Spatio-temporal study of urban dynamics with implications on land surface temperature of Gurugram City, India

Prompt urbanization is one of the irreversible anthropogenic activities that is a major challenge for ecosystems and climate change. This global challenge needs to be addressed as urbanization significantly affects land surface temperature (LST) and contributes to the urban heat island (UHI) effect. Urban development converts natural landscapes into impermeable surfaces, elevating LST, particularly in densely populated regions where heat-absorbent materials are abundant. To better understand these impacts and support evidence-based mitigation, this study analyses spatial patterns and transformations in land use and temperature. Accordingly, the study utilized remote sensing data, from Landsat 4-5 TM (2000) and 8-9 OLI/TIRS (2022), which were used to assess land use dynamics using spectral indices (normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNDWI)), and thermal band data was utilized to derive LST and examine their spatio-temporal dynamics and interrelationships with UHI and the consequent ecological (urban thermal field variance index (UTFVI)) implications, in Gurugram, India. The Landsat satellite images were processed in ArcGIS 10.8 and ERDAS IMAGINE 2015, and the data analysis of spectral indices was done through MATLAB and Excel. The land use land cover (LULC) map was classified with a supervised classification method based on the maximum likelihood classifier (MLC) method, with the kappa coefficients of 0.959 (2000) and 0.956 (2022), which reflected acceptable results for classifications and mapping of LULC. Compared with field-level surveys, the study achieved a classified accuracy of around 97.2% and 96.8%, respectively. The findings of the study reveal that over two decades, built-up areas increased by about 13%, agricultural fields decreased by 26%, and average LST rose by 2-3 °C, which also indicates different ecological status in various regions. These insights can be useful for urban planners, municipal authorities, and environmental protection organizations to create climate-resilient urban landscape ecoregions with specific targeting of vulnerable areas associated with urban hybrid heat islands and ecological degradation based on location. Statistical analysis of linear regression presented high NDBI values, which shows the strongest positive association with LST, and high NDVI and MNDWI values displayed a significant cooling effect, with the R-squared value 0.97 for the year 2000, and for 2022, it is 0.98. This study underscores the necessity for a comprehensive policy framework that incorporates geospatial data for urban planning to mitigate UHI effects via green infrastructure, afforestation, and water body restoration projects. Future studies should employ advanced technologies like LiDAR and UAVs for improved urban surveillance and investigate multi-hazard assessments that combine urban heat with flood and air quality concerns.