Search Results (609)

Glacier inventories are critical for monitoring glacier response to climate change, providing constraints for glacier modeling studies and for assessing the impacts of glacier retreat on ecosystems and human societies. In the Bhutanese Himalaya, an up-to-date glacier inventory and a systematic analysis of decadal-scale glacier changes is lacking. Here, we present three glacier inventories (1976, 1998, and 2024) for this region. Manual mapping of glacier outlines from multi-source satellite imagery and the Copernicus digital elevation model (DEM) are used to derive a glacier inventory with associated topographic attributes. We found that 1871 glaciers existed in this region in 1976, covering an area of 2297.07 ± 117.15 km². By 1998 this number had reduced to 1803 glaciers, covering 2106.99 ± 90.60 km². In 2024, only 1697 glaciers remained, covering 1584.18 ± 36.37 km². A total of 89 (1976–1998) and 435 (1998–2024) glaciers became extinct in the Bhutanese Himalaya during these two time periods, and glacier area decrease accelerated from ~0.38% yr⁻¹ to ~0.95% yr⁻¹. Lake-terminating glaciers retreated almost three times faster (~32.2 m yr⁻¹) than land-terminating (~10.4 m yr⁻¹) glaciers during the observation period. Debris-covered glacier area increased from 112.79 ± 11.50 km² in 1976 to 128.89 ± 10.50 km² in 2024. Glaciers on the South Bhutanese Himalaya (draining into Bhutan) experienced faster glacier retreat than the glaciers of the North Bhutanese Himalaya (draining into the Tibetan Autonomous Region). ERA5-Land reanalysis data show that summer decadal average temperature in this region increased by 0.003 °C yr⁻¹ between 1976 and 1998 and 0.020 °C yr⁻¹ between 1998 and 2024, with the increase in warming rate coinciding with accelerated glacier retreat after 1998. Our updated glacier inventories will be useful for assessments of global sea level change, mountain hazards, and water resources. Full article

Previous studies on single-polarized InSAR-based sub-canopy topography inversion have mainly relied on simplified or empirical models that only consider the volume scattering process. In a boreal forest area, the canopy layer is often discontinuous. In such a case, the radar backscattering echoes are mainly dominated by ground surface and volume scattering processes. However, interferometric scattering models like Random Volume over Ground (RVoG) have been little utilized in the case of single-polarized InSAR. In this study, we propose a novel method for retrieving sub-canopy topography by combining the RVoG model with multi-baseline InSAR data. Prior to the RVoG model inversion, a SAR-based dimidiate pixel model and a coherence-based penetration depth model are introduced to quantify the initial values of the unknown parameters, thereby minimizing the reliance on external vegetation datasets. Building on this, a nonlinear least-squares algorithm is employed. Then, we estimate the scattering phase center height and subsequently derive the sub-canopy topography. Two frames of multi-baseline TanDEM-X co-registered single-look slant-range complex (CoSSC) data (resampled to 10 m × 10 m) over the Krycklan catchment in northern Sweden are used for the inversion. Validation from airborne light detection and ranging (LiDAR) data shows that the root-mean-square error (RMSE) for the two test sites is 3.82 m and 3.47 m, respectively, demonstrating a significant improvement over the InSAR phase-measured digital elevation model (DEM). Furthermore, diverse interferometric baseline geometries and different initial values are identified as key factors influencing retrieval performance. In summary, our work effectively addresses the limitations of the traditional RVoG model and provides an advanced and practical tool for sub-canopy topography mapping in forested areas. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

