Analyzing the spatial relationship between land surface temperature and normalized difference vegetation index using remote sensing and GIS

Global warming has emerged as one of the most pressing environmental challenges with Land Surface Temperature (LST) serving as a critical indicator of climate change, particularly in urban areas. The rapid exploitation and consumption of natural resources exacerbate the environmental degradation, significantly impacting the land surface dynamics. Understanding the relationship between LST and Normalized Difference Vegetation Index (NDVI) is crucial using the remote sensing technology to assess and mitigate climate change effects. This study evaluates the LST-NDVI relationship in Mohali district officially known as Sahibzada Ajit Singh (SAS) Nagar district, Punjab India, utilizing Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data from 2013 and 2022. The LST has been calculated from Landsat 8 data using the mono-window technique. The findings revealed that there has been a significant increase in LST which is well evident from a shift of 28.1 °C–31.9 °C in 2013 to 30.7 °C–34.5 °C in 2022 in thermal scale, highlighting the intensification of the Urban Heat Island (UHI) effect. Statistical regression analysis demonstrated inverse corelation between the NDVI and LST coefficient value of − 0.481 for the year 2013 and − 0.426 for the year 2022 indicating a weakening inverse correlation over time. The results underscore the urgent need for sustainable urban planning measures, including increasing urban green cover, implementation of heat-resistant infrastructure, and integrating climate-sensitive policies. Incorporating these insights into future urban development strategies can help to mitigate rising temperatures and enhance climate resilience at the local and regional levels.