ABSTRACT Rock glaciers are an indispensable component of mountain cryosphere depicted as a promising future water reserve under ongoing global warming scenario. Conventionally, rock glaciers have been studied using high-resolution datasets employing geomorphic and kinematic-based approaches. The newly ventured domain of automatic detection of rock glaciers proved to be challenging and requiring competent skill and computation. The purpose of this study is to encourage the early career researchers to explore simpler techniques to detect and distinguish rock glaciers automatically, using free datasets. The present study provides a very simple semi-automatic method to differentiate rock glaciers developed in the complex mountain settings exploring widely accessible medium resolution optical datasets such as Sentinel 2A. The topographical peculiarity of rock glaciers were infused using freely obtained Advanced Land observing Satellite (ALOS) Digital Elevation Models (DEM). The visible and infrared bands of Sentinel 2A with hillslope and slope parameters were infused following some elementary image processing steps such as band ratio, principal component analysis (PCA) and band enhancement techniques to detect and distinguish rock glaciers semi-automatically.

Climate and land-use interactions shape the future of an endangered butterfly in a warming world

Methods for estimating building height based on street view images encounter challenges such as dependence on labeled data, image distortion, terrain relief, and occlusion. To address these issues, this study proposes a zero-shot segmentation model and a multi-feature constraint framework based on segment anything model 2 (SAM2) for arbitrary object segmentation without training. Two building semantic constraints are introduced: geospatial constraints, wherein SAM2 building prompt points are generated by intersecting the street-view skyline with building footprint orientation; and visual feature constraints, in which vegetation index and texture features are fused to eliminate non-building feature points, suppressing occlusion interference. Street view shooting location projections onto building footprints replace roof corner points, reducing equirectangular projection distortion. During height inversion, digital elevation model data convert relative heights into absolute elevations, whereas multi-view consistency optimization resolves errors from single-view occlusion and terrain relief. For buildings with insufficient street-view coverage, elevation interpolation leverages neighborhood spatial correlation. Final height is derived from roof–ground elevation difference. Experiments conducted in Nanjing (52 buildings) yielded an average absolute error of 1.36 m, with 100% accuracy within 4 m. A large-scale experiment in Guangzhou (>20,000 buildings) further demonstrated the superior accuracy, robustness, and scalability of the proposed framework.

We present a global survey of 232 ice-exposing scarps incised into the Latitude Dependent Mantle (LDM) on Mars and analyze their morphology, spatial distribution, and geomorphological context. These features are confined to 40–60°latitude due to the latitudinal dependence of sublimation-driven scarp formation and the equatorward extent of excess subsurface ice. Scarps are predominantly located on the lower, concave parts of leeward hillslopes, suggesting these areas represent zones of preferential snow accumulation and preservation. Using high-resolution digital terrain models, we estimated the local thickness of the Latitude-Dependent Mantle (LDM) from the depth of scarp depressions. Values range from 40 to over 190 meters, with thicker mantles observed downslope and on leeward slopes. The morphology and internal stratigraphy of the exposed layers reveal variable geometries, including fine, dipping layering interpreted as wind-blown snow mixed with dust and sand, similar to terrestrial niveo-aeolian deposits. These results support the hypothesis that the LDM formed through atmospheric processes, with wind and topography jointly controlling the distribution, structure, and thickness of ice-rich deposits. This has implications for Amazonian climate interpretations, as the LDM may not preserve a continuous climate record, and for future in situ resource utilization (ISRU), where ice accessibility is likely to vary greatly at small scales depending on local terrain and past depositional conditions. • Ice-exposing scarps preferentially form on the lower parts of leeward hillslopes, suggesting the ice that hosts them accumulated as wind-driven snow. • The thickness of the ice that hosts scarps is highly variable, ranging from 40 to more than 190 meters, and depends on local topography and potentially paleo wind direction. • Scarp setting and exposed layers support an atmospheric origin for the LDM, with implications for both climate interpretation and future ice resource accessibility on Mars.

