Geospatial Analysis of Urban Warming: A Remote Sensing and GIS-Based Investigation of Winter Land Surface Temperature and Biophysical Composition in Rajshahi City, Bangladesh

This study investigates urban warming in Rajshahi City, Bangladesh, by examining changes in land surface temperature (LST) from 1990 to 2023 and exploring its relationship with key biophysical factors. LST was derived from Landsat thermal imagery, and both spatial and temporal variations were analyzed using Geographic Information Systems (GIS). Key biophysical indices, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Bareness Soil Index (NDBSI), were calculated using corresponding Landsat satellite sensors, and they evaluated the impact of LULC types (vegetation, water, soil, and built-up areas) on thermal variations. LULC was derived following the Support Vector Machine classification technique. The Urban Thermal Field Variance Index (UTFVI) was employed to assess surface urban heat island (SUHI) effects, warming conditions, ecological stress, and thermal comfort zones. Spatial trend and hotspot analyses of LST change were performed using spatial trend analysis and the Getis-Ord Gi* statistic, respectively. Linear regression analysis examined the relationship between LST and biophysical indices. Results show that winter mean LST increased by 2.66 °C during the 33-year period, with maximum LST rising by 4.29 °C. The most significant warming occurred in central-northern, central-western, and south-eastern zones. The rise in LST and the growing intensity of SUHI effects are largely due to urban growth, especially where green spaces and water bodies have been replaced by impervious surfaces. Hotspot analysis identified clusters of high-temperature zones, while UTFVI analysis confirmed a marked expansion of strong heat island conditions, especially in central urban areas. Linear regression results showed notable links between LST and key biophysical variables, where higher LST values were commonly linked to greater built-up density and declines in vegetation cover and surface water. Overall, the results highlight the need for better urban planning approaches such as increasing green cover, using permeable materials, and adopting strategies that can adapt to climate impacts. This study presents a framework for analyzing urban climate dynamics that can be adapted to other rapidly growing cities, aiding efforts to promote sustainable development and build urban resilience.