, 60.06% and 4.25%, respectively, and area under other LU/LC classes, i.e., built-up, degraded forest, dense forest, drainage and waterbody, was increased by 115.47%, 93.39%, 3.62%, 15.81% and 3.70%, respectively, while areas of canal and transport remain unchanged from the year 2009 to 2018. The overall accuracy and kappa statistics for classified images of 2009 and 2018 were 70.27% and 89.19% with kappa coefficient values 0.66 and 0.87, respectively. Change analysis at village level and sub-watershed level was also carried out to identify the most affected regions of the study area. Total 13 sub-watersheds are categorized as sub-watershed number 1–13. The most affected sub-watershed number is 11 which consists of villages: Kotla, Balauli, Raipur sani, Ganj pur, Mahndali khurd and Pahar pur as per LU/LC change analysis, and management practices need to be look for these villages. In this study, we also use land surface temperature algorithms and Normalized Difference Vegetation Index (NDVI) values to estimate changes in LU/LC from 2009 to 2018, where decreasing trend in the values of NDVI is estimated which is the evident of overall decrease in vegetation in the region.