Flooding is a significant natural hazard in arid regions, particularly in Saudi Arabia, where intense rainfall events pose serious risks to both infrastructure and public safety. Bahrah City, situated between Jeddah and Makkah, has experienced recurrent flooding owing to its topography, rapid urbanization, and inadequate drainage systems. This study aims to develop a comprehensive flood hazard mapping approach for Bahrah City by integrating remote sensing data, Geographic Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA). Key input factors included the Digital Elevation Model (DEM), slope, distance from streams, and land use/land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to assign relative weights to these factors, which were then combined with fuzzy membership values through fuzzy overlay analysis to generate a flood susceptibility map categorized into five levels. According to the AHP analysis, the high-susceptibility zone covers 2.2 km², indicating areas highly vulnerable to flooding, whereas the moderate-susceptibility zone spans 26.1 km², representing areas prone to occasional flooding, but with lower severity. The low-susceptibility zone, covering the largest area (44.7 km ²), corresponds to regions with a lower likelihood of significant flooding. Additionally, hydraulic simulations using the rain-on-grid (RoG) method in HEC-RAS were conducted to validate the hazard assessment by identifying inundation depths. Both the AHP analysis and the RoG flood hazard maps consistently identify the western part of Bahrah City as the high-susceptibility zone, reinforcing the reliability and complementarity of both models. These findings provide critical insights for urban planners and policymakers to improve flood hazard mitigation and strengthen resilience to future flood events. Full article

The Synthetic Aperture Radar Interferometry (InSAR) technique enables researchers to generate Digital Elevation Models (DEMs) from SAR data, which researchers widely apply in multi-temporal analyses, including ground deformation monitoring, susceptibility mapping, and analysis of spatial changes in erosion basins. In this study, we generated two interferometric DEMs from Sentinel-1 (S1, VV polarization) and TerraSAR-X (TSX, HH polarization, ascending orbit) data, processed in SNAP, over a mountainous sector of the central Andes in Chile. We assessed the accuracy of the DEMs against two reference datasets: the SRTM DEM and a high-resolution LiDAR-derived DEM. We selected 150 randomly distributed points across different slope classes to compute statistical metrics, including RMSE and MedAE. Relative to the LiDAR DEM, both sensors yielded rMSE values of approximately 20 m, increasing to 23–24 m when compared with the SRTM DEM. The MedAE, a metric less sensitive to outliers, was 3.97 m for S1 and 3.26 m for TSX with respect to LiDAR, and 7.07 m for S1 and 7.49 m for TSX relative to SRTM. We observed a clear positive correlation between elevation error and terrain slope. In areas with slopes greater than 45°, the MedAE exceeded 14 m relative to the LiDAR DEM and reached ~15 m relative to the SRTM for both S1 and TSX. Full article

Surface soil moisture (SSM) is essential for crop growth, irrigation management, and drought monitoring. However, conventional field-based measurements offer limited spatial and temporal coverage, making it difficult to capture environmental variability at scale. This study introduces a multimodal soil moisture estimation framework that combines synthetic aperture radar (SAR), optical imagery, vegetation indices, digital elevation models (DEM), meteorological data, and spatio-temporal metadata. To strengthen model performance and adaptability, an intermediate fine-tuning strategy is applied to two datasets comprising 10,571 images and 3772 samples. This approach improves generalization and transferability across regions. The framework is evaluated across diverse agro-ecological zones, including farmlands, alpine grasslands, and environmentally fragile areas, and benchmarked against single-modality methods. Results with RMSE 4.5834% and $R^2$ 0.8956 show consistently high accuracy and stability, enabling the production of reliable field-scale soil moisture maps. By addressing the spatial and temporal challenges of soil monitoring, this framework provides essential information for precision irrigation. It supports site-specific water management, promotes efficient water use, and enhances drought resilience at both farm and regional scales. Full article

Rapid urbanization in developing megacities has exacerbated hydrological imbalances, positioning urban flooding as a major environmental and socio-economic challenge of the twenty-first century. This study investigates the spatial relationship between building density, topography, and flood susceptibility in Jakarta, Indonesia—one of the most flood-prone urban regions globally. Employing geospatial analysis and spatial autocorrelation techniques, the research assesses how variations in land-use concentration and elevation influence the spatial clustering of flood vulnerability. The analytical framework integrates multiple spatial datasets, including Digital Elevation Models (DEMs), building footprint densities, and flood hazard maps, within a Geographic Information System (GIS) environment. Spatial statistical measures, specifically Moran’s I and Local Indicators of Spatial Association (LISA), are utilized to quantify and visualize patterns of flood susceptibility. The findings reveal that zones characterized by high building density and low elevation form statistically significant clusters of heightened flood risk, particularly within the southern and eastern subdistricts of Jakarta. The study concludes that incorporating spatially explicit and statistically rigorous methodologies enhances the accuracy of flood-risk assessments and supports evidence-based strategies for sustainable urban development and resilience planning. Full article

