Assessing Land use Dynamics and Policies in the Waghur Basin Using Geospatial Techniques - Vikram Agone (International Journal of Land, 2026)
Changes in land use and land cover (LULC) are key indicators of human–environment interactions, especially in river basins where anthropogenic pressure is increasing. This study evaluated land use change and policy implications in the Waghur Basin, India, through a geospatial analysis of 35 years (1990–2025). Remote sensing and GIS-based supervised classification with the help of machine learning methods were applied to multi-temporal Land satellite images to create LULC maps and measure spatial–temporal changes. It was found that there are five large LULC classes, including agricultural land, forest land, bare land, settlements, and water bodies. The findings show that agricultural land increased significantly by 987.45 sq. km (39.74 percent) in 1990 to 1389.13 sq. km (55.90 percent) in 2025 at the expense of bare land, which reduced by 408.00 sq. km. Forest land showed a moderate loss of 26.89 sq. km, which is a sign of further forest degradation and fragmentation. Settlement areas almost doubled, indicating slow urbanization, and water bodies also increased because of water resource development efforts. These transformations point to the high impact of land-use policies, agricultural intensification, and infrastructure development on the landscape structure of the basin. The paper shows that geospatial methods are efficient in tracking LULC dynamics and that the method can be used to assess land-use policies. The results highlight the necessity of integrated, sustainable land-use planning that would balance agricultural development with forest protection, water management, and controlled settlement development to achieve environmental sustainability in the Waghur Basin in the long term.
Keywords: GIS; Machine Learning; artificial intelligence; land use change detection; remote sensing