Spatio-temporal Analysis of Land Use/Land Cover Dynamics and Vegetation Change Using Geospatial Techniques: A Case Study of Dadenggre, West Garo Hills, Meghalaya, India (2000–2025)
Phrangshai Hynniewta *
Captain Williamson Sangma State University, Balalgre, Tura, Meghalaya, India.
Satabdi Banerjee
William Carey University, Shillong, Meghalaya, India.
*Author to whom correspondence should be addressed.
Abstract
Land use and land cover changes, driven by socio-economic and environmental factors, are critical for understanding ecological sustainability and environmental transformations in biodiverse regions like Northeast India. This study investigates spatio-temporal land-use/land-cover (LULC) dynamics and associated vegetation change in Dadenggre Block, West Garo Hills District, Meghalaya, India, over a 25-year period (2000–2025) using multi-temporal satellite data and geospatial techniques. Landsat and Sentinel imagery were classified into five major LULC categories—forest, agriculture, settlement, water bodies, and open/degraded land—for the years 2000, 2010, 2020, and 2025. Supervised classification methods were employed, and classification accuracy was assessed using stratified random sampling and confusion matrix analysis.
LULC transitions were quantified through post-classification comparison and change detection matrices to evaluate class-wise conversions, as well as net gains and losses. The findings (to be updated with empirical results) indicate a substantial decline in forest and agricultural areas, accompanied by a notable expansion of settlements and open/degraded land, with the most pronounced changes occurring after 2010. Field-based validation and stakeholder interviews reveal that these transformations are primarily driven by population growth, rural–urban migration, expansion of smallholder agriculture and shifting cultivation practices, infrastructure development, and localized coal and stone extraction.
The study provides a robust and reproducible geospatial framework integrating classification, accuracy assessment, change detection, and field validation approaches. This methodological framework is transferable to similar ecologically sensitive and data-scarce regions, particularly in the North Eastern Hill region of India, and contributes to informed land management and sustainable planning strategies.
Keywords: Remote sensing, geographic information systems (GIS), Landsat, Sentinel-2, change detection, accuracy assessment, vegetation dynamics, land-use drivers.