Search Results (264)

Background/Objectives: Mesenteric panniculitis (MP) is a chronic inflammatory and sclerosing disorder of the mesenteric fat. This study evaluated the occurrence and MRI appearance of MP in patients with chronic inflammatory bowel disease and chronic inflammatory bowel conditions. Methods: In this retrospective single-center study, 312 individuals underwent standardized 3-T MRI Sellink examinations, including 252 patients with clinically confirmed chronic inflammatory bowel disease or chronic inflammatory bowel conditions and 60 control patients without intestinal inflammation. MP was diagnosed when at least three of five characteristic MRI findings were present: increased mesenteric signal intensity on fat-saturated fluid-sensitive T2 sequences, fat ring sign, pseudocapsule, embedded micronodules, and displacement of bowel loops. Group differences were analyzed using contingency table analysis with Monte Carlo exact testing; Pearson’s chi-square test and Holm-adjusted pairwise post hoc comparisons were additionally performed. A p-value < 0.05 was considered statistically significant. Results: MRI signs of MP were present in 221/312 patients (70.8%) overall, including 220/252 patients (87.3%) in the disease cohort and 1/60 patients (1.7%) in the control group. Among patients with MP, the underlying diseases/conditions were Crohn’s disease (104/220, 47.3%), sigmoid diverticulitis (88/220, 40.0%), and ulcerative colitis (28/220, 12.7%). The overall distribution of MP extent differed significantly among diagnostic groups (Monte Carlo exact test: p = 0.030), although adjusted pairwise comparisons were not statistically significant. Topographically, MP showed a clear predominance in the left upper quadrant. Conclusions: MRI-defined features consistent with mesenteric panniculitis are highly prevalent in this selected cohort of patients with chronic intestinal inflammatory conditions when standardized criteria are applied. These findings suggest that MP-like mesenteric changes may represent a common imaging correlate of chronic intestinal inflammation rather than a rare incidental finding. MRI enables consistent detection and topographic assessment of MP. Full article

Flood monitoring in dry tropical basins, such as the Tumbes River (Peru), faces critical challenges due to persistent cloud cover that restricts the operability of optical sensors during extreme events, coupled with the operational gap between satellite products and conventional hydrological monitoring. To overcome these limitations, this research developed a comprehensive methodological framework in Google Earth Engine that unifies automated image thresholding and Sentinel-1 SAR time series analysis for flood detection and the estimation of early warning thresholds. The Bmax Otsu and Edge Otsu algorithms were evaluated, previously calibrated using high-resolution imagery (PlanetScope) as reference data, topographically constrained by the HAND (Height Above the Nearest Drainage) model, and validated against established change detection algorithms. The analysis of seven hydrological events between 2017 and 2024 confirmed the statistical superiority of Bmax Otsu; although both methods achieved high overall accuracy (Bmax 95.8% versus Edge 95.7%), Bmax Otsu outperformed Edge Otsu in spatial consistency (Kappa 66.1% vs. 63.7%; IoU 45.6% vs. 45.0%). Based on this, a time series analysis was applied to discriminate permanent water bodies and isolate flood dynamics. Subsequently, the functional discharge–impact response was evaluated by linking the instantaneous flood extent captured by the SAR overpasses to their corresponding peak discharges. Validated against official INDECI damage reports, it was determined that significant impacts begin at an activation threshold of 743.49 m³/s (151 flooded ha, 157 affected inhabitants) and scale linearly up to extreme peak events of 1629.02 m³/s, compromising 1234 agricultural ha and 749 inhabitants. This methodology provides a validated, low-cost tool to translate SAR observations into critical thresholds for early warning systems in data-scarce regions. Full article

