Search Results (1,047)

Traditional satellite-derived bathymetry (SDB) often suffers from systematic optical path distortions due to the neglect of seafloor slope effects, leading to significant accuracy degradation in high-gradient coastal areas. This study proposes a Slope-Aware Physics-Informed Neural Network (SA-PINN) framework that synergistically utilizes ICESat-2 bathymetric photons and Sentinel-2 multispectral imagery. The core innovation involves a slope-aware operator, integrated into the radiative transfer-based physics loss function, which explicitly rectifies directional optical path deviations induced by seafloor inclination. By fusing physical mechanisms with data-driven features, the model utilizes a seven-dimensional feature space comprising four spectral bands, two directional slope components, and prior depth. Applications at Culebra, Maui, and Molokai demonstrate that SA-PINN significantly outperforms the Stumpf model, Random Forest, and standard CNNs, achieving root mean square errors (RMSE) of 1.36 m, 2.91 m, and 1.34 m, respectively. Ablation studies confirm that SA-PINN reduces RMSE by up to 37% compared to CNN in complex regions with slopes exceeding 10°, ensuring superior physical consistency and spatial continuity. This research provides a robust, in situ-free automated solution for high-resolution bathymetric mapping in remote and steep coastal environments globally. Full article

The unchecked expansion of tea plantations onto steep, forest-adjacent slopes in subtropical mountains engenders a conflict between agricultural productivity and ecosystem integrity, particularly by exacerbating habitat fragmentation and soil erosion. While precise monitoring is essential to navigate this trade-off for sustainable management, accurate inventorying remains a challenge due to the plantations’ strong phenological variability, heterogeneous canopy structures, and high spectral confusion with surrounding vegetation. This study proposes a feature-optimized deep learning framework for mapping and characterizing tea plantations in complex landscapes, using Xinyang City, China, as a study area. The framework integrates multi-temporal Sentinel-1/2 observations with a sequential Jeffries-Matusita (JM)-Pearson feature filtering strategy. This approach effectively condenses a 132-variable high-dimensional pool (including optical spectra, vegetation indices, textures, and SAR polarimetry) into a compact 28-feature subset (a 78.8% reduction), preserving critical phenological and structural cues while minimizing redundancy. These optimized predictors drive a hybrid VGG16–UNet++ segmentation network, which couples transfer-learning-based semantic encoding with detail-preserving dense skip fusion. Extensive experiments across 18 model–feature configurations demonstrate that the optimal setting achieves an Overall Accuracy of 97.82%, an F1-score of 0.9093, and a mean IoU of 0.7968. Notably, the method significantly reduces misclassification in rugged, cloud-prone terrain, yielding a User’s Accuracy of 91.14% for tea. Based on the generated wall-to-wall map, we derived two decision-support indicators: multi-threshold steep-slope exposure and a normalized tea–forest interface density. This framework provides actionable, high-precision spatial products to support slope-based zoning, ecological restoration, and sustainable management in fragile mountain agroforestry systems. Full article

Tailings management presents a critical challenge for the mining industry, particularly in mountainous regions with high seismicity and steep slopes. This article presents the development and design criteria for dry stacking of filtered tailings as a sustainable and safe alternative to conventional slurry tailings storage facilities (TSFs). The study focuses on the extreme conditions of a mountainous location characterized by complex topography with 10% slopes, space constraints, and significant seismic activity defined by a peak ground acceleration (PGA) of 0.3 g. The design methodology, which incorporates layered compaction of the filtered tailings to achieve a geotechnically stable structure, is detailed for a filtered TSF consisting of 7 terraces, each 10 m high, reaching a total height of 70 m. This approach minimizes the risk of liquefaction and prepares the filtered tailings surface for progressive closure, with unit operating costs (OPEX) of 2.5 USD/t. The results of the physical stability analysis confirm the viability of this solution: pseudo-static stability analysis yielded a safety factor of 1.22, demonstrating a significant reduction in water consumption and potential environmental impact. It is concluded that the dry disposal of filtered tailings is a technically robust option for tailings management in extreme mountainous environments, offering greater long-term safety guarantees and facilitating landscape integration, thus setting a precedent for mining projects in similar geographies. Full article

