The impact of land use and land cover on land surface temperature in an Indian riverine town over a decade and how it varied post-lockdown

Abstract

Abstract In towns and cities in developing countries, negligence in consistently regulating the growth of urban sprawl is commonplace. The purpose of the study was to analyze spatiotemporal changes in land use land cover (LULC) and their impact on land surface temperature (LST) in Balurghat, Dakshin Dinajpur district, West Bengal, India. The results revealed a decrease in the vegetation cover (64–44%) and an increase in the built-up area (14–39%) from 2012 to 2022. Over the study period, built-up regions and bare land had the highest temperatures, ranging from 20.6°C to 24.96°C, and waterbodies had the lowest temperatures, ranging from 17.85°C to 20.47°C. From 2012 to 2017, LST exhibited an increasing trend. However, after the lockdown, LST declined slightly in 2022. The mean LST variations in the study area from 2012 to 2022, presenting a pre- and post-pandemic scenario, were also highlighted in this study. Furthermore, this study emphasized the correlation analysis between LST and four spectral indices, which are the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), the Soil Adjusted Vegetation Index (SAVI), and the Modified Normalized Difference Water Index (MNDWI). Multiple linear regression (MLR) containing NDVI and MNDWI with LST has been consistently the best-fit model for 2012, 2017 and 2022. These models have been established using various statistical tools, primarily the Akaike information criterion (AIC) model selection and the Inflation Factor (VIF). The results provide a framework for sustainable urban design and development, which can serve as a resource for policymakers and increase public understanding.