Search Results (8,344)

Gabal (G.) Suwayqat El-Arsha contains two distinct phases of granitoids: I-type granodiorite and A-type monzogranite. Both of them experienced intense fractional crystallization that affected plagioclase, alkali feldspar, quartz, and, to a lesser degree, ferromagnesian minerals. EnMAP hyperspectral data were used to discriminate between the different granitoid types through spectral analysis, using various techniques, including the Sequential Maximum Angle Convex Cone (SMACC) method. Granodiorite has high SiO₂ (68.21–71.44 wt%), Al₂O₃ (14.29–14.92 wt%), Fe₂O₃ (1.99–3.32 wt%), and CaO (2.34–3.87 wt%), whereas monzogranite has even higher SiO₂ (73.58–75.87 wt%) and K₂O (4.28–4.88 wt%). Both granodiorite and monzogranite exhibit calc-alkaline, peraluminous to metaluminous, and medium- to high-K characteristics, with attendant enrichment of light REE and LILE and depletion of heavy REE and HFSE. A negative Eu anomaly may indicate early plagioclase fractionation, especially in the monzogranite. The I-type granodiorite is likely derived from a high-K, mafic protolith that partially melted during lithospheric delamination, leading to severe fractional crystallization in the upper crust in a post-collisional environment. In contrast, the monzogranite exhibits A-type characteristics and was likely emplaced in an anorogenic setting. Both granites were affected by several episodes of hydrothermal alteration, resulting in silicification, kaolinitization, sericitization, and chloritization. The intrusions studied here exhibit key similarities with those in the Wadi El-Hima area, including tectonic setting, petrogenetic type, Neoproterozoic age (Stage I collisional: ca. 650–620 Ma; Stage II post-collisional: ca. 630–590 Ma), and mineralogical assemblages (notably two-mica granites). These correlations suggest that both suites form part of a regionally extensive batholith composed of I- and A-type granites, stretching from north of the Marsa Alam Road (Umm Salatit–Homrit Waggat) southward to at least Wadi El-Hima. Full article

Wildfires, which encompass all fires that occur outside urban areas, represent one of the most frequent forms of natural disaster worldwide. This study presents the wildfire occurrence across the territory of Southeastern Europe, covering an area of 800,000 km² (Greece, Romania, Serbia, Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, Bulgaria, and Moldova). The research applies geospatial artificial intelligence techniques, based on the integration of machine learning (Random Forest (RF), XGBoost), deep learning (Deep Neural Network (DNN), Kolmogorov–Arnold Networks (KAN)), remote sensing (Sentinel-2, VIIRS), and Geographic Information Systems (GIS). From the geospatial database, 11 natural and anthropogenic criteria were analyzed, along with a wildfire inventory comprising 28,952 historical fire events. The results revealed that areas of very high susceptibility were most prevalent in Greece (10.5%), while the smallest susceptibility percentage was recorded in Slovenia (0.2%). Among the applied models, RF demonstrated the highest predictive performance (AUC = 90.7%), whereas XGBoost, DNN, and KAN achieved AUC values ranging from 86.7% to 90.5%. Through a SHAP analysis, it was determined that the most influential factors were global horizontal irradiation, elevation, and distance from settlements. The obtained results hold international significance for the implementation of preventive wildfire protection measures. Full article