We evaluated six global digital elevation DEMs at 1-arc-sec resolution: CopDEM and AW3D30, which are digital surface models (DSMs), and EDTM, GEDTM, FABDEM, and FathomDEM, which are digital terrain models (DTMs). We compared them to reference DTMs created by mean aggregation from 1–2 m lidar-derived DTMs from national mapping agencies, using 1510 approximately 10 × 10 km test tiles from the United States and western Europe. Our criteria used the grids for elevation and derived land surface parameters (LSPs), including characteristics of the difference distributions and the fraction unexplained variance derived from grid correlations. The best DEM depends on the LSP used and the characteristics of the test tile, especially average slope, barrenness, and forest coverage. FathomDEM emerged as the best among the DEMs, with CopDEM the best overall for the DEMs with unrestricted licenses. GEDTM performed poorly. This is especially important for LSPs like curvature measures, which require higher-order partial derivatives for computation, and which should be used very cautiously. Full article

Collecting accurate and reliable beach morphology data is essential for informed coastal management. The beach adjacent to the seawall on North Padre Island, Texas, USA has experienced increased erosion and disrupted natural processes. City ordinance mandates the placement of bollards to restrict vehicular traffic when the beach width from the seawall toe to mean high water (MHW) is less than 45.7 m. To aid the City of Corpus Christi’s understanding of seasonal beach changes, mobile lidar scanning (MLS) surveys with a mapping-grade system were conducted in February, June, September, and November 2023, and post-nourishment in March 2024. Concurrent uncrewed aircraft system (UAS) photogrammetry surveys were performed in February and November 2023, and March 2024 to aid beach monitoring analysis and for comparative assessment to the MLS data. MLS-derived digital elevation models (DEMs) were used to evaluate seasonal geomorphology, including beach slope, width, shoreline position, and volume change. Because MHW was submerged during all surveys, highest astronomical tide (HAT) was used for shoreline analyses. HAT-based results indicated that bollards should be placed from approximately 390 to 560 m from the northern end of the seawall, varying seasonally. The March 2024 post-nourishment survey showed 102,462 m³ of sand was placed on the beach, extending the shoreline by more than 40 m in some locations. UAS photogrammetry-derived DEMs were compared to the MLS-derived DEMs, revealing mean HAT position differences of 0.02 m in February 2023 and 0.98 m in November 2023. Elevation and volume assessments showed variability between the MLS and UAS-SfM DEMs, with neither indicating consistently higher or lower values. Full article

A combined Differential InSAR (D-InSAR) and Digital Elevation Model (DEM)-based analysis revealed that earthquake-triggered landslides significantly altered river morphology and intensified flooding in the Kota Belud catchment, Sabah, Malaysia. This 1386 km² catchment, home to about 120,000 people, has experienced a marked rise in flood events following the 4 June 2015 and 8 March 2018 earthquakes. Multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data and a 30 m Shuttle Radar Topography Mission (SRTM) DEM, complemented by river network information from HydroBASINS, were integrated to map sediment redistribution and model flood extent. Upstream zones exhibited extensive coseismic landslides and pronounced geomorphic disruption. Interferometric analysis showed that coherence was well preserved over stable terrain but rapidly degraded in vegetated and steep areas. Sediment aggradation, interpreted qualitatively from patterns of coherence loss and increased backscatter intensity, highlights slope failure initiation zones and depositional build-up along channels. Conversely, downstream, similar sedimentary adjustments were detected immediately upstream of areas with repeated flood incidents. Between 2015 and 2018, flood occurrences increased over fivefold, and after 2018, they increased by more than thirteenfold relative to pre-2015 conditions. DEM-based inundation simulations demonstrated that channel shallowing substantially reduced conveyance capacity and expanded flood extent. Collectively, these results confirm that earthquake-induced landslides have contributed to reshaping the geomorphology and amplified flooding in the area. Full article

