Toushif Jaman

Machine learning (ML)-based classification provides an effective approach for extracting land use/land cover (LULC) information from multi-temporal satellite observations. This study evaluates Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest (RF) for mapping LULC changes in Kullu district, Himachal Pradesh, India, using Landsat data from 1991 to 2022. SVM achieved the highest classification accuracy and was therefore used for subsequent LULC mapping. Six LULC classes were identified and their temporal changes were examined in relation to Landsat-derived land surface temperature (LST). The built-up area increased from 4.37% in 1991 to 20.40% in 2022, whereas vegetation declined from 34.41% to 16.23% and snow cover from 15.20% to 3.02%. The area-weighted mean LST was 4.10 °C in 1991, 10.03 °C in 2001, 8.43 °C in 2011, and 4.18 °C in 2022, representing a marginal net increase of 0.08 °C over the study period. Class-wise analysis showed that barren and agricultural lands generally recorded higher mean LST, while vegetation and waterbodies exhibited comparatively lower values. The relatively high snow-cover LST in 2022 may reflect reduced and fragmented seasonal snow cover and snowmelt conditions during image acquisition. Correlation analysis revealed an inverse relationship between LST and vegetation and snow-cover indicators, with R² values reaching 0.66 for NDVI and generally exceeding 0.62 for NDSI. Overall, the findings demonstrate substantial LULC transformation and pronounced spatial differences in surface temperature, emphasizing the role of vegetation and snow cover in regulating thermal conditions and providing useful information for sustainable land management and spatial planning in the ecologically sensitive Himalayan environment.

The escalating climate crisis presents a profound challenge to global environmental equilibrium, with rapid urbanization acting as a primary catalyst for land-use transformation. This study investigates the intricate relationship between land use and land cover changes (LULC) and the intensification of the Surface Urban Heat Island (SUHI) effect within the Shillong Planning Area (SPA). By integrating remote sensing data with advanced geospatial modeling and machine learning architectures which include Random Forest (RF), Support Vector Machine (SVM), and XGBoost, the research provides a comprehensive analysis of environmental shifts from 2000 to 2024, with predictive projections extending to 2034 and 2044. The analysis reveals a significant expansion in the built environment, with the Normalized Difference Built-up Index (NDBI) rising from 0.17 to 0.26. This urban growth has come at the expense of ecological health, as evidenced by a decline in the Normalized Difference Vegetation Index (NDVI) from a peak of 0.87 down to 0.74. A strong negative correlation between vegetative density and Land Surface Temperature (LST) underscores the critical role of green infrastructure in regional climate regulation. SUHI projections using the RF model, which achieved an Area Under the Curve (AUC) of 0.868, estimate SUHI values of 6.02 °C for 2034 and 6.66 °C for 2044. Predicted LULC scenarios for 2034 and 2044 suggest continued urban expansion, likely intensifying thermal stress. The application of predictive modeling through machine learning provides a robust framework to inform climate-resilient urban planning and sustainable land management.

Soil erosion is a critical environmental issue in the Brahmaputra River Basin, threatening agricultural productivity, water resources, and ecological balance. This study employs the revised universal soil loss equation (RUSLE) alongside remote sensing, geographic information systems (GIS), and advanced machine learning models like random forest (RF) and gradient boosting (GB) to analyze soil erosion patterns from 2005 to 2024. The analysis revealed that average annual soil loss increased from 15.8 tons/ha/year in 2005 to 25.4 tons/ha/year in 2024, marking a 60.76% rise over two decades. Peak erosion rates were observed in 2020, with localized hotspots recording up to 32,130 tons/ha/year. Spatial analysis from 2005 to 2024 indicated substantial variability, with soil loss values ranging from − 7.024 to 9034 tons/ha in 2005. Topographic influence, quantified using the LS factor, revealed that 47.2% of the basin area has slopes steeper than 16°, significantly contributing to elevated erosion risk. The rainfall erosivity (R-factor) fluctuated throughout the period, peaking at 2305.73 MJ mm/ha h year in 2015 but declining to 799.21 MJ mm/ha h year by 2024, indicating a temporal shift in rainfall patterns. Vegetation cover improvements during this time reduced the mean C-factor from 0.52 to 0.34, though 13.8% of the basin (approximately 3.05 million ha) still falls under high to very high erosion risk zones. RF model predictions achieved an R² of 0.915 and RMSE of 4.82, while GB attained an R² of 0.952 with RMSE of 3.97, indicating superior predictive performance. These findings underscore the urgent need for targeted soil conservation measures, afforestation programs, and sustainable watershed management. The integration of AI-driven modeling with remote sensing and GIS provides a robust framework for long-term soil erosion monitoring, enabling informed decision-making for climate adaptation in the region.