The Greater Caucasus is one of the most extensively glacierized mountain systems in mid-latitude Eurasia and has experienced substantial glacier retreat in recent decades. Continuous monitoring using high-resolution satellite observations is therefore essential for accurately quantifying ongoing and future changes. In this study, we present a new glacier inventory for 2025 derived from high-resolution (3 m) PlanetScope satellite imagery combined with topographic information from the 30 m Advanced Land Observing Satellite (ALOS) Global Digital Surface Model (2006–2011). A total of 101 cloud-free PlanetScope scenes, acquired primarily during August–September 2025, were manually delineated to ensure precise glacier boundary detection. Regional climatic data, including summer temperature and winter precipitation from the ERA5 reanalysis, were compiled to support interpretation of glacier changes since the 1960s. The new inventory identifies 2341 glaciers covering 964.0 ± 22.8 km² across the Greater Caucasus. Glacier distribution is highly uneven: most of the glacier-covered area is found in the Central Caucasus (730.2 ± 15.5 km²), whereas considerably smaller glacierized areas occur in the Western and Eastern sectors. Most glaciers are located on northern slopes (687.7 ± 16.0 km²), reflecting strong topographic and climatic asymmetry. Mean glacier elevations range from ~3300 to 3600 m a.s.l., increasing eastward in response to decreasing precipitation. Size-class analysis shows that small glaciers (<0.5 km²) dominate numerically, whereas a limited number of large valley glaciers (>5.0 km²) contribute disproportionately to total glacier area. Comparison with previous inventories indicates continued and accelerated glacier retreat, particularly since 2014, with a mean area loss rate of −1.8% yr⁻¹. These comparisons further show that a total of 965 glaciers (~122.9 km²) have become extinct across the Greater Caucasus since the 1960s. This trend is primarily driven by increasing summer temperatures and declining winter precipitation. This high-resolution inventory provides the most detailed glacier dataset currently available for the Greater Caucasus and establishes an updated benchmark for future glacier monitoring, climate change studies, and hydrological assessments. Full article

SAR interferometry (InSAR) provides a framework for extracting high-resolution topographic information and detecting surface deformation. By analyzing the phase difference between radar acquisitions obtained at different times, one can characterize landscape geometry and surface changes. However, inherent phase noise often compromises the reliability of the resulting interferometric products. Consequently, there is a sustained need for spatial filtering techniques that suppress noise while preserving structural integrity and resolution. This work addresses the challenge of filtering the unwrapped phase, a process traditionally reliant on accurate coherence images to identify reliable pixels. We evaluate three statistically based spatial filters for phase noise reduction. The Enhanced Lee filter, which utilizes spatial adaptation and a physically grounded probability model, serves as the baseline for comparison. We examine the Gierull model, which improves computational efficiency by restricting the parameter space. To further reduce execution time, we propose and evaluate two empirical alternatives: the truncated wrapped normal (TcN) and the truncated wrapped Cauchy (TcC) distributions. Results indicate that these empirical models significantly reduce computational demand without degrading the quality of the filtered phase. We assess performance using a simulated dataset for objective validation alongside InSAR imagery of La Cumbre volcano, Los Alamos, and Robledo volcano. While the proposed models demonstrate significant gains in computational efficiency compared to current methods, we identify numerical integration as a primary bottleneck in the filtering process; this challenge warrants further investigation. Our results indicate that empirical statistical models provide a viable path for accelerated InSAR processing with accuracy equivalent to traditional, computationally intensive approaches. Full article

Coastal beach–dune systems along the Western Black Sea Coast represent geomorphologically complex and ecologically valuable environments that have been increasingly affected by long-term urbanisation and recreational pressure. This study examines the geomorphological settings, sedimentary connectivity and associated Natura 2000 dune habitats within two urbanised beach–dune systems, Pobeda (Burgas) and Asparuhovo (Varna), to improve their cadastral documentation and support objective conservation assessment. The analysis is based on high-resolution UAS-LiDAR surveys, complemented by UAS photogrammetry and field observations, allowing detailed three-dimensional characterisation of dune landforms, surface morphology and habitat patterns. The results identify foredune-dominated system architectures in both study areas, with the Pobeda (Burgas) and Asparuhovo (Varna) beach–dune systems comprising embryonic dunes, established foredune ridges and low-relief foredune plains, variably developed and spatially fragmented as a result of long-term urbanisation and recreational pressure, and spatially associated with dune habitats. Despite substantial anthropogenic modification, these elements remain recognisable, although locally fragmented and morphologically degraded. Subtle topographic changes related to trampling, informal access routes and surface compaction were detected, particularly affecting foredune crests and foredune plains, with implications for sediment transport continuity and habitat stability. The study shows that conventional habitat inventories alone are insufficient for capturing such changes. Integrated geomorphological and habitat analysis based on UAS-LiDAR provides a reliable framework for accurate mapping, conservation status assessment and informed consideration of coastal dune systems within the Natura 2000 network and related protection schemes. Full article

