Search Results (8,806)

Draining peatlands for peat extraction converts them into significant sources of greenhouse gas (GHG) emissions. Quantifying GHG emissions at the regional scale remains challenging because direct field measurements are spatially limited, while GHG accounting for land-use planning requires spatially explicit information. Building on the advances in remote sensing (RS) as a scalable low-cost emission accounting tool for large areas, this study presents a proof-of-concept workflow that integrates satellite-based land-cover classification with an emission-factor (EF) approach to support spatial upscaling of peatland GHG estimates. Using Sentinel-2 imagery and a supervised Random Forest classifier, peatland-related land-cover classes were mapped for selected sites in Latvia. The classification results show higher accuracy for spectrally distinct classes such as raised bogs and active peat-extraction areas, while more heterogeneous classes exhibited lower performance. The study provides an overview of how to utilize the RS approach to generate accurate land-cover maps, which can be used to upscale GHG estimation in Latvia when field data is limited. The study does not include calibration against site-level flux measurements, uncertainty propagation, or temporal variability analysis; therefore, the emission results are illustrative and consistent with current EF-based inventory practice rather than validated site-specific fluxes. Full article

Fragmented farmland and persistent cloud–snow interference in the high-altitude cold regions of the Qinghai–Tibet Plateau, coupled with unstable crop phenology, pose significant challenges for accurate cereal and oil crop identification using single-date imagery or low-dimensional features. This study focused on the agricultural areas of the Shigatse River Valley in the Qinghai–Tibet Plateau. Leveraging the Google Earth Engine (GEE) cloud computing platform, we integrated Sentinel-2 remote sensing data with field survey sampling data to extract the planting structures, distribution patterns, and cultivated areas of cereal and oil crops. Three machine-learning classifiers—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—were evaluated to investigate the influence of different feature sets and classifier combinations on mapping accuracy. The results indicated that when all feature bands were utilized, the RF classifier achieved the highest performance, with an overall accuracy of 84.77% and a kappa coefficient of 0.64, outperforming both the SVM and GBT models. The incorporation of phenological and topographic features further enhanced classification accuracy, providing a robust framework for identifying cereal and oil crops in high-altitude environments. Based on the optimal model estimation, the cultivated areas in 2021 were 581.52 km² for highland barley, 295.39 km² for wheat, and 386.81 km² for rapeseed. Their spatial patterns closely aligned with the valley-terrace topography and local irrigation conditions. These findings offer novel insights and a reliable methodology for the rapid extraction of crop spatial information in regions with complex planting structures. Full article

Rice plant height is a key indicator of crop growth and phenology, yet continuous daily estimation remains challenging under limited field observations. This study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements. Daily plant height was estimated as a posterior-weighted ensemble of multiple LUT-derived heights, together with uncertainty reflecting ambiguity among plausible growth trajectories. Applied to rice paddies in Ryugasaki City, Japan, using Harmonized Landsat–Sentinel-2 data from the 2025 growing season, the method achieved and RMSE = 7.08 cm on the validation dataset, outperforming simple baseline approaches. The estimated daily height time series also enabled evaluation of the timing at which plant height reached 70 cm, revealing clear spatial variability among fields and an associated uncertainty of approximately 10 days. Although this threshold was discussed with reference to previous studies on L-band SAR sensitivity, the present study relied solely on optical observations. Overall, the proposed framework provides a data-efficient and explainable approach for daily, spatially explicit rice growth monitoring, while current limitations include the single-region, single-year LUT construction and the simplified statistical assumptions used in the Bayesian weighting framework. Full article

The optical high spatial resolution component of the ESA Copernicus Earth Observation program is relying on the Sentinel-2 satellites. To secure the mission continuity, the Sentinel-2C unit was launched and has recently joined the Sentinel-2A and Sentinel-2B operational plan. The objective of the paper is to provide a status and a quantified assessment of the radiometric inter-operability of the latest unit with the constellation. The analyses reported here were performed using different vicarious methods during the commissioning phase of Sentinel-2C. Two of the methods were used for the first time with a Sentinel-2 satellite: lunar calibration and tandem inter-comparisons on selected surfaces. The results of the different methods are compared and the vicarious radiometric adjustment strategy is described. Finally, we discuss the impact of the different sources of uncertainty impacting the radiometric assessment. Full article

