Abstract
Accurate estimation of the microenvironment and radiative susceptibility, as well as forecasting of climate and weather scenarios in urban contexts, are critical for achieving sustainable development goals (SDGs). Rapid urbanization has a substantial impact on both the local and global atmosphere, resulting in Urban Heat Islands (UHIs). This study is the first to attempt to analyze spatiotemporal space-borne sensor datasets in the Indian Himalayan foothills using multifractal detrended fluctuation analysis (MDFA). The coefficients derived through MDFA such as holder exponent (h), spectral width (α), irregularity, truncation spectra, and spatial distribution are analyzed for various land features to illustrate the dynamic patterns and corroborate the multifractalitybehavior of the land surface temperature (LST). The negative correlation between LST and normalized difference vegetation index (NDVI) suggests that vegetated land can assist to mitigate the effects of UHIs, whereas the positive correlation between LST and normalized difference built-up index(NDBI) suggests that urbanization can aggravate the effects of UHIs. The obtained result encourages to implementation of the suggested framework to address anthropogenic heat and the transportation of surface heat fluxes, as well as empowers to work on non-linear dynamic climate models, which are critical for building real-time resilient infrastructure to meet environmental sustainability.