Remote sensing provides an efficient approach for monitoring ecosystem dynamics in the arid and semi-arid regions of Xinjiang, yet traditional forest-land extraction methods (e.g., spectral indices, threshold segmentation) show limited adaptability in complex environments affected by terrain shadows, cloud contamination, and spectral confusion with grassland or cropland. To overcome these limitations, this study used three convolutional neural network-based models (FCN, DeepLabV3+, and PSPNet) for accurate forest-land extraction. Four tri-band training datasets were constructed from Sentinel-2 imagery using combinations of visible, red-edge, near-infrared, and shortwave infrared bands. Results show that the FCN model trained with B4–B8–B12 achieves the best performance, with an mIoU of 89.45% and an mFscore of 94.23%. To further assess generalisation in arid landscapes, ESA WorldCover and Dynamic World products were introduced as benchmarks. Comparative analyses of spatial patterns and quantitative metrics demonstrate that the FCN model exhibits robustness and scalability across large areas, confirming its effectiveness for forest-land extraction in arid regions. This study innovatively combines band combination optimization strategies with multiple deep learning models, offering a novel approach to resolving spectral confusion between forest areas and similar vegetation types in heterogeneous arid ecosystems. Its practical significance lies in providing a robust data foundation and methodological support for forest monitoring, ecological restoration, and sustainable land management in Xinjiang and similar regions. Full article

Fibroblasts, traditionally viewed primarily as structural cells responsible for extracellular matrix production and tissue architecture, have emerged as important immunomodulatory players in inflammation. These cells actively participate in inflammatory processes through multiple mechanisms: recognizing and responding to inflammatory stimuli, producing diverse inflammatory mediators, and engaging in complex interactions with various immune cells. This review explores the multifaceted immunomodulatory functions of fibroblasts, including their capacity to sense inflammatory signals, secrete inflammatory mediators, modulate immune cell behavior, and establish a pro-inflammatory microenvironment. Understanding the dynamic role of fibroblasts in inflammatory processes provides insights into inflammatory pathology and may inform the development of novel therapeutic strategies targeting fibroblast-mediated immune modulation. Full article

Land consolidation requires reliable and objective land valuation to ensure transparency and fairness in the reallocation process. This study introduces a data-driven method for assessing agricultural site productivity based on vegetation indices derived from multispectral imagery, supported by Sentinel satellite data and validated using handheld chlorophyll meter measurements. Site productivity, defined as the land’s ability to generate yield and biological value, is determined by natural and environmental factors that directly influence economic worth. Vegetation indices (NDVI, SAVI) obtained from UAV imagery showed a strong correlation with chlorophyll content, confirming the reliability of this non-invasive assessment. The analysis, conducted in Poland and Slovakia, demonstrated the method’s applicability under two different land consolidation systems: a market-based model in Poland and an ecologically oriented approach in Slovakia. The proposed framework proved easy to implement and provided consistent results even without the use of ground control points. By reducing fieldwork time and costs while improving valuation accuracy, this method enhances the objectivity and transparency of land consolidation procedures. The findings confirm the potential of vegetation indices to support data-driven and environmentally informed land valuation across diverse consolidation contexts. Full article

Monitoring construction-phase settlement of estuary-crossing tunnels founded on coastal soft soils is critical for risk management, yet dense in situ measurements are often unavailable along linear corridors. This study uses Sentinel-1A ascending SAR imagery (65 scenes, September 2022–August 2025) to retrieve time-series deformation along the Sanya Estuary Channel tunnel (China) using Permanent Scatterer InSAR (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR). The two approaches reveal a consistent subsidence hotspot at Tunnel Section D (DK0+000–DK0+330), while most of the corridor remains within ±5 mm/a. The line-of-sight deformation rates range from −24 to 17.7 mm/year (PS-InSAR) and −29.9 to 18.7 mm/a (SBAS-InSAR). Time-series analysis at representative points in Section D indicates a maximum cumulative settlement of −75.7 mm and a clear acceleration after May 2023. By integrating the deformation results with geological reports, construction logs and rainfall records, we infer that compressible marine clays and interbedded sand/aquifer zones control the hotspot, whereas excavation/dewatering and rainfall-related groundwater fluctuations further promote consolidation. The results provide a practical basis for subsidence risk screening and monitoring prioritization for estuary-crossing infrastructure in coastal soft-soil settings. From a sustainability perspective, the proposed joint PS–SBAS InSAR framework provides a scalable and cost-effective tool for continuous deformation surveillance, supporting preventive maintenance and risk-informed management of urban underground infrastructure. Full article

