Land Use and Land Cover Change Study Using RS-GIS and Its Impact on Biodiversity: Study from Arunachal Pradesh, India

This study aims to depict the intricate relationship between biophysical parameters, particularly land surface temperature (LST), and their impact on biodiversity at Itanagar Wildlife Sanctuary in Arunachal Pradesh. Focused on the consequences of anthropogenic activities, our research assesses changes in forest cover using Landsat images from 1990, 2003, and 2019. Utilizing satellite data, calculation of indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), and LST were performed. Land use land cover (LULC) was determined through supervised classification, achieving accuracies of 75%, 87%, and 83% for 1990, 2003, and 2019, respectively. The analysis highlighted a substantial increase in settlement area from 9.37 km2 in 1990 to 22.86 km2 in 2019, indicative of significant urbanization impacts.Our findings reveal a pronounced positive correlation between settlement growth and elevated LST, indicating the influence of urbanization on thermal dynamics. Additionally, positive correlations were observed between LST and NDBI, while a negative correlation existed between LST and NDVI. Questionnaire-based surveys complemented remote sensing data, exposing a gradual decline in wildlife sightings, diminishing populations of rare plant species, and the proliferation of invasive plants within and around the Wildlife Sanctuary since 1990.This research underscores the substantial impact of urbanization on the biodiversity of the Itanagar Wildlife Sanctuary. Positive correlations between settlement growth, LST, and biophysical parameters emphasize the urgency for targeted conservation strategies. The decline in wildlife sightings and the encroachment of invasive species underscore the need for proactive measures to balance biodiversity conservation with the challenges posed by urban development. Our integrated approach, combining remote sensing, survey data, and ecological indices, offers valuable insights for sustainable management and conservation efforts in similar habitats globally.