Urban rooftop photovoltaic systems represent a substantial yet still underutilized renewable energy resource, particularly in high-density residential environments. Accurate large-scale assessment of rooftop solar potential, however, remains challenging due to the complex geometry of urban morphology and the limited availability of high-resolution geospatial data. This study presents a large-scale methodological framework for estimating the theoretical photovoltaic potential of urban rooftop spaces using Unmanned Aerial System (UAS)-based digital photogrammetry and GIS-based spatial analysis. The approach integrates centimeter-resolution Digital Surface Models (DSMs) and orthophotos derived from fixed-wing UAS surveys with detailed rooftop vectorization and solar radiation modeling implemented in a GIS environment. The methodology accounts for rooftop geometry, surface orientation, slope, shading effects, and rooftop-mounted obstacles. The methodology consists of data collection of high-resolution RGB imagery suitable for detailed three-dimensional reconstruction. The images are captured with a UAS equipped with a S.O.D.A. 3D photogrammetric camera, creating a dense, georeferenced three-dimensional point cloud based on UAS imagery. Based on the point cloud, a high-resolution Digital Surface Model (DSM) was produced. Rooftop boundaries and rooftop-mounted structures were digitized on the basis of an orthophoto created from UAS imagery. The analysis workflow consists of solar modeling using ArcGIS Pro, including calculating the solar radiation. The next methodological step is to filter low radiation rooftops, steep slopes, and northern-oriented rooftops. Finally, we calculate the potential electricity production. The framework was applied to high-density residential districts in Sofia, Bulgaria, dominated by prefabricated panel buildings with predominantly flat rooftops. Drone applications in such studies are typically restricted to modeling individual roofs, which severely limits their scalability for district-wide evaluations. To overcome this, the study employs a specialized fixed-wing UAS uniquely certified for legal operations over densely populated urban environments. This platform rapidly maps large territories, ensuring consistent lighting and shading conditions that significantly enhance the accuracy of subsequent rooftop digitization. Furthermore, the resulting centimeter-level precision enables the exact vectorization of micro-rooftop obstacles. Capturing these intricate details is a critical innovation that effectively prevents the overestimation of solar energy potential commonly observed in conventional large-scale models. Solar radiation was modeled at the pixel level for a full annual cycle and filtered using photovoltaic suitability criteria, including minimum annual radiation thresholds, slope, and aspect constraints. Theoretical electricity production was subsequently estimated using zonal statistics and system performance parameters representative of contemporary photovoltaic installations. The results indicate a total theoretical annual electricity potential of approximately 76.7 GWh for the analyzed rooftop spaces, with an average production of about 34 MWh per rooftop and pronounced spatial variability driven by rooftop geometry and exposure conditions. The findings demonstrate the significant renewable energy potential embedded in existing urban rooftop infrastructure and highlight the applicability of UAS-based photogrammetry for high-resolution, large-area solar potential assessments. The proposed framework provides actionable information for urban energy planning, municipal solar cadaster development, and the strategic integration of photovoltaic systems into dense urban environments, particularly in regions lacking open-access high-resolution geospatial datasets. Full article