Landslide deformation monitoring plays a critical role in geohazard prevention and risk mitigation in mountainous regions, where timely and reliable deformation information is essential for early warning and disaster management. Monitoring landslide deformation in mountainous areas remains a persistent challenge, largely due to rugged topography, dense vegetation cover, and low interferometric coherence—factors that substantially limit the effectiveness of conventional InSAR methods. To address these issues, this study aims to develop a robust time-series InSAR framework for enhancing deformation detection and measurement density under low-coherence conditions in complex mountainous terrain, and accordingly introduces the Sequential Estimation and Total Power-Enhanced Expectation–Maximization Inversion (SETP-EMI) approach, which integrates dual-polarization Sentinel-1 SAR time series within a recursive estimation framework, augmented by polarimetric coherence optimization. This methodology allows for dynamic assimilation of SAR data, improves phase quality under low-coherence conditions, and enhances the extraction of distributed scatterers (DS). When applied to Zhenxiong County, Yunnan Province—a region prone to geohazards with complex terrain—the SETP-EMI method achieved a landslide detection rate of 94.1%. It also generated approximately 2.49 million measurement points, surpassing PS-InSAR and SBAS-InSAR results by factors of 22.5 and 3.2, respectively. Validation against ground-based leveling data confirmed the method’s high accuracy and robustness, yielding a standard deviation of 5.21 mm/year. This study demonstrates that the SETP-EMI method, integrated within a DS-InSAR framework, effectively overcomes coherence loss in densely vegetated plateau regions, improving landslide monitoring and early-warning capabilities in complex mountainous terrain. Full article

Klebsiella pneumoniae is increasingly reported in canine medicine, with growing attention to multidrug-resistant (MDR) and hypervirulent strains. Although its overall prevalence in dogs appears relatively low, published studies indicate that affected animals may harbor clinically important resistance determinants, including extended-spectrum β-lactamases and, less frequently, carbapenemases. Canine isolates described in the literature also carry virulence-associated traits such as hypermucoviscosity and enhanced iron-acquisition systems, which overlap with features of high-risk human lineages and suggest potential, but largely inferred, interspecies links. These observations highlight the relevance of a One Health perspective and the importance of coordinated surveillance that includes companion animals. This narrative review synthesizes available literature on the epidemiology, clinical presentations, antimicrobial resistance, virulence traits, and molecular characteristics of K. pneumoniae in dogs. We critically evaluate evidence suggesting that dogs may function as reservoirs, sentinels, or amplifiers of MDR strains, particularly in clinical settings or following antimicrobial exposure. In addition, we summarize emerging alternative and adjunctive strategies—such as bacteriophage therapy, antimicrobial peptides, anti-virulence approaches, microbiome-based interventions, as well as strengthened antimicrobial stewardship and infection-control practices—that are under investigation as complements to conventional antibiotics. Overall, published evidence indicates that K. pneumoniae infections in dogs represent an under recognized but potentially important clinical and One Health concern. Continued surveillance, responsible antimicrobial use, and rigorous evaluation of non-antibiotic strategies will be essential to inform future veterinary practice and public health policy. Full article

Catastrophic mass flows originating from the high mountain cryosphere often cause cascading hazards. With increasing human activities in the alpine region and the sensitivity of the cryosphere to climate warming, cryospheric hazards are becoming more frequent in the mountain regions. Monitoring the evolution and impact of the glaciers and ice-rock avalanches and hazard consequences in the mountain regions is crucial to understand nature and drivers of mass flow process in order to prevent and mitigate potential hazard risks. In this study, the glacier and ice-rock avalanches that occurred in the Tibetan Plateau (TP) were investigated based on the Sentinel-2 satellite data and in situ observations, and the main driving forces and impacts on the regional environment, landscape, and geomorphological conditions were also analyzed. The results showed that the avalanche deposit of Arutso glacier No. 53 completely melted away in 2 years, while the deposit of Arutso glacier No. 50 melted in 7 years. Four large-scale ice-rock avalanches in the Sedongpu basin not only had significant impacts on the river flow, landscape, and geomorphologic shape in the basin, but also caused serious disasters in the region and beyond. These glacier and ice-rock avalanches were caused by temperature anomaly, heavy precipitation, climate warming, and seismic activity, etc., which act on the specific glacier properties in the high mountain regions. The study highlights scientific advances should support and benefit the remote and vulnerable mountain communities to make mountain regions safer. Full article

