Slowly moving active landslides threaten infrastructure, particularly along highway routes traversing active mountainous ranges. Detecting and characterizing such landslides in highly elevated mountainous terrains is challenging due to their inaccessibility, wide area coverage, limited approaches, and the complex nature of mass movements. In this study, we processed Sentinel-1 Synthetic Aperture Radar data acquired from 2015 to 2024 to detect active landslides along the Karakoram Highway alternate route (Chitral-Gilgit) and the Karakoram Highway part (Gilgit-Khunjerab). We detected 1037 active landslides in the study region using phase gradient stacking and a deep learning network. Based on the detection, we applied time series InSAR analysis to reveal the velocity and deformation series for some large-scale landslides, revealing high displacement rates with line-of-sight velocities reaching up to −81 mm/yr. We validated our detections by comparing them with Google Earth imagery and the previously published landslide inventories along the Karakoram Highway. This study reveals the spatial distribution of active landslides along the uplifted mountainous terrain, highlighting potentially unstable zones, and offers insights into hazard mitigation and risk analysis, especially for less monitored economic roads in orogenic zones.

Multispectral and hyperspectral remote sensing represent critical instrumental methodologies that significantly enhance geological mapping and facilitate the identification of mineral resources. Collisional remnants of Himalaya-Hindukush northern Pakistan offer a unique opportunity to explore the hydrothermal deposits hosting economic minerals, inaccessible due to rugged and steep topography. The primary applications of ASTER and ZY1-02D remote sensing data include the extraction of alteration zones to detect economically significant minerals and depicting lithological units. To assess potential mineralization zones in the rugged terrain of northern Pakistan, herein, we implement integrated remote sensing techniques like false color composite, decorrelation stretching, band ratio, machine-learning techniques (maximum likelihood classification, spectral angle mapper, neural net classification, and match filtering), and validation (field observations). The results revealed 11 lithological units and several mineralization zones with key indicator minerals. These zones contained clay minerals, i.e. kaolinite, muscovite/sericite, and propylitic minerals, including calcite and chlorite, indicating copper and iron mineralization. The detected minerals emphasize that hydrothermal alteration in the region, controlled by the hot fluids from the granitic plutons (Tarichmir, Garam Chashma, and Kafiristan) of northwest Pakistan. The acquired mineral signature from remote sensing data was validated with ground truth observations, spectrometry, and petrographic analysis. Furthermore, this study proved the effectiveness of implemented approaches in delineating mineralization zones and discriminating lithological units.

Landslide susceptibility mapping of Chitral, northwestern Pakistan using GISMukhtar S. Ahmad1, *, Mona Lisa1, Saad Khan2 Munawar Shah31Department of Earth Sciences, Quaid-I-Azam University, Islamabad 45320, Pakistan2Bacha Khan University Charsadda, Pakistan3Department of Space Science, Institute of Space Technology, 44000 Islamabad, Pakistan1mukhtargeo44@gmail.com1lisa_qau@yahoo.com2saadkhan@bkuc.edu.pk3shahmunawar1@gmail.com*Corresponding author: mukhtargeo44@gmail.comAbstractLandslides are the most frequently occurring geohazard in rugged Himalayan mountainous terrains. They often cause significant loss to life and property, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. In this study, LSM is carried out in the Chitral district of the Hindukush region in northwestern Pakistan. Several Geographic Information System (GIS) based models (such as Analytical Hierarchy Process (AHP), weighted overlay) has been used to build landslide inventory and susceptibility maps. The study incorporated nine main factors (including human-induced parameters, such as distance from road; topographical parameters, such as slope, aspect, and landcover; geological parameters, such as lithology, distance to fault, seismicity; hydrological parameters, such as rainfall and distance to stream) to generate LSM, further classified in five classes, very high susceptibility zone, high, moderate, low, and very low susceptible zone. It is concluded that most of the landslides in the study area are the result of steep slopes of mountains, followed by precipitation and earthquake. Landslide in the form of rockfall is mostly due to the active seismicity of the Hindukush region. The predicted susceptible zones of landslide in the study area are in good agreement with the past landslide localities, which is an indication of the susceptibility mapping of landslides in the region.

Soil erosion is a significant environmental concern that threatens agricultural activities, reduces soil fertility, and eventually impacts productivity. Assessing soil erosion is essential for effective planning and conservation initiatives in a basin or watershed. This study aims to assess water-induced soil erosion using machine learning techniques and identify key factors contributing to erosion vulnerability in a watershed. This study employed three advanced machine learning techniques: Random Forest (RF), k-Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost) - to analyze and forecast water-induced soil erosion patterns. The investigation identifies key factors contributing to soil erosion vulnerability by utilizing a comprehensive dataset derived from Digital Elevation Models, climatic records, and land use patterns. The models were trained on 80% of the data, while the other 20% were used for evaluation, resulting in an accuracy demonstrating their robustness in environmental modeling across various topographic features. The models were extensively assessed using various accuracy measures, including sensitivity, specificity, precision, and the Kappa coefficient. The Area under the Curve (AUC) values for the models were 87% for RF, 89% for kNN, and 91% for XGBoost, indicating high predictive performance. RF, kNN, and XGBoost models demonstrated high sensitivity values (0.9, 0.87, and 0.91, respectively) and specificity (0.9, 0.86, and 0.89). The Kappa index for the ML models was 0.80 for RF, 0.73 for kNN, and 0.80 for XGBoost. These metrics indicated that RF, kNN, and XGBoost are highly effective in identifying water-induced soil erosion in the research region. This study not only identifies critical sites susceptible to erosion but also provides a decision-support tool for evaluating soil erosion within the investigated area and similar riverine ecosystems.