Assessing Landscape Dynamics and Variations in Land Surface Temperature in the Parbati Watershed of the Himalayas

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Abstract
Land-Use/Land-Cover (LULC) plays a crucial role in environmental management, yet its dynamic changes, especially in mountainous regions, remain inadequately studied. Sustainable land management requires comprehensive policies integrating environmental sustainability, climate resilience, and socioeconomic factors. However, the lack of Strategic Environmental Assessment (SEA) often leads to fragmented policies. This study examines LULC changes and their impact on Land Surface Temperature (LST) in the Parbati Watershed (PW), Northwestern Himalayas, focusing on human-induced vegetation changes and policy effectiveness. LULC changes from 2001 to 2021 were analyzed using Landsat satellite imagery and spectral indices such as Normalized Difference Vegetation Index (NDVI) and LST. LULC maps were generated using Support Vector Machine (SVM) classification algorithm to ensure high accuracy and robustness. LST was extracted from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data using the split-window algorithm (SW), which accounts for surface emissivity and reduces atmospheric interference. Linear regression models assessed the relationship between LST and NDVI in relation to LULC classes. Results indicate a decline in glaciers/snow (⁓-6.37%), Himalayan moist temperate forests (HMTF), (⁓-0.21%), Himalayan dry temperate forests (HDTF), (⁓-0.07%), vegetation/grass/grazing areas (⁓-0.86%), and water bodies (⁓-0.18%). Conversely, built-up/settlements areas, agricultural/plantations land, barren/sandy lands and rocky/open/debris land increased by ⁓0.15%, ⁓0.28%, ⁓0.24% and ⁓6.47%, respectively. Strong correlations were observed between LST and NDVI in built-up/settlement class (R²=0.63) and barren/sandy areas (R²=0.87) in 2021. Accurate retrieval of LST is essential for monitoring urban heat islands (UHI), deforestation, and agricultural expansion, particularly in the context of rapid climate change and intensified anthropogenic activities impacting various LULC classes. This study advances climate adaptation and sustainable land management by integrating LST analysis with landscape dynamics. The findings offer valuable insights for policymakers, supporting the development of effective climate resilience strategies and informed infrastructure planning. Graphical abstract
Funding Information
  • University of Technology Sydney

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