Kanwarpreet Singh

Landslides are among the most significant natural hazards in the Himalayan region of India, causing substantial damage to infrastructure, natural resources, and local communities. This study presents an integrated hydro-geotechnical approach for regional landslide susceptibility assessment by combining statistical susceptibility mapping with slope stability evaluation. The proposed framework considers the influence of key hydrologic factors, including rainfall, drainage density, topographic wetness, and groundwater conditions, together with geotechnical properties to better explain the mechanisms controlling slope failure. Five bivariate statistical models, namely, frequency ratio (FR), information value (IV), weight of evidence (WoE), certainty factor (CF), and Shannon entropy (SE), were applied to produce landslide susceptibility maps. Their performance was evaluated using the area under the curve (AUC) and landslide density index (LDI). Among the evaluated models, SE achieved the highest predictive accuracy ( AUC = 87.17 % ), followed closely by FR (87.06%). The results indicate that steep slopes ( > 30 ° ), weak lithological formations, high drainage density, and groundwater conditions are the main factors controlling landslide occurrence in the study area. Slope stability was further evaluated using laboratory-derived geotechnical parameters and circular failure charts (CFC), providing an independent assessment of slope conditions under different groundwater scenarios. The combined use of statistical modeling, hydrologic factors, and geotechnical verification improves the reliability of landslide susceptibility assessment and provides a practical framework that can be applied in other mountainous regions affected by rainfall-induced landslides.

Land surface temperature (LST) is an important parameter for monitoring the thermal condition of the land surface, especially in rapidly urbanizing mountain areas. This study examines the spatial pattern of LST and its statistical association with land use/land cover (LULC) in Sundernagar Tehsil, Himachal Pradesh, India, using Landsat 8 OLI/TIRS Collection 2 Level 2 scenes of 23 March 2015, 4 March 2020 and 18 March 2025. The three scenes were acquired on different dates within March; therefore, each scene is treated as a separate cross-sectional observation, and differences in absolute LST among dates are not interpreted as long-term change. LST was taken from the Surface Temperature band (ST_B10) and summarised for five LULC classes: Protected Area, Agricultural Area, Build-up Area, Barren Land and Water Bodies. Zonal statistics gave the same thermal order in all three scenes, with Barren Land showing the highest mean LST (29.74 °C, 22.20 °C, 25.21 °C) and Water Bodies the lowest (24.05 °C, 18.18 °C, 20.72 °C). Neighboring pixels are spatially autocorrelated and cannot be treated as independent observations. Therefore, the pixel-level Pearson correlations between LST and the spectral indices (NDVI, NDWI, and NDBI) are interpreted as descriptive measures of association, while nominal p-values and confidence intervals are not treated as inferential statistics. On the full raster, NDBI had positive correlations with LST (r = 0.3671, 0.2406, and 0.3154), while NDWI had negative correlations (r = −0.2790, −0.2988, and −0.2801). Across the three spectral indices, the unadjusted relationships explained approximately 2–13% of LST variance. NDVI showed weak positive unadjusted correlations with LST (r = 0.1315–0.1879), but its regression coefficient became negative in all three scenes after adjustment for elevation, slope, and aspect. Adding terrain variables to the LULC models increased R² from 0.148–0.315 to 0.598–0.632, indicating that interpretation of land cover thermal associations was sensitive to terrain adjustment. The results show a consistent association between land cover and surface temperature within each date and can support land use planning in Himalayan towns, but they do not show a causal effect or a long-term trend.

Land-use/land-cover (LULC) change affects the balance between settlements, agricultural land, and natural ecosystems, making its assessment important for land-use planning in mountainous regions. This study integrates Random Forest (RF) classification and Cellular Automata–Artificial Neural Network (CA-ANN) modeling to assess historical LULC change in Sarkaghat Tehsil, Himachal Pradesh, and generate baseline projections for 2031 and 2046. Landsat imagery from 2016, 2021, and 2026 was classified into six classes: Forest Land (FL), Agricultural Land (AL), Built-up Land (BUL), Barren Land (BL), Water Body (WB), and Grass Land (GL). Overall classification accuracy was 95%, 96%, and 93%, respectively, with Kappa coefficients of 0.91, 0.94, and 0.89. The 2026 map had the lowest accuracy; the lower agreement was particularly evident for Grass Land, which had a user’s accuracy of 0.83. Historical comparisons from 2016 to 2026 showed increases in BUL (0.46%), BL (0.04%), and GL (0.16%), and decreases in AL (0.45%) and FL (0.21%); WB changed negligibly. The CA–ANN simulation of 2026, evaluated against the independently classified 2026 map, achieved an overall accuracy of 97.898% and a Kappa coefficient of 0.965. Relative to 2016, the 2031 baseline projection indicates changes of FL 0.00%, AL −0.37%, BUL +0.27%, BL −0.03%, WB 0.00%, and GL +0.13%. For 2046, the corresponding changes are FL −0.01%, AL −0.37%, BUL +0.27%, BL −0.03%, WB 0.00%, and GL +0.14%. The projections are conditional on stable historical transition relationships and should be interpreted as baseline scenarios rather than certain forecasts. The integrated workflow provides a locally evaluated basis for assessing land transitions and informing land-use planning in Sarkaghat Tehsil.

Heavy Metals (HMs) are persistent, non-biodegradable metallic elements that accumulate into soils due to weathering processes and anthropogenic activities, such as mining, smelting and industrial discharges. They can be classified as essential (Cu, Zn, Ni, Fe, Mn), which are toxic in excessive amounts, and non-essential (Cd, Pb, As, Hg), which are dangerous even in small amounts. HMs affect plant metabolism causing production of Reactive Oxygen Species (ROS), growth inhibition and nutrient deficiency, Arabidopsis thaliana seedlings germination was lowered to 55% and 21% at concentrations of 50 and 100 mg L⁻¹ CdCl₂. Phytochelatins, metallothioneins and vacuole sequestration protect plants, but become ineffective at high concentrations of HMs. Metal-resistant fungi help to cope with stress conditions using mechanisms of biosorption, bioaccumulation, biomineralization, bioleaching and biotransformation, adapted Aspergillus niger managed to leach 78.5% Ni and 82.3% Mo during 30 days, while Penicillium chrysogenum eliminated 63% Cd in soil. Partners of fungi, such as Trichoderma and arbuscular mycorrhizal fungi, regulate production of ROS, activation of MAPKs and ABA/JA/SA/ethylene/auxin signaling pathway and expression of MT, PCS, GST, ABC, NRAMP and HMA genes under control of WRKY, MYB, NAC and bZIP transcription factors. Graphic abstract