Sediment connectivity is a key indicator of whether eroded sediment can be efficiently transported within a catchment. Landslides are a major form of rainfall-induced erosion on the steep slopes of the Loess Plateau and contribute substantially to overall catchment sediment yield. However, evaluating the connectivity of landslide-derived sediment and its implications for sediment transport risk remains challenging. Therefore, field investigations were conducted in three watersheds (R1, R2, and R3) on the Loess Plateau to examine landslides triggered by rainstorms. We analyzed the characteristics of landslide erosion and its influencing factors, applied graph theory to investigate sediment connectivity after landslides occurred, and assessed the risk of sediment transport to the catchment outlet. The results showed that the landslide number densities in the catchments R1, R2, and R3 were 9, 155, and 214 km⁻², respectively. The average erosion intensities were 25,153, 53,074, and 172,153 t km⁻², respectively. The network analyses indicated that the locations of landslides within the catchments were primarily concentrated in areas with high transport networks and high sediment accessibility to the catchment outlets. The sediment connectivity index further showed that 59%, 43%, and 51% of landslides in the three watersheds, respectively, were at high risk of delivering sediment to the catchment outlet. Accordingly, measures such as slope drainage and gully dam construction may help reduce both landslide occurrence and sediment transport. These findings provide new insights into the transport risk of eroded sediment from a connectivity perspective, identify hotspot areas of sediment connectivity and landslide erosion, and support the targeted prevention and control of catchment erosion. Full article

The Yeongnam wildfires in March 2025 destroyed over 40 temple halls across five Buddhist monasteries in South Korea, exposing a critical gap in wildfire management for mountain-sited cultural heritage: the existing approaches rely on static hazard maps and reactive suppression, lacking real-time terrain-aware prediction and proactive resource deployment. This study proposes a Digital Twin framework coupling high-resolution wildfire simulation with adaptive water-mist optimization to address this gap. Bogwangsa Temple (est. 949 CE, ~315 m elevation, Cheonmasan Mountain, Namyangju) serves as the case study, selected for its representative vulnerability—dense Pinus densiflora forests on steep western slopes forming a continuous fire corridor, limited vehicular access, and proximity to recent large-scale fire events. A modified Rothermel model on a 1 m cellular-automata grid, driven by a 1 m DEM, Korea Forest Service fuel data, and local weather records, simulates five scenarios from normal spring to extreme dry-wind conditions through Monte Carlo ensembles. Binary integer optimization selects the minimum-cost nozzle configuration, keeping the fire-arrival probability at four heritage structures below a safety threshold via pre-emptive activation. The adaptive deployment reduces the mean fire-arrival probability by approximately 80% compared with static sprinklers while substantially lowering water consumption. Sensitivity analyses confirm that 1 m DEM resolution captures micro-terrain features that are critical to accurate spread prediction that are lost at coarser resolutions. The modular, transferable framework contributes to SDG 11 (Sustainable Cities and Communities, Target 11.4) and SDG 13 (Climate Action). Full article

Submarine canyon-channel systems play a critical role as potential conduits for warm-water upwelling around Antarctica, potentially influencing ice-sheet stability. Integrating multibeam bathymetry, seismic profiles, and morphometric analysis, this study identifies 29 canyon-channel systems along the Adélie Land margin and reveals clear morphological contrasts between the Adélie Depression and the Adélie Bank. Systems in the Depression are elongated, slightly sinuous, and dendritic, with downstream increases in width-to-depth ratio, whereas those on the Bank are shorter, isolated, and single-branched, with irregular along-thalweg variations. Mann–Whitney U tests show significant differences in sinuosity and thalweg gradient (p < 0.01). These contrasts reflect the combined effects of shelf-slope topography, sediment supply, and ice-sheet dynamics. In the Depression, steep slopes, focused glacial sediment input from the Wilkes Subglacial Basin, and associated progradational wedges and mass transport deposits promote mass failures and turbidity-current incision. Strong correlations among canyon-channel length, width, and depth indicate coherent scaling under concentrated sediment supply. In contrast, gentler slopes and lower sediment input on the Bank produce simpler systems. These results highlight how glaciated-margin canyon morphology records coupled sedimentary and ice-sheet–ocean processes. Full article

