Land Surface Temperature and vegetation processes are progressively important parameters to comprehend the effects of climate change, particularly over ecologically vulnerable mountainous regions. This research explores the spatiotemporal dynamics of LST and NDVI during Mandi district, Himachal Pradesh, based on 1990, 2008, and 2022 multi-temporal Landsat data. LULC classification was done through supervised Maximum Likelihood Classification (MLC) and resulted in four major classes: forest, agriculture/grassland, barren land, and water bodies. The outcome indicates that the forest cover reduced by 11.8% between 1990 and 2022, while built-up/barren land rose by more than 9%. Similarly, mean LST in deforested regions increased by 4.1°C during the 32-year period, showing strong local warming. NDVI decreased from a mean of 0.47 in 1990 to 0.36 in 2022, showing vegetation stress and loss. There existed a significant inverse relationship (R² = 0.71) between LST and NDVI, especially in degrading and urbanizing areas. Integrating thermal data, spectral indices, and classified land cover maps using GIS tools, the current research delivers localized, high-resolution information regarding land climate relationships. This research completes an existing research gap at the regional level and provides new evidence in favor of climate-sensitive land-use planning for Himalayan hill districts undergoing swift land transformation.
Guleria et al. (Wed,) studied this question.