Abstract Determining long‐term soil erosion and deposition rates and understanding landform evolution are important for managing both natural and human‐modified landscapes, including postmining rehabilitation sites. Although various landform evolution models (LEMs) have been developed to simulate erosion processes and landscape change, relatively few studies have directly compared modelled outputs with field‐based erosion estimates. This study evaluates and compares two LEMs, (i) SIBERIA, which is widely used in the Australian mining sector, and (ii) SSSPAM, a newer coupled soilscape–LEM, together with the well‐established soil erosion model (the revised universal soil loss equation). A formerly grazed hillslope in the Upper Hunter region of New South Wales was used as the case study, and a high‐resolution light detection and ranging (LiDAR)‐derived digital elevation model was used as the landscape input. Field‐based erosion rates were quantified using sediment yield data from a catchment dam and 137 Cs isotopic analysis. Sediment trap measurements indicated erosion rates ranging from 0.43 to 0.61 t/ha/year, while 137 Cs results showed erosion and deposition rates of up to 1.5 and 1.1 t/ha/year, respectively. SIBERIA predicted erosion rates of 1.07 t/ha/year under dense vegetation cover and 3.74 t/ha/year under moderate cover, while SSSPAM estimated 0.35 and 2.43 t/ha/year for the same conditions. The RUSLE model predicted an average erosion rate of 2.23 t/ha/year, with values ranging from 0.58 to 4.65 t/ha/year. Overall, the modelled erosion estimates were broadly consistent with the field observations, demonstrating the ability of both SIBERIA and SSSPAM to reproduce realistic hillslope erosion rates. These findings support the use of LEMs as valuable tools for guiding sustainable land management and rehabilitation practices in both natural and constructed landscapes.

ABSTRACT Au mineralization and emplacement processes are related to several factors, including the deformation of the underlying rocks, which influences hydrothermal movements, particle displacements, and geomorphology. In mountainous regions with difficult access and sampling conditions, the use of remote sensing multispectral and hyperspectral imagery is becoming increasingly popular for gold prospectivity mapping due to its ability to reveal hydrothermal alteration minerals and geological structures associated with gold. These images can quickly indicate the spatial distribution of complex structures and mineralization alteration indices in these regions. The Edrengin Nurruu orogenic belt, located between the orogenic provinces of Bayanling Bayangovi and Tumurteyn Nuruu in the southwest part of Mongolia’s complex geological structure, is a typical example of gold mineralization. We mapped gold prospectivity using a set of geophysical (gravity and magnetic) anomaly images and remote sensing imagery (multispectral Sentinel-2, radar Sentinel-1, and digital elevation models). The particularity lies in extracting and classifying gold resource indices based on the joint use of optical, radar, gravity, and magnetic image processing results. A binary encoding classification algorithm was used to map the study area’s gold prospectivity, based on the gold indices highlighted by the various processing operations carried out on the geospatial data. The binary encoding classification gave an overall accuracy of 94.80% and enabled us to accurately extract zones with a high probability of gold occurrence. The approach established in this work is offered as a strategy for enhancing the mapping of surface gold prospectivity and assisting in making additional forecasts. The map, therefore, can provide guidance and reference for future gold exploration in the area, where no prospecting studies have yet been carried out, and can be helpful for mineral resource assessment.

Urban microtopography plays an important role in regulating soil processes and vegetation performance in newly constructed green spaces, yet its effects on surface runoff, soil nutrients, and plant growth remain insufficiently quantified in urban relocation sites. This study investigated how slope gradient, slope position, and slope curvature influence surface runoff, soil nutrient distribution, and tree growth in Shanghai Expo Cultural Park. Field monitoring was conducted in 36 plots planted with Cinnamomum camphora and Ginkgo biloba in 2017, 2020, and 2024. Microtopographic characteristics were quantified using terrestrial and handheld three-dimensional laser scanning, point-cloud processing, and digital elevation models (DEMs), and plant growth, calculated runoff, and soil physiochemical properties were analyzed using analysis of variance (ANOVA) and regression analysis. Annual DBH increments were greatest on meso slopes (mean = 0.558 cm), followed by gentle slopes (0.513 cm) and abrupt slopes (0.511 cm). Growth was also greater at slope-tail positions than at slope-head positions and greater on concave slopes than on convex slopes. The mean calculated runoff increased from gentle to meso and abrupt slopes, and soil organic matter, total nitrogen, hydrolysable nitrogen, available phosphorus, available potassium, and cation exchange capacity were generally higher at slope-tail positions. These results indicate that micrographic design affects tree growth mainly through runoff-mediated redistribution of water and soil nutrients. These findings provide practical guidance for optimizing microtopographic design, tree species selection, and soil management in urban green spaces established on relocation sites.