Mechanized timber harvesting on steep slopes causes soil disturbance; however, comprehensive post-harvest assessment remains challenging because terrain complexity and safety constraints render traditional field-based methods labor-intensive, spatially limited, and difficult to implement systematically. In this study, we developed and evaluated a convolutional neural network-based semantic segmentation model for detecting soil disturbances using high-resolution unmanned aerial vehicle (UAV) imagery in a steep-slope harvested area (2.50 ha, mean slope of 53.4%) in Republic of Korea. A U-Net semantic segmentation model was trained on manually annotated orthomosaic tiles incorporating RGB and digital elevation model (DEM) inputs. Ensemble predictions at an optimized threshold of 0.65 achieved Intersection over Union (IoU) of 0.55 and F1-score of 0.71. Although moderate, these values reflect the inherently challenging conditions of steep-slope forest terrain compared to similar studies conducted under gentler terrain. DEM-derived depth estimation enabled severity classification of the detected disturbances, with light disturbances predominating. Field validation using 38 pinboard measurements demonstrated reliable spatial detection (ρ = 0.567, RMSE = 6.45 cm). This approach provides an effective alternative to traditional monitoring practices in mountainous forests, where systematic trail planning is impractical, and may support evidence-based assessment of harvesting impacts for sustainable forest management. Full article

Fractures as critical discontinuous structural planes in rock masses, directly govern their stability and serve as the core controlling factor in rock mechanics engineering. Existing deep learning models for fracture extraction face persistent challenges, including imbalanced integration of deep and shallow features, limited suppression of background noise, inadequate multi-scale feature representation, and large parameter sizes—making it difficult to strike a balance between detection accuracy and deployment efficiency. Focusing on the Wanshanshan quarry in Yunnan, this study first constructs a high-precision digital model using close-range photogrammetry and 3D real-scene reconstruction. A lightweight yet high-accuracy intelligent detection method, termed MSEM-Deeplabv3+, is then proposed for rock mass fracture extraction. The model adopts lightweight MobileNetV2 as the backbone network, incorporating inverted residual modules and depthwise separable convolutions, resulting in a parameter size of only 6.02 MB and FLOPs of 30.170 G—substantially reducing computational overhead. Furthermore, the proposed MAGF (Multi-Scale Attention Gated Fusion) and SCSA (Spatial-Channel Synergistic Attention) modules are integrated to enhance the representation of fracture details and semantic consistency while effectively suppressing multi-source and multi-scale background interference. Experimental results demonstrate that the proposed model achieves an mPA of 89.69%, mIoU of 83.71%, F1-Score of 90.41%, and Kappa coefficient of 80.81%, outperforming the classic Deeplabv3+ model by 5.81%, 6.18%, 4.53%, and 9.2%, respectively. It also significantly surpasses benchmark models such as U-Net and HRNet. The method accurately captures fine and continuous fracture details, preserves the spatial distribution of long-range continuous fractures, and maintains robust performance on the CFD cross-scene dataset, showcasing strong adaptability and generalization capability. This approach effectively mitigates the risks associated with manual high-altitude inspections and provides a lightweight, high-precision, non-contact intelligent solution for fracture detection in high-steep rock slopes. Full article

The Gulf of Taranto (Ionian Sea) is a key transitional sector between the Southern Apennines collisional belt and the Calabrian Arc system, where the expression of Pleistocene–Holocene deformation in the shallow stratigraphic record remains debated. This study focuses on the Taranto Canyon area, the main morphologic feature of the northeastern Gulf of Taranto slope. We integrate high-resolution multibeam bathymetry (10 m grid) with Sparker seismic profiles to (i) define the shallow seismo-stratigraphic framework and (ii) document spatial relationships between shallow discontinuities, morphostructural lineaments, and submarine channel network organization. A simplified tie to the Livia 001 well constrains the subdivision of the shallow succession into four seismic units: the late Pleistocene–Holocene unit (PtH), the Santerno Formation (SNT), the Calcarenite di Gravina (GRA), and the Cupello Limestones (CPL). The PtH interval shows the strongest lateral variability and includes widespread acoustically disturbed bodies and recurrent sub-vertical fluid escape acoustic anomalies. Steep discontinuities producing reflector terminations, minor vertical separation, and localized bending affect PtH and, locally, SNT, with normal fault geometries prevailing where resolvable. Bathymetric mapping reveals multiple lineament families and preferred channel orientations that persist across higher Strahler orders, supporting a structurally conditioned template that guides seafloor morphology, sediment routing, and canyon–slope evolution in the northeastern Gulf of Taranto. Full article

