Analyzing the Spatio-temporal Changes in the Hasdeo Subbasin in Chhattisgarh Using Sentinel-2 Imagery

Abstract

Understanding the dynamics of the world's landscapes is made possible by the multi-temporal change detection analysis made possible by Remote Sensing (RS) and GIS technologies. The current study provides an illustration of the spatiotemporal changes in land use land cover (LULC) of the Hasdeo subwatershed, Chhattisgarh, India, between 2016 and 2019 using Sentinel-2 imagery. To quantify the changes in the LULC area from 2016 to 2019, Sentinel-2 images from 2016 and 2019 were obtained. With the ERDAS Imagine software, the supervised classification method has been used. Eight different LULC classes, including forest, cultivated land, fallow land, water body, barren land, built-up land, mining and sand, were created using the image data of the research area. The outcome shows that between 2016 and 2019, there was an increase in built-up area and water body, while there was a decline in cultivated land, forest, and barren land. Kappa Coefficient accuracy is 88.00% and overall accuracy is 92.1% according to the LULC accuracy assessment for the data analysis. The paper's data base emphasises the significance of change detection approaches for spotting significant changes in land cover and for tracking the effects of human-induced change on the hydrological environment, behaviour, and setting of the Hasdeo sub watershed.