Glacial lake outburst floods (GLOFs) represent one of the most severe climate-induced hazards in high-mountain regions. The Shisper Glacier in Hassanabad Village, Hunza, Pakistan, has experienced repeated surge events and rapid ice-dammed lake expansion, posing escalating risks to downstream communities and critical infrastructure. This study presents a multi-temporal, multi-index remote sensing and GIS-based assessment of Shisper Glacier dynamics and associated GLOF hazards for the period 2020-2024. Landsat-8 OLI imagery was used to compute the normalized difference snow index (NDSI), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), land surface temperature (LST), land use and land cover (LULC), and green-red-NIR-shortwave infrared (GRZI) composite indices. Elevation change analysis was performed using the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), while NASA POWER data provided climatological context for temperature, precipitation, and humidity patterns. The ice-dammed lake area at the glacier's snout was delineated using MNDWI-based water body extraction from Landsat-8 OLI imagery, and lake volume was estimated using a depth-area empirical scaling relationship, consistent with established methodologies for ice-dammed lakes in the Karakoram. Results indicate that the debris-covered glacier expanded from 161 km2 in 2015 to 197 km2 in 2018, while the ice-dammed lake exceeded 0.1 km2 with an estimated volume between 1 × 106 m3 and 10 × 106 m3, representing a substantial and growing GLOF hazard. Rising land surface temperatures and changing precipitation patterns are identified as key drivers of accelerated glacial melt. Approximately 80% of Hassanabad Village's infrastructure, including the Karakoram Highway Bridge and a nearby hydropower facility, lies within the projected GLOF inundation zone. The novelty of this study lies in the first integrated application of seven spectral indices combined with DEM-based surge mapping and infrastructure exposure assessment for Shisper Glacier within a unified hazard framework. These findings underscore the urgent need for continuous satellite-based monitoring, early warning systems, and risk-informed land-use planning in the Hunza region.

Precise inversion of soil water-salt and management zoning for oasis cropland in arid regions.

Navigating hilly orchards is challenging for autonomous agricultural vehicles due to the rugged terrain and dense canopy cover. Standard environmental modeling techniques are widely used, yet they often overlook how elevation uncertainty propagates during Digital Elevation Model (DEM) reconstruction. This oversight can directly affect terrain risk assessments and navigation planning. From an error-propagation perspective, this review examines how uncertainties originating from RTK-GNSS, LiDAR, and computer vision propagate through DEM reconstruction, terrain-feature extraction, cost map construction, and path planning. We further analyze how DEM elevation errors and vertical inaccuracies affect slope estimation, roughness representation, traversability assessment, vehicle stability, and navigation safety. Finally, we highlight practical bottlenecks in hilly orchard scenarios and suggest several research priorities, including multimodal fusion, uncertainty-aware modeling, lifelong map updating, and learning-based traversability assessment.