Accurate mapping of rice cultivation is vital for ensuring food security, reducing greenhouse gas emissions, and achieving sustainable development goals. However, large-scale deep learning–based crop mapping remains limited due to the demand for vast, uniformly distributed, high-quality samples. To address this challenge, we propose a Progressive Deep Learning Crop Mapping (PDLCM) framework for national-scale, high-resolution rice mapping. Beginning with a small set of localized rice and non-rice samples, PDLCM progressively refines model performance through iterative enhancement of positive and negative samples, effectively mitigating sample scarcity and spatial heterogeneity. By combining time-series Sentinel-2 optical data with Sentinel-1 synthetic aperture radar imagery, the framework captures distinctive phenological characteristics of rice while resolving spatiotemporal inconsistencies in large datasets. Applying PDLCM, we produced 10 m rice maps from 2022 to 2024 across the middle and lower Yangtze River Basin, covering more than one million square kilometers. The results achieved an overall accuracy of 96.8% and an F1 score of 0.88, demonstrating strong spatial and temporal generalization. All datasets and source codes are publicly accessible, supporting SDG 2 and providing a transferable paradigm for operational large-scale crop mapping. Full article

The deployment of wireless seismic nodal systems necessitates the efficient identification of optimal locations for sensor installation, considering factors such as ground stability and the absence of interference. Semantic segmentation of satellite imagery has advanced significantly, and its application to this specific task remains unexplored. This work presents a baseline empirical evaluation of the U-Net architecture for the semantic segmentation of surfaces applicable for seismic sensor installation. We utilize a novel dataset of Sentinel-2 multispectral images, specifically labeled for this purpose. The study investigates the impact of pretrained encoders (EfficientNetB2, Cross-Stage Partial Darknet53—CSPDarknet53, and Multi-Axis Vision Transformer—MAxViT), different combinations of Sentinel-2 spectral bands (Red, Green, Blue (RGB), RGB+Near Infrared (NIR), 10-bands with 10 and 20 m/pix spatial resolution, full 13-band), and a technique for improving small object segmentation by modifying the input convolutional layer stride. Experimental results demonstrate that the CSPDarknet53 encoder generally outperforms the others (IoU = 0.534, Precision = 0.716, Recall = 0.635). The combination of RGB and Near-Infrared bands (10 m/pixel resolution) yielded the most robust performance across most configurations. Reducing the input stride from 2 to 1 proved beneficial for segmenting small linear objects like roads. The findings establish a baseline for this novel task and provide practical insights for optimizing deep learning models in the context of automated seismic nodal network installation planning. Full article

Land-use and land-cover change (LULCC) is a crucial indicator of environmental transformation and has significant implications for biodiversity, ecosystem services, and climate change. This study investigates land-cover changes between 2017 and 2023 in three distinct mountain regions: the Bucegi Mountains, the Allgäu High Alps, and Mount Olympus. Using remote-sensing data from Sentinel 2 and Geographic Information System (GIS) tools, we analyzed temporal shifts in land-cover types across these regions. The analysis highlights the varying rates and patterns of land-cover transformation in response to environmental and anthropogenic factors. Additionally, the MOLUSCE model was employed to predict future land-cover changes for the year 2029. The findings emphasize the dynamic nature of land-cover in these mountainous areas and offer insights into the potential environmental implications of predicted changes. The Bucegi and the Olympus regions experienced minor land-use changes, while the Allgäu High Alps have the most dynamic changes. The study contributes to a deeper understanding of land-cover dynamics and the applicability of remote sensing and GIS-based predictive models in ecological monitoring. Full article

