The main objective of this study is to assess the prediction and success rate based on bivariate frequency ratio (FR), weight of evidence (WoE), Shannon entropy (SE), and information value (IV) models for landslide susceptibility in the sedimentary terrain of Nepal Himalaya, as the area is facing threat for sustainable development as well as sustainable resource management. This study also seeks to evaluate the causative factors for landslide susceptibility. Initially, a landslide inventory map was created, consisting of 1158 polygons. These polygons were randomly divided into two sets, with a ratio of 70% for training and 30% for testing data. The multicollinearity approach was evaluated to assess the relevance of selected conditioning variables and their inclusion in the model construction process. The area under the curve (AUC) and other arithmetic evaluation methods were employed to validate and compare the outcomes of the models. In comparison, the predictive accuracy of the FR model surpasses that of the IV and SE models. The success rates, ranked in descending order, are as follows: WoE (79.9%), FR (75.3%), IV (74.4%), and SE (73.2%). Similarly, the success rates of four distinct models, namely WoE, FR, IV, and SE, are 85%, 78.75%, 78.57%, and 77.2%, correspondingly. All models have an accuracy and prediction rate exceeding 70%, making them suitable for assessing landslide susceptibility in the Siwalik Hills of Nepal. Nevertheless, the weight of evidence model provides more precise outcomes than other models. This study is expected to provide important information for road and settlement sustainability in the study area.

Reconstructions of failure surfaces (prior to potential landslides or after their release), landslide deposits, or other palaeotopographic features are important for hazard and erosion assessment. The volumes involved in landslide and failure surfaces constrain the propagation of a landslide, and knowledge of the past topography helps us to understand these hazards. Some methods exist to characterise landslide geometry, but these methods usually require monitoring information. This study tries to assess the validity of the sloping local base level (SLBL) method for this purpose. Two sets of airborne lidar digital elevation models (DEMs) of the Kii Peninsula (Japan) are used: the first one was acquired before Typhoon Talas, and the second one was acquired after. A total of 70 deep-seated landslides occurred during this event between 2 and 5 September 2011. This study shows that the SLBL method is efficient using either the slope deformations identifiable on the DEM before the release of the landslide or a reliable 2.5-D failure surface created by using both DEMs (the 2.5-D corresponds to a surface which has only one z value for each x–y coordinate; in other words, no true vertical topography or overhang can be represented perfectly). In addition, this method allows for the reconstruction of eroded deposits and buried valleys. Most of the volumes estimated are within ±35 % of the estimation made by Chigira et al. (2013), and the coefficients of expansion range from 10 % to 25 %. These results show considerable sensitivity to the parameters used for the reconstruction of the landslide volume estimations and demonstrate the need for an efficient and fast tool to reconstruct potential landslide geometries or histories.

A rapidly growing population across mountain regions is pressuring expansion onto steeper slopes, leading to increased exposure of people and their assets to slow‐moving landslides. These moving hillslopes can inflict damage to buildings and infrastructure, accelerate with urban alterations, and catastrophically fail with climatic and weather extremes. Yet, systematic estimates of slow‐moving landslide exposure and their drivers have been elusive. Here, we present a new global database of 7,764 large (A ≥ 0.1 km²) slow‐moving landslides across nine IPCC regions. Using high‐resolution human settlement footprint data, we identify 563 inhabited landslides. We estimate that 9% of reported slow‐moving landslides are inhabited, in a given basin, and have 12% of their areas occupied by human settlements, on average. We find the density of settlements on unstable slopes decreases in basins more affected by slow‐moving landslides, but varies across regions with greater flood exposure. Across most regions, urbanization can be a relevant driver of slow‐moving landslide exposure, while steepness and flood exposure have regionally varying influences. In East Asia, slow‐moving landslide exposure increases with urbanization, gentler slopes, and less flood exposure. Our findings quantify how disparate knowledge creates uncertainty that undermines an assessment of the drivers of slow‐moving landslide exposure in mountain regions, facing a future of rising risk, such as Central Asia, Northeast Africa, and the Tibetan Plateau.

In recent decades, forests have experienced an increasing trend in the number of pest outbreaks worldwide, apparently driven by strong annual variability in precipitation, higher air temperatures, and strong winds. Pest outbreaks have negative ecological, economic, and environmental impacts on forest ecosystems, such as reduced biodiversity, carbon sequestration, and overall forest health. Traditional monitoring methods of these disturbances, while accurate, are time-consuming and limited in scope. Remote sensing, particularly UAV (Unmanned Aerial Vehicle)-based technologies, offers a precise and cost effective alternative for monitoring forest health. This study evaluates the temporal and spatial progression of bark beetle damage in a fir-dominated forest in the Zao Mountains, Japan, using UAV RGB imagery and DL (Deep Learning) models (YOLO - You Only Look Ones), over a four-year period (2021–2024). Trees were classified into six health categories: Healthy, Light Damage, Medium Damage, Heavy Damage, Dead, and Fallen. The results revealed a significant decline in healthy trees, from 67.4% in 2021 to 25.6% in 2024, with a corresponding increase in damaged and dead trees. Light damage emerged as a potential early indicator of forest health decline. The DL model achieved an accuracy of 74.9% to 82.8%. The results showed the effectiveness of DL in detecting severe damage but highlighted that challenges in distinguishing between healthy and lightly damaged trees still remain. The study highlights the potential of UAV-based remote sensing and DL for monitoring forest health, providing valuable insights for targeted management interventions. However, further refinement of the classification methods is needed to improve accuracy, particularly in the precise detection of tree health categories. This approach offers a scalable solution for monitoring forest health in similar ecosystems in other subalpine areas of Japan and the world.