ABSTRACT Unmanned aerial vehicles (UAVs) are increasingly used for post-conflict and disaster mapping because they enable rapid acquisition of high-resolution imagery and dense 3D point clouds in hazardous or inaccessible areas. This study evaluates the accuracy of UAV-derived point clouds for Kafranbel, Syria, a 143-hectare war-damaged urban area where rubble, vegetation, and damaged structures complicate ground control placement and photogrammetric reconstruction. Four UAV missions were conducted using a DJI Mavic 2 Pro: three nadir flights at 45 m, 50 m, and 65 m altitude with approximately 80% front and side overlap, and one oblique flight at 38 m with a 70° camera angle. The missions captured 400–1000 images each. Fifty-four independent checkpoints were surveyed using a Topcon 105N total station with <2 cm accuracy and used as external reference data for validation. Images were processed in Agisoft Metashape using a structure-from-motion workflow to generate dense point clouds, digital elevation models, and orthophotos. Accuracy was evaluated by computing RMSE in X, Y, Z, and RMSE3D. The 45 m nadir flight achieved the highest accuracy, with an RMSE3D of about 0.13 m, compared with about 0.35 m at 50 m and 0.45 m at 65 m. Planimetric errors in the nadir models were generally 5–10 cm. The 38 m oblique mission improved modelling of vertical structures such as façades and walls, although horizontal precision on flat surfaces decreased to about 14 cm. Overall, a flight altitude near 50 m provided the best compromise between accuracy, coverage, and efficiency. The results recommend combining nadir and oblique imagery with a well-distributed checkpoint network for reliable 3D documentation of war-damaged urban areas.

The agricultural sector is moving from the Agriculture 4.0 model to the Agriculture 5.0 model because of advances in machine learning (ML), big data, and remote sensing. The purpose of this study is to introduce and validate a novel Attention Temporal Neural Network (ATNN) framework for the high-resolution prediction of soil nutrient concentrations, demonstrating its practical value using a real-world Digital Soil Mapping (DSM) dataset from Bezuidenhout Park, Johannesburg. The ATNN framework explicitly models temporal and contextual dependencies in multisource predictors, which include a Digital Elevation Model (DEM), spectral indices from Landsat and Sentinel-2 imagery, and meteorological covariates. The method involves combining the deep feature extraction capabilities of the ATNN with the strength of gradient-boosting regressors, specifically XGBoost, to leverage both architectures for robust tabular regression. The resulting ATNN-XGBoost hybrid model delivered the best performance in the experiments. It significantly reduced prediction error and improved agreement with laboratory measurements, achieving, for example, an RMSE ≈ 1.98ppm, MAPE ≈ 2.81%, CCC ≈ of 0.76, and R2 ≈ of 0.69 for aluminium. This approach materially improved nutrient estimation accuracy over baseline models, including Random Forest (RF), Gradient Boosting (GB), and AdaBoost (ADB). The key contributions to this work are threefold: (1) the development of a compact ATNN architecture tailored for soil nutrient time series and spatial covariates; (2) a practical hybridisation strategy that pairs attention-based feature encoding with XGBoost (XGB); and (3) an empirical demonstration of superior performance on a real South African DSM dataset. These advances support more accurate and timely fertiliser management, offering a scalable path towards smarter, more sustainable precision agriculture systems.

