Spatiotemporal assessment of forest cover dynamics in Lohit District, Arunachal Pradesh using remote sensing and GIS techniques

Forests in the eastern Himalayan region play a critical role in climate regulation, biodiversity conservation, hydrological stability, and livelihood support. However, increasing population pressure, land-use change, infrastructure expansion, and climate-related stressors are accelerating forest degradation across Northeast India. Existing forest cover change studies in the region are often limited by short temporal coverage, coarse spatial analysis, insufficient uncertainty assessment, and limited integration of complementary vegetation indices in complex mountainous environments. To address these gaps, this study assesses long-term forest cover dynamics in Lohit District, Arunachal Pradesh, over a 35-year period (1988–2023) using multi-temporal Landsat TM and OLI imagery integrated with geographic information system techniques. Forest cover was evaluated using the Normalized Difference Vegetation Index (NDVI) through radiometric and atmospheric correction, threshold-based classification, and post-classification change detection at approximately five-year intervals. In addition, the Normalized Difference Moisture Index (NDMI) was used to improve interpretation of vegetation moisture conditions and degradation patterns. Land-cover classes included water bodies, bare surfaces, shrub–grassland, moderate forest, and dense forest. Classification reliability was evaluated using multi-temporal consistency analysis, visual interpretation, and cross-validation with high-resolution imagery (Google Earth). Results indicate a substantial decline in dense forest cover from approximately 70% in 1988 to about 20% in 2023, with accelerated losses after 2013. Concurrently, shrubland and moderate forest expanded, reflecting secondary succession, canopy thinning, and progressive forest degradation rather than abrupt deforestation. These changes are driven by shifting cultivation, timber extraction, infrastructure development, demographic pressure, and climatic variability. The study demonstrates the effectiveness of integrating NDVI with NDMI for long-term forest monitoring and provides insights for sustainable forest management.