Background and Objectives: Sentinel lymph node biopsy (SLNB) is increasingly used for high-risk, clinically node-negative cutaneous squamous cell carcinoma (cSCC), yet pathological reporting remains binary, lacking morphological stratification. The prognostic relevance of nodal tumor burden subtypes—isolated tumor cells (ITC), micrometastases, and macrometastases—is well established in melanoma and breast cancer but remains uncharacterized in cSCC. We aimed to describe the morphological spectrum of sentinel lymph node involvement in a consecutive institutional cohort and determine whether primary tumor characteristics predict the extent of nodal colonization. Materials and Methods: We conducted a retrospective-observational study at Clinical Center Niš (Serbia) including 35 consecutive clinically N0 high-risk cSCC patients who underwent SLNB using a dual-tracer protocol (⁹⁹mTc-labeled albumin and methylene blue). Sentinel nodes were processed by serial sectioning with hematoxylin-eosin and pancytokeratin (AE1/AE3) immunohistochemistry. Deposits were classified as ITC (≤0.2 mm), micrometastases (>0.2–2.0 mm), or macrometastases (>2.0 mm). Clinicopathologic predictors were evaluated using the Mann–Whitney U test, Fisher’s exact test, the Kruskal–Wallis test, and the Spearman rank correlation test. Results: SLN involvement was identified in 12 of 35 patients (34.3%). Among positive cases, ITC accounted for 6 patients (50.0%), micrometastases for 5 (41.7%), and macrometastasis for 1 (8.3%)—minimal nodal disease constituting 91.7% of positive findings. No primary tumor feature—including diameter, thickness, grade, perineural invasion, or lesion multiplicity—significantly distinguished ITC from overt metastatic deposits. Patients with ITC showed numerically higher median tumor thickness (8.0 mm) than those with micrometastases (4.0 mm), though this did not reach significance (Kruskal–Wallis p = 0.065). Conclusions: SLN positivity in high-risk cSCC is morphologically heterogeneous, with minimal nodal disease predominating. Primary tumor features do not reliably stratify the extent of nodal colonization. Structured tumor-burden reporting—distinguishing ITC, micrometastases, and macrometastases—should be adopted as standard practice to enable meaningful prognostic comparisons and inform individualized management. Full article

Background/Objectives: Multidrug-resistant (MDR) Escherichia coli resistant to third-generation cephalosporins are a growing One Health concern, but data on extraintestinal pathogenic E. coli (ExPEC) from wildlife in North Africa remain scarce. We aimed to characterize ESBL/AmpC-producing ExPEC from captive wild mammals in Tunisia and to situate these isolates in a global genomic context. Methods: In 2018, 30 fecal samples from 14 captive wild mammals in a private farm were screened on cefotaxime agar. Four cefotaxime-resistant E. coli isolates were recovered from a llama, lion, hyena, and tiger. Antimicrobial susceptibility testing and Illumina whole-genome sequencing were combined with in silico typing, resistome and virulome profiling, plasmid and mobile element analysis, human pathogenicity prediction and core-genome MLST-based minimum-spanning trees. Results: All isolates were MDR but remained susceptible to carbapenems, colistin and tigecycline. Two ST162/B1 isolates from the llama and tiger carried bla_CMY-2, whereas two ST69/D isolates from the lion and hyena harbored bla_CTX-M-15 and qnrS1. Genomes encoded 61–68 antimicrobial resistance genes and 114–131 virulence-associated genes, together with IncF-, IncI1- and IncY-type plasmids and IS26-rich insertion sequence profiles. Mating-out assays yielded cefotaxime-resistant transconjugants, supporting plasmid transferability of bla_CMY-2 or bla_CTX-M-15. PathogenFinder predicted a ≥0.93 probability of human pathogenicity for all isolates. cgMLST-based trees showed that Tunisian ST69 and ST162 clustered within internationally disseminated lineages containing human, animal and food isolates, rather than forming wildlife-restricted branches. Conclusions: Captive wild mammals in Tunisia can harbor high-risk ExPEC lineages combining ESBL/AmpC production, multidrug resistance and extensive virulence and mobility gene repertoires. These findings highlight captive wildlife as potential reservoirs and sentinels of clinically relevant E. coli and underscore the need for integrated WGS-based One Health surveillance at the human–animal–environment interface in North Africa. Full article