As direct geomorphic evidence and records of earthquakes on the surface, coseismic surface ruptures have long been a key focus in earthquake research. However, compared with strike-slip and normal faults, studies on reverse-fault surface ruptures remain relatively scarce. In this study, surface rupture characteristics of the most recent earthquake on the Kumysh thrust fault in eastern Tianshan were investigated using high-resolution topographic data, including 0.5 m- and 5 cm-resolution Digital Elevation Models (DEMs) generated from the WorldView-2 satellite stereo image pairs and Unmanned Aerial Vehicle (UAV) images, respectively. We carefully mapped the spatial geometry of the surface rupture and measured 120 vertical displacements along the rupture strike. Using the moving-window method and statistical analysis, both moving-mean and moving-maximum coseismic displacement curves were obtained for the entire rupture zone. Results show that the most recent rupture on the Kumysh Fault extends ~25 km with an overall NWW strike, exhibits complex spatial geometry, and can be subdivided into five secondary segments, which are discontinuously distributed in arcuate shapes across both piedmont alluvial fans and mountain fronts. Reverse fault scarps dominate the rupture pattern. The along-strike coseismic displacements generally form three asymmetric triangles, with an average displacement of 0.9–1.1 m and a maximum displacement of 2.8–3.2 m, yielding an estimated earthquake magnitude of M_w 6.6–6.7. This study not only highlights the strong potential of high-resolution remote sensing data for investigating surface earthquake ruptures, but also provides an additional example to the relatively underexplored reverse-fault surface ruptures. Full article

The Caucasus Mountains are recognized as a global center of biodiversity but currently face significant risks of degradation due to intensified economic development and the effects of climate change. Forest inventory and mapping are essential for biodiversity conservation in the Caucasus region. Geospatial modeling is a common method of thematic mapping, but its reliability depends heavily on the initial classification of reference data used for model training. Modern vegetation science features various classification approaches, most of which were developed independently of digital mapping practices and are rarely assessed for their suitability in geospatial modeling. To fill this gap, we classified the same dataset of vegetation relevés from mixed and broadleaf forests in the northwestern Caucasus using two approaches, based on floristic and dominant concepts, and compared the predictive performance of geospatial models trained on these datasets. We considered multiple types of geospatial variables, including optical satellite imagery, a digital elevation model (DEM), and bioclimatic and soil features, to evaluate their informativeness for spatial differentiation of the resulting forest types and to identify optimal variable combinations for modeling via multistage feature selection. We trained several models using different variable sets and machine learning methods for both classifications and evaluated their accuracy via nested cross-validation. The forest types produced by the two approaches scarcely matched, and the selected variable sets for model training differed accordingly. Unexpectedly, bioclimatic and soil variables were more effective than DEM- and satellite-derived variables, despite their coarser spatial resolution. Floristic-based geospatial models outperformed dominant-based models in terms of forest-type separability and predictive accuracy. Therefore, a floristic classification approach may be preferable for forests with complex species composition, both ecologically and in terms of the reliability of geospatial modeling and the derived mapping results. Full article