Vegetation net primary productivity (NPP) is a key indicator of ecosystem functioning in tropical rainforests and has important implications for carbon cycling and ecosystem stability. Examining the spatial and temporal variation in vegetation NPP and the factors associated with it can help inform ecosystem management and responses to climate change. In this study, Hainan Tropical Rainforest National Park (HTR), China, was selected as a representative tropical rainforest ecosystem. MODIS NPP data, Landsat imagery, meteorological variables, topographic factors, soil data, and socioeconomic indicators were integrated to analyze the spatiotemporal evolution of vegetation NPP from 2000 to 2023. The Theil–Sen Median trend analysis and Mann–Kendall test were applied to detect temporal trends, while the Optimal Parameter Geographical Detector (OPGD) model was used to identify dominant driving factors and their nonlinear interactions. The results showed that vegetation NPP in HTR exhibited an overall increasing trend during the study period, although short-term fluctuations occurred. Spatially, NPP was higher in the west and south and lower in the east and north. Elevation, soil type, and land use type were the main variables associated with this pattern. Moreover, interactions between natural and human-related factors accounted for more of the spatial variation in NPP than individual factors considered separately. These findings improve the understanding of vegetation productivity dynamics in tropical rainforest ecosystems and provide scientific insights for carbon sequestration enhancement, ecological conservation, and sustainable ecosystem management in tropical rainforests under global climate change. Full article

Mountain ecosystems are susceptible to climate change, and alpine treeline ecotones (ATEs) represent one of the significant responsive indicators of climate-driven environmental change. This study examines long-term spatiotemporal dynamics of the ATE across the Eastern Slopes of the Canadian Rocky Mountains (ESCR) from 1984 to 2023, with the objective of assessing whether regional climate warming has influenced ATE extent and elevation across different aspects and watersheds. Multi-decadal Landsat imagery, ERA5-Land temperature data, and topographic variables were integrated within a Google Earth Engine (GEE) framework to map ATEs using the Alpine Treeline Ecotone Index (ATEI), a probabilistic approach designed to capture transitional vegetation zones. Temporal trends were evaluated using non-parametric statistics, correlation analyses, and watershed- and aspect-based comparisons. Results indicate that the total alpine treeline ecotone (ATE) area in the ESCR was approximately 13.3% larger in 2023 than in 1984. However, the temporal evolution of ATE extent and elevation was non-monotonic, and linear trend analyses did not detect statistically significant increasing or decreasing trends over the full study period. ATE elevation and expansion exhibited pronounced spatial heterogeneity, with greater changes occurring on north- and northwest-facing slopes and within selected watersheds. In contrast, summer (July–September) temperatures increased significantly (+2.84 °C), exceeding global land-only warming rates, and vegetation greenness (NDVI) showed a strong, statistically significant positive relationship with temperature. These findings show that while climate warming has clearly increased vegetation productivity, elevational ATE dynamics remain spatially heterogeneous and temporally non-synchronous with summer temperature trends. Full article

