Estimating the plausible projections of land use/land cover dynamics in Jhelum and Chenab River basins using satellite imageries and machine learning models in Google Earth Engine

The objectives of this study are to map the past and present LULC accurately, ensure the precision of classification and prediction models, and estimate future LULC changes using Google Earth Engine. LULC classification from 2014-2024 was performed using robust SmileCART and future LULC prediction for 2024, 2030, 2040, 2050, 2060 and 2070 was conducted using the Smile Random Forest (RF) algorithm incorporating various socio-economic variables. The classification model SmileCART was trained by using the European Space Agency World Cover data and validated through Landsat 8 satellite imagery, achieving training and test accuracies of 83% and 84% respectively. SmileRF prediction model showed an accuracy of 87% with a kappa coefficient of 0.86. The results indicate a decline in vegetation cover, snow & ice and water bodies, and an increase in built-up areas and cropland, with other classes showing fluctuations in the Jhelum and Chenab River basins from 2014 to 2070. These insights contribute to a deeper understanding of these critical watersheds, informing sustainable land management, water resource planning, and decision-making for the future sustainable development of the Jhelum and Chenab River basins using GEE and remote sensing data.