We present and discuss time-lapse gravity variations recorded by a large-scale absolute gravity network operating in Central Italy. The network comprises four stations distributed across the Lazio, Umbria, and Abruzzo regions, areas affected by the significant seismic activity of 2009 and 2016–2017. From 2018 to 2023, six campaigns were carefully conducted using an FG5 absolute gravimeter. We detected significant gravity decreases around 2020 reaching between −15 and −20 μGal in three sites and approximately −37 μGal at the fourth. The Sentinel-1 time series of permanent scatterers (PS) allowed us to exclude significant contribution from vertical deformations to the observed gravity changes. We analyzed both ground-based data (rainfall gauges and well water levels) and satellite-based observations (the Gravity Recovery and Climate Experiment-Follow-On, GRACE-FO, mission) together with the Global Land Data Assimilation System (GLDAS) and precipitation models. The results reveal a significant decrease in the regional groundwater content from 2018 to the end of 2020, which coincides temporally with the observed gravity decrease. We show that the absolute gravity variation trends observed at all stations are consistent with regional-scale hydrological processes, pointing to a significant decrease in terrestrial water storage (TWS) during the same time interval. At L’Aquila (AQUI), the gravity anomaly is larger than expected from regional hydrological products alone, suggesting an additional local component possibly related to the hydrogeological response of the fractured karst system undergoing significant post-seismic activity. Full article

Background/Objectives: Sebaceous carcinoma (SC) is a rare and aggressive malignancy most often arising in the head and neck. The reported rate of lymph node metastasis is variable and current clinical guidelines surrounding pre-treatment imaging and management of lymph nodes are not well defined. The aim of our systematic review and meta-analysis was to determine a pooled rate of clinically apparent and occult lymph node metastases for SC of the head and neck to inform clinical guidelines. Methods: Per PRISMA guidelines, systematic search of the Pubmed/MEDLINE and EMBASE databases identified studies published before October 2023 reporting regional lymph node status in adults with SC of the head and neck. Meta analysis using the random-effects model was applied to calculate the pooled proportion of subjects with lymph node metastasis. Clinical characteristics of subjects were further analyzed using chi square tests and univariate logistic regression. Results: Thirty-eight studies met inclusion criteria with a total of 2371 patients. The pooled prevalence of regional lymph node involvement, including clinically apparent and occult disease, was 16% (95% CI 13–18%, I² 65%), with increased risk with increasing T stage. The pooled rate of occult lymph node metastases was 7% (95% CI 4–9%, I² 68%). Conclusions: There is a high rate of lymph node involvement in SC of the head and neck, much of which goes undetected during initial workup and treatment. Initial workup should reflect this risk and include appropriate physical exam, imaging, consideration for sentinel lymph node biopsy, and involvement of a multi-disciplinary team. Full article

To address the key limitations of traditional automated landslide detection methods—namely their reliance on large training datasets, insufficient detection accuracy, and high false positive rates—this study proposes an InSAR-based automated landslide detection approach integrating multi-weight factor coupling, referred to as an Improved Hot Spot Analysis (IHSA) method. Built upon InSAR-derived surface deformation data, the proposed method optimizes the hotspot detection model through a spatial weighting matrix that incorporates multi-feature fusion. Morphological processing is further applied to refine landslide boundaries. Validation against manually interpreted ground truth data demonstrates that the proposed method achieves a precision of 90.20%, representing an improvement of 53.61 percentage points over the conventional hotspot analysis method, while maintaining a stable recall rate of 92.00%. The extracted landslide boundaries exhibit high consistency with manual interpretation results, effectively overcoming common issues in traditional approaches such as fragmented outputs and internal voids. This study provides an efficient, training-free solution for large-scale early identification of potential landslides, offering critical methodological support and data foundations for regional landslide detection and hazard mitigation. Full article