The northeastward expansion of the Tibetan Plateau is caused by the ongoing N–S India-Asia convergence, which leads to the eastward lateral extrusion of fault-bounded blocks. The world-famous Ordos Loess Plateau has an iconic fluvial incision pattern formed on one of these stable cratonic blocks, the Ordos Block. Geomorphologically, the Ordos Loess Plateau on the southern Ordos Block consists of tableland, which relates to the Pliocene Tangxian Planation surface in the western part (Jinghe and North Luohe river areas), and which is more eroded and incised in eastern parts around the Yellow River. In this study, we investigated the effect of tectonic activity on the geomorphic evolution of the Ordos drainage system by field surveys and topographic analysis using digital elevation models. The results show that the drainage systems in the Ordos Loess Plateau are controlled by two factors: (1) the presence of the erosion-resistant fault-bound Weibei Uplift, which represents a barrier separating the southwestern Ordos Loess Plateau from the Weihe Graben; and (2) by the still ongoing surface uplift and erosion due to large increase of the Yellow River catchment since ca. 1.2 Ma. Consequently, the drainage system is in a state of morphological disequilibrium, with drainage basins tilted towards the Liupan Mts. in the west, which overthrusted the western Ordos Block representing a thrust load, which led to tilting of the Tangxian Planation surface. The longitudinal channel profiles of the meandering rivers in the western Ordos Loess Plateau are straight and deviate from usual concave ones because of limited incision due to the fault-bound Weibei Uplift. We suggest that the India-Asia convergence led to the Cenozoic uplift of the Tibetan Plateau and the eastward lateral extrusion of fault-bounded blocks, from which the northernmost was stopped by the cratonic Ordos Block along the Liupan fold-thrust zone. This process led to gentle W-tilting of the southern Ordos Block and is also recorded by GPS data. The formation of unusually straight longitudinal channel profiles is affected by the W-tilting of the Ordos Block and the drainage system reorganized to a principal south-directed flow towards the Weihe Graben in the south. The eastward decreasing elevation of the tableland in front of the Liupan Mts. is related to the indentation of the Liupan Mts. into the Ordos Loess Plateau, which caused the ongoing E–W shortening and ca. N–S extension. Thereby, the eastern Ordos Loess Plateau is progressively incised by and destructed after formation of the Pliocene Tangxian Planation surface. • The cratonic Ordos Block resists the eastward lateral extrusion of the Tibetan Plateau. • The eastward lateral extrusion led to gentle W-tilting of the Ordos Block, which affected the drainage system. • The erosion-resistant Weibei Uplift controls the drainage system and the preservation of the Tangxian Planation surface. • The meandering Jinghe and North Luohe channel profiles are straight deviating from normal ones due to the Weibei Uplift barrier. • The eastern rivers in the southern Ordos Loess Plateau incised deeper and led there to destruction of the Tangxian Planation surface.

Soil erosion and sediment export under rainfed agriculture dominated landscapes: Model-based evidence from the Didessa watershed, southwestern Ethiopia

This study proposes a two-level stacking machine learning approach for predicting rainfall erosivity ( R m ) in Taiwan, providing a flexible alternative to traditional empirical methods. Conventional models rely on limited high-resolution rainfall data and are often region-specific, which limits their accuracy elsewhere. In contrast, the proposed ensemble framework captures complex, non-linear interactions among climatic and topographic variables to improve prediction accuracy. In the first level, six base models were combined, and in the second level, each base model was used as a meta-model to form the ensemble structure. Twenty-eight predictor variables, including climatic and topographic factors, were derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) high-resolution global climate data and a digital elevation model (DEM). To ensure robustness, the modeling procedure was repeated five times using different train–test splits, and final performance metrics were calculated as averages across five datasets. Feature selection using Boruta identified rainfall-related variables as the most important contributors. The ensemble approach significantly improved predictive performance, achieving a root mean square error (RMSE) of 5317 . 92 ± 261 . 23 MJ ⋅ mm ⋅ ha − 1 ⋅ hour − 1 ⋅ year − 1 and a Nash–Sutcliffe efficiency (NSE) of 0 . 67 ± 0 . 02 . The analysis revealed an increasing trend in R m , particularly under higher emission scenarios (SSP3-7.0 and SSP5-8.5), with increases projected in the latter half of the century. These findings highlight the importance of targeted climate mitigation and adaptation strategies for soil conservation and watershed management. This study supports Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land) by improving R m prediction to reduce land degradation and enhance climate resilience. • Two-level stacking ensemble ML framework predicts rainfall erosivity ( R m ) in Taiwan. • Combined six base and meta models with 28 climate and DEM predictors. • Random forest (RF) meta-model achieved best accuracy (NSE = 0.67, RMSE = 5317.92 MJ ⋅ mm ⋅ ha −1 ⋅ hour −1 ⋅ year −1 ). • R m shows increasing trends under high-emission scenarios in late 21st century.