Land degradation is a growing concern in arid and semi-arid regions, posing severe threats to ecosystem stability, agricultural productivity, and rural livelihoods due to the combined effects of natural processes and human activities. This study examines the role of Opuntia ficus-indica (OFI), a drought-resistant cactus, in mitigating land degradation and enhancing ecosystem resilience in central Tunisia using Landsat 5 and 9 satellites with 30 m spatial resolution. Spatio-temporal dynamics of land use/land cover (LULC) and variations in key spectral indices sensitive to vegetation and soil conditions were analyzed over the period from 2000 to 2024. Using a remote sensing-based multi-index framework, Land Degradation Index (LDI) maps were generated for 2000–2010 and 2010–2024 sub-periods. Change detection analysis revealed a marked reduction in moderate-to-severe land degradation, particularly in areas characterized by OFI expansion. NDVI values associated with OFI increased significantly, from less than 0.1 in 2000 to about 0.18 in 2024, indicating enhanced vegetation vigor and improved adaptive capacity under semi-arid climatic conditions. To further assess species performance, correlation analyses were conducted between NDVI-OFI values and topographic variables, including elevation and terrain curvature. Results show a strong positive relationship between NDVI-OFI and elevation, with a clear temporal improvement from 2000 to 2024. In addition, NDVI values were highest in convex terrain forms (0.2), highlighting OFI’s ability to thrive in erosion-prone and topographically exposed environments. Findings confirm the effectiveness of OFI in reversing land degradation processes, supporting restoration through an integrated approach combining multi-temporal remote sensing and topographic analysis. The study highlights the potential of OFI as a cost-effective and scalable nature-based solution for land rehabilitation in semi-arid regions. Full article

The objective of this study was to evaluate the accuracy of detecting crop damage caused by wild boar in rapeseed fields using UAV (unmanned aerial vehicle)-derived RGB (red, green and blue) imagery and deep learning segmentation models. The experiments were conducted on rapeseed crops at full maturity shortly before harvest in central-western Poland in 2021. Four convolutional neural network architectures—U-Net (U-shaped network), U-Net++, DeepLabV3+ (deep learning + labelling), and PSPNet (Pyramid Scene Parsing Network)—were benchmarked using two input configurations: RGB imagery alone and RGB combined with the topographic position index (TPI) derived from a digital surface model (DSM). Model performance was assessed using overall accuracy, F1-score (harmonic mean of precision and recall), and Intersection over Union (IoU), with class-specific metrics reported to provide a realistic evaluation of damaged-area detection. For RGB-only data, overall accuracy ranged from 0.957 to 0.972, while damaged-class F1 and IoU reached 0.752 and 0.603, respectively, for the best-performing model (U-Net). When RGB data were supplemented with TPI, overall accuracy and damaged-class metrics changed only slightly, indicating limited benefit from the topographic feature under these field conditions. Non-damaged crop areas were consistently well-classified (F1 > 0.977, IoU > 0.955). These results confirm that UAV-based RGB imagery enables reliable late-season assessment of wildlife-induced crop damage, and that reporting class-specific metrics in spatially independent test sets is essential for realistic performance evaluation. Full article

Automated landslide change detection using remote sensing imagery is critical for rapid disaster response. However, landslide change detection using bi-temporal optical imagery is frequently degraded by cross-region domain shifts and by the elongated, anisotropic morphology of landslide boundaries, leading to substantial pseudo-change alarms. To suppress pseudo-changes and improve cross-region robustness, we propose a DEM-assisted topography-conditioned and orientation-adaptive Siamese network (DEMO-Net) that injects topographic inductive bias through terrain-conditioned feature modulation and orientation-adaptive convolutions. Specifically, DEM-derived multi-channel priors are encoded to predict spatially varying FiLM parameters that recalibrate shallow optical features, suppressing spurious changes while preserving discriminative cues. In addition, we introduce an adaptive-oriented attention convolution that leverages a DEM-derived aspect to guide sparse multi-orientation aggregation via shared-kernel transformation, enabling direction-aware receptive-field alignment for elongated and direction-varying landslide structures without costly global attention. Experiments on the GVLM benchmark under a 5-fold site-wise cross-region protocol show that DEMO-Net achieves 85.17% F1 and 74.26% mIoU, outperforming the strongest CNN baseline FC-EF by 5.05% and 7.20%, respectively. These results demonstrate the effectiveness of jointly leveraging terrain-conditioned calibration and physically consistent orientation-aligned feature extraction for robust cross-region landslide change detection. Full article