Quarries represent critical sites for raw material extraction, for which regular monitoring and mine surveying documentation, along with its updating, is essential to ensuring safety, environmental protection, and effective management of the mining process. This article aims to evaluate the modern approach to quarry surveying and the creation of a base mining map using advanced laser scanning methods, such as terrestrial laser scanning (TLS) and simultaneous localization and mapping (SLAM)-based mobile laser scanning (MLS). Particular attention is given to the analysis of noise generated using TLS and SLAM-based MLS methods. An analysis of mutual differences between point clouds is presented to compare the spatial accuracy of the point clouds obtained using MLS technology against those from the reference TLS method on both horizontally and vertically oriented test areas. To assess the quality and usability of data obtained using the TLS and MLS methods, a selected section of the mining wall was analyzed based on the distance between points (Cloud-to-Cloud analysis), cross-section analysis, and volume calculations based on 3D mesh models generated from stage edges and point clouds. The findings offer valuable insights into the effective use of each method in quarry surveying, contributing to the development of innovative approaches to spatial data collection as a base for creating Digital Twins of quarries. The article also evaluates the efficiency of both measurement approaches in terms of accuracy, measurement speed, and practical applicability in mining practices. The results show that the point cloud obtained by the TLS Leica RTC360 device, compared to that by the MLS method using the FARO Orbis device (FARO Technologies, Inc., Lakemary, FL, USA), achieves better values in terms of average noise level, standard deviation, interval of highest point density, and RMSD (Root Mean Square Deviation) in test areas. Our conclusions highlight the high potential of laser scanning for the modernization of mining documentation and the improvement of surveying processes in the smart mining industry, particularly for updating Digital Elevation Models (DEMs), Digital Terrain Models (DTMs), Digital Surface Models (DSMs), and other 3D models of quarries for the creation of their Digital Twins. Full article

Floods increasingly threaten semiarid regions, yet their long-term soil ecological impacts remain underdocumented. This study quantifies the hydrologic change and flood-induced soil transformation on the Zhabay River floodplain (Akmola, Kazakhstan) using integrated field, laboratory, and remote sensing data. Gauge records (2012–2024) were analyzed; inundation was mapped from a 0.30 m DEM (Digital Elevation Model) merging SRTM (Shuttle Radar Topography Mission), Landsat 8/Sentinel 2, and UAV (Unmanned Aerial Vehicle) photogrammetry (NDWI (Normalized Difference Water Index) > 0.28) and validated with 54 in situ depths (MAE (Mean Absolute Error) 0.17 m). Soil samples collected before and after floods were analyzed for texture, bulk density, pH, Eh, macronutrients, and heavy metals. Annual maxima increased by 0.08 m yr⁻¹, while extreme floods became more frequent. Thresholds of ≥0.5 m depth and >7 days duration marked compaction onset, whereas >1 m and ≥12 days produced maximum organic carbon loss and Zn/Ni enrichment. The combination of high-resolution DEMs, ROC (Receiver Operating Characteristic) analysis, and soil microbial monitoring provides new operational indicators of soil degradation for Central Asian steppe floodplains. Findings contribute to SDG 13 (Climate Action) and SDG 15 (Life on Land) by linking flood resilience assessment with sustainable land-use planning. Full article

The creation of simulation-ready digital twins for real-world simulations is hindered by two key challenges: the lack of widely consistent, application-ready open access terrain data and the inadequacy of conventional evaluation metrics to predict practical, in-engine performance. This paper addresses these challenges by presenting an end-to-end, open-data pipeline that generates simulation-ready terrain and procedural 3D objects for the Unity engine. A central finding of this work is that the architecturally advanced Swin2SR transformer exhibits severe statistical instability when applied to Digital Elevation Model (DEM) data. We analyze this instability and introduce a lightweight, computationally efficient stabilization technique adapted from climate science—quantile mapping (qmap)—as a diagnostic remedy which restores the model’s physical plausibility without retraining. To overcome the limitations of pixel-based metrics, we validate our pipeline using a three-axis evaluation framework that integrates data-level self-consistency with application-centric usability metrics measured directly within Unity. Experimental results demonstrate that qmap stabilization dramatically reduces Swin2SR’s large error (a 45% reduction in macro RMSE from 47.4 m to 26.1 m). The complete pipeline, using a robust SwinIR model, delivers excellent in-engine performance, achieving a median object grounding error of 0.30 m and real-time frame rates (≈100 FPS). This study provides a reproducible workflow and underscores a crucial insight for applying AI in scientific domains: domain-specific stabilization and application-centric evaluation are indispensable for the reliable deployment of large-scale vision models. Full article