Soil compaction and reduced infiltration capacity are critical constraints limiting soil physical quality and hydraulic functioning in semi-arid vineyard systems subjected to repeated machinery traffic. This study aimed to develop and evaluate a geometry-optimized strip tillage tool designed to improve structural functionality within the compacted root zone while minimizing inter-row disturbance. A U-shaped working body configuration, consisting of two oppositely inclined shanks and a central chisel, was theoretically substantiated and optimized using multifactor analysis. Field experiments were conducted to assess changes in penetration resistance, bulk density, and infiltration rate within the 20–40 cm soil layer under semi-arid conditions. The optimized geometry significantly reduced penetration resistance and bulk density in the trafficked strip, indicating alleviation of mechanical impedance and improved root-relevant physical conditions. Infiltration capacity increased after treatment, indicating enhanced hydraulic continuity within the root zone. Unlike full-width subsoiling, the localized strip intervention preserved inter-row soil stability and limited unnecessary disturbance, which is consistent with conservation-oriented soil management. The results indicate that geometry-optimized strip tillage is associated with improved soil physical quality and hydraulic function within compacted vineyard strips. The operational applicability of the developed implement may also depend on vineyard layout and terrain conditions. The prototype tool was tested under conditions representative of vineyards with standard row spacing and relatively moderate slopes typical for the experimental site. In vineyards with very narrow row spacing, steep slopes, or highly heterogeneous soil conditions, adjustments in working width, shank spacing, or tractor–implement configuration may be required. Future studies should therefore investigate the performance of the optimized geometry under contrasting vineyard configurations, including steep hillside vineyards and high-density planting systems. By linking implement design to quantitative soil structural and hydraulic indicators, this study contributes to the development of vineyard soil management practices for semi-arid perennial cropping systems. Full article

Chieng Hac in northern Vietnam is expanding maize cultivation, intensifying water competition and soil erosion. This study mapped regional water balance and erosion using remote sensing and GISs by coupling the Thornthwaite–Mather (TM) water balance model with the Revised Universal Soil Loss Equation (RUSLE) at 12.5 m resolution. Land cover was classified into maize, tree crops, paddy, forest, and other types using Random Forest. The TM model used 2021 precipitation and temperature measurements to estimate evapotranspiration, surplus, and deficit, while the RUSLE quantified soil loss. Two scenarios were evaluated: a baseline reflecting existing land use and an adjusted case applying strip cropping on 10–20° maize slopes and converting maize to tree crops on slopes > 20°. Tree crop conversion increased evapotranspiration and prolonged seasonal deficits relative to maize, increasing water deficit from 1013.6 to 1022.2 mm/year. In contrast, the interventions reduced mean soil loss from 15.52 to 11.51 t/ha/year, with the largest decline in the 5–25 t/ha/year class. Residual hotspots persisted on steep slopes and near drainage lines. The integrated framework highlights trade-offs between erosion control and seasonal water availability, supporting slope-based land-use planning in upland agricultural systems. These findings offer guidance for slope-based land-use planning by indicating that intervention priorities should vary depending on slope conditions and local water availability. Full article

