Avtar Singh Jasrotia

This study presents a landslide susceptibility assessment using multiple parameters by integrating remote sensing, GIS, and field observations in the Jammu and Kashmir region. Eight contributing variables were selected for landslide susceptibility analysis: Land Use and Land Cover (LULC), proximity to roads, streams, slope gradient, slope orientation (aspect), geology, geomorphology, and elevation. In addition, an extensive landslide inventory consisting of 669 landslide events was developed using the Field Landslide Inventory Mapping (FLIM) application, LISS-IV satellite data, and field observations, covering an area of 42,950.43 km². Landslide susceptibility mapping (LSM) was carried out using the Analytical Hierarchy Process (AHP) approach and validated with MaxEnt software and field-generated landslide data. The resulting landslide susceptibility map was classified into five categories: very high, high, medium, low, and very low susceptibility zones. Based on the AHP approach, these zones cover 3.65% (1,569.0483 km²), 24.43% (10,492.8912 km²), 51.56% (22,147.2369 km²), 18.81% (8,079.2199 km²), and 1.54% (662.0301 km²) of the study area, respectively. The weighted overlay and MaxEnt models proved effective for landslide vulnerability mapping, with MaxEnt achieving AUC values of 0.82 for training data and 0.807 for testing data, indicating good predictive performance. Field validation further showed that 87% of landslides occurred within high and very high susceptibility zones. The Jackknife test identified road proximity, slope, and stream proximity as the most significant independent variables influencing landslide occurrence. The generated landslide susceptibility map provides important insights for reducing landslide risk and serves as a valuable tool for infrastructure planning, community development, and disaster management in the region.

Land use/land cover (LULC) change significantly influences a range of environmental and socio-economic issues, including climate change, deforestation, biodiversity loss, soil degradation, ecosystem services, and food security, at local, regional, and global levels. In the northwestern Himalayan region, particularly in Rajouri district of Jammu and Kashmir (J&K), LULC change has profound environmental and socio-economic implications. Understanding the temporal and spatial dimensions of LULC change is crucial for assessing the impact of human activities on the region’s environment. The present study aimed to analyze LULC change in Rajouri district of J&K, India over a 30-year period from 1990 to 2020 and to project future LULC dynamics for the next 30 years up to 2050. Landsat imagery with a supervised classification technique was used for classification and generation of LULC maps. Moreover, CA Markov model was used to predict the future LULC status of the area. The model validation exhibited strong performance, with Kappa statistics exceeding 0.90, indicating a high level of reliability in the projections. The results indicate considerable changes in different land use classes from 1990 to 2020. Over the 30-year period, dense forest showed the maximum reduction of about −20.69 Km², followed by open forest (−15.87 Km²) and grassland (−13.75 Km²). Wasteland showed the maximum increase of about +28.24 Km², followed by built-up (+17.90 Km²) and cropland (+12.50 Km²). The cumulative impact of deforestation from 1990 to 2020 amounts to approximately 43.17 Km², while afforestation efforts only managed to reclaim 6.61 Km² of land. The future prediction using the CA Markov model suggests further changes in LULC patterns, with built-up, cropland, and wasteland projected to increase exponentially by 2050, accompanied by sharp declines in forests. Therefore, policymakers should prioritize sustainable land management and forest conservation strategies to mitigate the potential negative impacts of LULC changes on the environment, ensuring balanced and sustainable development.