Assessment of ground deformation in Mandalay, Myanmar, using InSAR with Sentinel-1 data after the March 2025 earthquake

This study examines the hydraulic performance and operational vulnerabilities of the Sunkoshi Hydropower Plant in Nepal through two-dimensional (2D) hydraulic modeling. The objectives include analyzing flow dynamics under various operational conditions, optimizing gate management for flood resilience, and maintaining the minimum ecological flow required to ensure environmental needs. Employing historical hydrological data (1965-2012) from the Department of Hydrology and Meteorology, Nepal, and a high-resolution Digital Elevation Model (DEM), the study simulates flood scenarios (10 to 100–year return periods) and operational configuration of the barrage and intake gate with 2D HEC-RAS Modeling. The findings show that during extreme floods, water depths and velocities significantly surpass the design limits, increasing the risks of structural damage and environmental disruption. The findings highlight the necessity of adaptive gate operations, structural reinforcements, and ecological flow considerations to enhance resilience and sustainability. The methodology provides a replicable framework for such hydropower projects in mountainous regions, contributing to sustainable hydropower development.

Assessing the accuracy of different open-source Digital Elevation Models (DEMs) in morphometric analysis of Kanchi River basin

Advances in automated image classification, together with near-global imaging coverage of the Martian surface, have enabled detailed characterization of the spatial distribution of pitted cones, providing a foundation for systematic investigations of their morphological diversity. Concurrently, the continued acquisition of high-resolution Martian imagery over the past decades has allowed photogrammetrically derived digital elevation models (DEMs) to enhance the accessibility, precision, and overall robustness of morphological analyses. However, the number of identified Martian pitted cones causes systematic, manual morphological measurements to be highly labour-intensive and, consequently, impractical for large datasets. To address this challenge, we present a command-line open-source MarsCONE software toolbox, designed to perform automatic cone-morphology detection, and to compute the morphological parameters of Martian pitted cones using High Resolution Imaging Science Experiment (HiRISE)-derived DEMs. The toolbox is built on as a suite of Python tools and Jupyter notebooks, and performs key processing steps including data preparation and transect generation according to user-defined configurations (Generator), signal extraction and landform-point morphology detection (Finder), and cross-transect aggregation with uncertainty handling and data export (Analyzer). This enables MarsCONE to analyze hundreds of pitted cones in a single batch, providing fully reproducible and systematic results within seconds and at minimal computational cost. Consequently, the MarsCONE toolbox improves reproducibility, reduces manual workload, and facilitates large-scale comparative studies of pitted cones across Mars, thereby supporting a more robust understanding of the geological processes governing their formation.

Perito Moreno is one of the largest glaciers in the Southern Patagonian Ice Field. Known until recently for its unique stability against climate change, favoured by the stabilizing effect of a subglacial ridge, it is now undergoing a distinct transition. We processed images from Pléiades-SPOT satellites during 2015–2023 to create digital elevation models and compute their difference, showing an average ice thickness change of −2.7 m a −1 in the terminal area. This rapid downwasting, accelerating since 2020, is likely driven by recent atmospheric warming and severe droughts, causing the glacier front to retreat beyond its pinning point. We assessed the ablation rate at the glacier terminus by solving the mass balance equation as a function of surface elevation change, velocity field, and ice thickness along two transects, one located close to the calving front. Surface velocity was derived from 88 Sentinel-2 images at 10 m resolution, acquired from 2019 to 2024, and processed using Imgraft software. Specifically, we integrated the mass balance equation over the time required for the glacier to travel between the upstream and downstream transects. In contrast to spatial integration, this approach leverages the mean trajectory velocity, effectively smoothing out local instabilities and rendering the results robust against the high uncertainty of pointwise velocities. The ablation rate of −16.4 m w.e. a −1 during 2015–2023 is consistent with recent measurements from ablation stakes, validating the reliability of the proposed time integral framework. • Perito Moreno glacier shows rapid thinning, with an average ice thickness change of −2.7 m yr −1 in the terminal area during 2015–2023. • Time integral of mass balance equation provides consistent estimate of ablation between two transects • Mass balance analysis, supported by Pléiades-SPOT DEMs and Sentinel-2 velocity fields, indicates strong surface lowering at the glacier front. • The ablation rate in the downstream area is −16.4 m w.e. yr −1 , in agreement with previous in situ stake measurements.