As a major water conservation region and ecological security barrier in China, the Three-River Source Region (TRSR) of the Qinghai–Tibet Plateau (QTP) is underlain by extensive permafrost. However, how permafrost degradation alters regional water conservation, particularly the existence of critical thresholds and time-lagged responses, remains insufficiently understood. To clarify these issues, spatiotemporal variations in water conservation (1990–2020) were quantified, and their nonlinear, lagged, and spatially heterogeneous responses to active layer thickness (ALT) were assessed. Using multi-source remote sensing and in situ observations from 1990 to 2020, spatiotemporal variations in water conservation were quantified with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and responses to permafrost degradation were examined by integrating Sen’s slope, GeoDetector, geographically weighted regression (GWR), and structural equation modeling (SEM) methods. The results showed that water conservation increased overall during 1990–2020 and exhibited a pronounced southeast–northwest gradient (higher in the southeast and lower in the northwest); the rates of change in the Lancang, Yellow, and Yangtze headwaters were 63.5, 56.5, and 31.0 mm a⁻¹, respectively. GeoDetector results indicate that precipitation was the dominant control on the spatial heterogeneity of water conservation (q = 0.704), and its interaction with active layer thickness (ALT) further increased explanatory power (q = 0.736). ALT also interacted with vegetation (q = 0.224) and topography (q = 0.157), suggesting that permafrost effects are modulated by vegetation condition and topographic setting in addition to water inputs. Piecewise regression identified a potential threshold at ALT = 1.77 m, indicating a shift in the ALT–water conservation relationship across this threshold. A 5–7-year lag in the response of water conservation to ALT was also detected, particularly apparent in continuous permafrost zones. Overall, water conservation exhibits a clear southeast–northwest gradient and a delayed response to ALT changes. In addition, the response exhibits clear spatial clustering, with the strongest sensitivity observed in areas with ice-rich permafrost overlain by alpine meadow, and a potential ALT breakpoint further suggests nonlinear permafrost–water conservation coupling. Full article