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

Unmanned Aerial Vehicle (UAV) imagery has become an important tool for erosion monitoring, but little is known about its application in Mediterranean agricultural systems such as vineyards and olive groves. In this study, drone flights were conducted in vineyards and olive groves where mulch and biochar treatments had been applied. Digital terrain models (DTMs) and orthomosaics were constructed using a photogrammetry workflow, and model error was determined via global positioning system (GPS) transects. Erosion was assessed using Digital elevation models of Difference (DoD) and compared with field-based erosion plot measurements. Explanatory variables for erosion (soil roughness, slope length, steepness, vegetation cover) were derived from DTMs and orthomosaics and were evaluated in a multiple linear regression model. Although direct measurement of erosion from the DoDs was difficult, this was primarily influenced by the unexpectedly low erosion rates during the study period, and the high root mean square error (RMSE) of the DTMs. Significant differences in DTM-derived variables were found between study areas, and especially between areas with organic and integrated management, even though treatments showed similar patterns. The multiple linear regression model demonstrated strong explanatory power, accounting for a large part of the variation in measured erosion using the UAV-derived variables (R² = 0.81). Slope and slope length were the most important predictors of erosion together with the interaction between these two variables. The results suggest that soil erosion in the study areas was mostly determined by topographic and management factors, rather than the applied treatments. This study highlights the value of UAV imagery in advancing the understanding of erosion processes in Mediterranean agricultural systems, while also identifying the challenge of accurately measuring erosion from DoDs under conditions of low erosion rates. Full article

Against the backdrop of a gradual shift in the focus of cultural heritage (CH) conservation and utilization toward the integrated system formed by CH and its surrounding environment as well as regional systems, research on the coordinated protection of nature and culture to promote regional high-quality development has become a new trend. However, systematic summaries of the spatial–temporal distribution of CH in cross-regional typical geomorphic units at the river basin scale and their correlation with the natural environment remain insufficient. This study takes 387 Cultural Relics Protection Units in the Three Gorges of the Yangtze River (the Three Gorges region) as the research objects, utilizing GIS spatial analysis technology to examine the impact of the natural environment on CH across different periods and types. The theory of time-depth is introduced to reveal the layering mechanisms and underlying cultural logics. Coupled with the Minimum Cumulative Resistance (MCR) model, this study constructs a cultural corridor network and proposes spatial planning strategies. The findings are as follows: (1) The absolute core area for the distribution of CH across all periods remains the gentle slope zone near the river, characterized by elevations below 500 m, slopes within 25°, and distances from water systems within 1 km. However, the adaptive scope exhibits a diachronic evolution from core accumulation to peripheral expansion. (2) Different types of CH exhibited distinct natural adaptation strategies and vertical accumulation. Settlement Sites in the Before Qin Dynasty Period formed the foundational layer of survival rationality, while Ordinary Tombs in the Qin–Yuan Dynasty Period reinforced sedentism. Ancient Architecture in the Ming–Qing Dynasty Period underwent a transformation from “adapting to nature” to “reconstructing nature” as a product of environmental construction. Modern and Contemporary Significant Historical Sites and Representative Buildings in the After Qing Dynasty Period are characterized by a ruptured insertion on steep slopes, inscribing revolutionary memory onto space. The main stream of the Yangtze River serves as the core area of continuous deposition, while the extremely steep slopes form a distinctive stratigraphic accumulation of precipitous terrain. (3) Based on these distribution patterns, the study further proposes a spatial framework for CH called “One Corridor, Three Wings.” This framework uses the main stream of the Yangtze River as the spatial–temporal axis, linking the four core overlapping nodes of Fengjie, Wushan, Badong, and Xiling, supplemented by three secondary cultural clusters of the red heritage sites in southern Badong, the ancient town along the Daning River in Wushan, and the fortress sites in the Xiling–Yiling area. This research not only reveals the evolutionary path of CH in the Three Gorges region, but also provides a scientific basis for the systematic conservation and differentiated utilization of regional CH. Furthermore, it serves as a planning foundation and strategic reference for planning the Yangtze River National Cultural Park, as well as for the integrated preservation and utilization of river basin CH and linear CH with the aim of coordinated natural and cultural conservation. Full article