Vegetation degradation in arid and semi-arid regions is intensifying due to rising temperatures, declining rainfall, soil exposure, and persistent human pressures. Drylands cover over 41% of the global land surface and support nearly two billion people, making their degradation a major environmental and socio-economic concern. However, many remote sensing and GIS-based assessment approaches remain fragmented and difficult to reproduce. This study proposes a Computational Sustainability Framework for vegetation degradation assessment that integrates multi-source satellite data, biophysical indicators, automated geospatial preprocessing, and the Analytical Hierarchy Process (AHP) within a transparent and reproducible workflow. The framework comprises four phases: data preprocessing, indicator extraction and normalization, AHP-based modeling, and spatial classification with qualitative validation. The framework was applied to the Al-Khunfah and Harrat al-Harrah Protected Areas in northern Saudi Arabia using multi-source datasets for the January–April 2023 period, including Sentinel-2, Landsat-8, CHIRPS precipitation, ESA-CCI land cover, FAO soil data, and SRTM DEM. High degradation zones were associated with low NDVI (<0.079), high BSI (>0.276), and elevated LST (>49 °C), whereas low degradation areas were concentrated near wadis and relatively more fertile soils. Overall, the proposed framework provides a scalable and interpretable tool for early-stage vegetation degradation screening in arid environments, supporting the prioritization of areas for ecological investigation and restoration planning. Full article

Eutrophication driven by algal blooms underscores the need for reliable chlorophyll-a (Chl-a) monitoring. Multi-source remote sensing, integrating Sentinel-2 multispectral and UAV hyperspectral data, provides complementary information but its applicability across optically diverse inland waters remains limited. This study evaluates the cross-water-body transferability of Chl-a inversion models using a “single training area with three validation areas” experimental design. Multiple empirical and machine learning models were constructed, and several hyperparameter optimization strategies were tested. Among all modes, the Extreme Gradient Boosting (XGB) model optimized using the Genetic Algorithm (GA) achieved the best performance for UAV data (R² = 0.98, MAPE = 18.59%, RMSE = 2.15 μg/L). The Sentinel-2 counterpart also performed well (R² = 0.86, MAPE = 50.03%, RMSE = 7.89 μg/L). While cross-water-body validation caused moderate performance declines, all models maintained R² > 0.71. Overall, integrating multi-source remote sensing with cross-water-body validation enhances the robustness and transferability of Chl-a inversion models for eutrophic inland waters. Full article

Commercial smooth-hound sharks of the genus Mustelus are commonly landed and consumed in Mediterranean fisheries, raising concerns about potential human exposure to persistent contaminants. This study investigated the occurrence of organochlorine compounds (OCs), including hexachlorobenzene (HCB), dichlorodiphenyltrichloroethane (DDT) and its metabolites, and polychlorinated biphenyls (PCBs), together with per- and polyfluoroalkyl substances (PFAS), in muscle and liver tissues of Mustelus mustelus and Mustelus punctulatus collected in the waters of the Egadi Archipelago (central Mediterranean Sea). OCs were detected in all analyzed samples, with total PCB concentrations reaching higher values in liver compared to muscle tissues, reflecting tissue-specific accumulation and detoxification processes. PFAS were detected in all analyzed muscle samples (1.10–58.5 ng/g w.w.), with PFOS, PFOA and PFNA generally below current European regulatory thresholds, although isolated exceedances were observed. Stable isotope analysis (δ¹³C and δ¹⁵N) highlighted differences in trophic ecology between the two species and suggested that feeding habitat and trophic position may influence contaminant exposure patterns, particularly in M. punctulatus. The human health risk assessment, conducted as a screening-level evaluation, indicated potential concern associated with PCB concentrations in liver tissue, while risks associated with muscle consumption were generally lower. Overall, the integration of contaminant analysis and stable isotopes provides insights into organismal exposure pathways and supports the use of smooth-hound sharks as sentinels of contaminant presence in Mediterranean coastal ecosystems. Full article