Reliable hydrologic modeling in arid, topographically complex watersheds depends on accurate land-use/land-cover (LULC) representation. This study evaluates how different LULC categorization methods affect simulated runoff for the Wadi Hatta watershed (UAE) using a GIS-driven machine learning framework that combines high-resolution remote sensing with hydrologic modeling. LULC maps were generated in Google Earth Engine using Random Forest (RF) and Support Vector Machine (SVM) classifiers applied to Sentinel-2 (10 m) and Landsat 8/9 (30 m) imageries and compared with the 10 m ESRI predefined LULC dataset. The resulting LULC classifications were converted to SCS Curve Numbers and used in HEC-HMS hydrologic modeling to simulate runoff under a 50-year design storm, under consistent meteorological and physical conditions. Results show that Sentinel-2 + SVM achieved the highest classification accuracy (overall accuracy up to 0.86) and produced the earliest and highest simulated peak discharge (11.4 m³/s), reflecting improved detection of impervious surfaces. In contrast, the Landsat-9 + RF scenario yielded the lowest peak (7.5 m³/s), consistent with a higher proportion of pervious land covers. LULC change analysis between 2017 and 2024 showed increases in forest cover (1.0–3.3%) and built-up areas (6.0–7.9%) driven by afforestation and urban expansion. These results demonstrate that LULC input resolution and classifier selection significantly influence hydrologic model sensitivity and runoff estimates, underscoring the need for carefully selected, high-resolution LULC products in flood risk assessment and water resource planning in data-scarce arid environments. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Long-term wind speed changes over the Yangtze River waterway have critical implications for inland shipping efficiency, emission dispersion, and renewable energy potential. This study utilizes a high-resolution 5 km gridded reanalysis dataset spanning 1979–2018 to conduct a comprehensive spatiotemporal analysis of surface wind climatology, variability, and trends along China’s primary inland waterway. A pivotal regime shift was identified around 2000, marking a transition from terrestrial stilling to a recovery phase characterized by wind speed intensification. Multiple change-point detection algorithms consistently identify 2000 as a pivotal turning point, marking a transition from the late 20th century “terrestrial stilling” to a recovery phase characterized by wind speed intensification. Post-2000 trends reveal pronounced spatial heterogeneity: the upstream section exhibits sustained strengthening (+0.02 m/s per decade, p = 0.03), the midstream shows weak or non-significant trends with localized afternoon stilling in complex terrain (−0.08 m/s per decade), while the downstream coastal zone demonstrates robust intensification exceeding +0.10 m/s per decade during spring–autumn daytime hours. Three distinct wind regimes emerge along the 3000 km corridor: a high-energy maritime-influenced downstream sector (annual means > 3.9 m/s, diurnal peaks > 6.0 m/s) dominated by sea breeze circulation, a transitional midstream zone (2.3–2.7 m/s) exhibiting bimodal spatial structure and unique summer-afternoon thermal enhancement, and a topographically suppressed upstream region (<2.0 m/s) punctuated by pronounced channeling effects through the Three Gorges constriction. Critically, the observed recovery contradicts widespread basin greening (97.9% of points showing significant positive NDVI trends), which theoretically should enhance surface roughness and suppress wind speeds. Correlation analysis reveals that wind variability is systematically controlled by large-scale atmospheric circulation patterns, including the Northern Hemisphere Polar Vortex (r ≈ 0.35), Western Pacific Subtropical High (r ≈ 0.38), and East Asian monsoon systems (r > 0.60), with distinct seasonal phase-locking between baroclinic spring dynamics and monsoon-thermal summer forcing. These findings establish a comprehensive, fine-scale climatological baseline essential for optimizing pollutant dispersion modeling, and evaluating wind-assisted propulsion feasibility to support shipping decarbonization goals along the Yangtze Waterway. Full article

Mountain Excavation and Land Creation Projects (MELCPs) have emerged as a critical strategy for expanding urban development space in mountainous regions facing land scarcity. Dynamic monitoring and risk management of these projects are essential for promoting sustainable urban development. This study develops an integrated monitoring framework for MELCPs by combining ascending and descending Sentinel-1 SAR data, Sentinel-2 optical imagery, SRTM digital elevation models (DEM), and field survey data. The framework incorporates multi-temporal change detection, random forest classification, and time-series InSAR analysis to systematically capture the spatiotemporal evolution and subsidence mechanisms associated with MELCPs. Key findings include: (1) The use of dual-orbit SAR data significantly improves the detection accuracy of excavation areas, achieving an overall accuracy of 87.1% (Kappa = 0.85) and effectively overcoming observation limitations imposed by complex terrain. (2) By optimizing the combination of spectral, texture, topographic, and polarimetric features using a random forest algorithm, the classification accuracy of MELCPs is enhanced to 91.2% (Kappa = 0.889). This enables precise annual identification of MELCP progression from 2017 to 2022, revealing a three-stage evolution pattern: concentrated expansion, peak activity, and restricted slowdown. Specifically, the reclaimed area increased from 2.66 km² (pre-2018) to a peak of 12.61 km² in 2021, accounting for 34.56% of the total area of the study region, before decreasing to 2.69 km² in 2022. (3) InSAR monitoring from 2017 to 2023 indicates that areas with only filling experience minor shallow subsidence (<50 mm), whereas subsequent building loads and underground engineering activities lead to continuous deep soil consolidation, with maximum cumulative subsidence reaching 333.8 mm. This study demonstrates that subsidence in MELCPs follows distinct spatiotemporal patterns and is predictable, offering important theoretical insights and practical tools for engineering safety management and territorial spatial optimization in mountainous cities. Full article