Remote sensing is widely used in archaeological prospection to detect surface anomalies (crop marks) indicating buried remains, typically through recognition of visual patterns in high- or very high-resolution imagery acquired by means of satellite, airborne, or drone sensors. In contrast, spectroscopic approaches focusing on variations in spectral signatures still remain rarely applied in archaeological research. This study proposes a technological barrier-free method addressed to archaeologists which is based on pixel-level analysis of the Reflectance Values (RV) and spectral shape variations in the visible, near-infrared and short-wave infrared (VIS-NIR-SWIR) range derived from Sentinel-2 imagery. Spectral signatures are extracted through sampling polygons designed to account for the spatial resolution of the different Sentinel-2 bands and their spatial relationship with the location and size of the archaeological features. The RV method is tested on two Roman archaeological contexts: the ancient city of Telesia Vetere (San Salvatore Telesino, Benevento) and a Roman villa at Podere Colle Agnano (Labro, Rieti) using the full Sentinel-2 archive since 2017. While Telesia has previously been investigated through aerial photo interpretation and archaeological fieldwork, the Roman villa at Labro is documented here for the first time. Results show consistent seasonal repeated spectral separability between areas corresponding to known buried archaeological features and surrounding areas. Similar anomalies were also detected in areas without previously documented remains, thus suggesting the possible presence of buried structures and highlighting the predictive potential of the RV method. Owing to its easiness to use beyond image processing specialism and reliance on open-access data, the method can support archaeological decision-making and guide further investigation with higher-resolution remote sensing data or targeted field surveys, particularly in the framework of preventive archaeology. Full article

Antimicrobial resistance (AMR) is one of the most significant threats to healthcare systems, particularly in low- and middle-income nations where infection prevention and control, antimicrobial stewardship, and laboratory surveillance might not be optimal. Acinetobacter baumannii is a high-risk nosocomial pathogen that has a strong capacity to develop extreme resistance phenotypes. Still, the degree to which extensively drug-resistant (XDR) and pandrug-resistant (PDR) phenotypes reflect the cumulative impact of antimicrobial pressure at unit and system levels in Iraqi hospitals is not fully described. This was a cross-sectional surveillance study that was a laboratory-based investigation done in public hospitals in the Governorate of Kirkuk between January 2024 and January 2025. The BD Phoenix system identified 80 non-duplicate A. baumannii isolates that were obtained in high-risk hospital units. The interpretation of antimicrobial susceptibility testing was done according to CLSI guidelines. Internationally recognized definitions were adjusted to local therapeutic availability to classify isolates as XDR or PDR. Unadjusted odds ratios and Fisher’s exact test were used to assess the associations between the PDR phenotype and the chosen clinical or unit-level variables. Among the 80 isolates, 60 (75%) were XDR and 20 (25%) were PDR. Burn units and wound-related infections were disproportionately represented by PDR isolates. There were significant associations between the PDR phenotype and burn unit admission, wound infection, exposure to invasive devices, long hospitalization (greater than 14 days), and previous exposure to broad-spectrum antibiotics. ICU admission and respiratory infection were not significantly related. Cefepime had in vitro activity only in a subset of XDR isolates. Extreme resistance phenotypes can be used as convenient sentinel measures of cumulative antimicrobial pressure and system-level stress in resource-limited environments. There is an urgent need to strengthen infection prevention and control, antimicrobial stewardship, and laboratory surveillance to preserve the remaining therapeutic options. Full article

Mediterranean meadows of Posidonia oceanica are chronically exposed to complex mixtures of environmental contaminants, including metals and trace elements derived from coastal urbanization, maritime traffic, and industrial activities. This study aimed to assess metal-related toxicity in P. oceanica by integrating multi-element burden analysis with a panel of oxidative stress biomarkers. Concentrations of a wide suite of elements were quantified in samples of internal (juvenile), intermediate, and external (adult) leaves, reflecting the ontogenetic structure of the plant. Oxidative responses were evaluated using five biomarkers [i.e., hydrogen peroxide (H₂O₂), lipid peroxidation (TBARS), superoxide dismutase (SOD), glutathione S-transferase (GST), and catalase (CAT)] measured on each leaf compartment. Biomarker data were standardized and integrated into a merged Stress Index summarizing overall physiological toxicity. Associations between individual elements, the sum of all measured elements (ΣallElements), the Stress Index, and single biomarkers were explored using Pearson correlation analysis. Juvenile leaves exhibited the highest Stress Index values, elevated H₂O₂ and TBARS, and marked activation of SOD and GST, indicating early oxidative toxicity. Intermediate leaves showed a trend toward increased CAT activity, not reaching statistical significance, along with minimal damage, suggesting effective detoxification, whereas adult leaves accumulated higher levels of Fe, Ni, and Pb, but displayed moderate stress responses. Overall, leaf-class structure strongly modulated both exposure and toxicological response. The integration of ΣAllElements with multi-biomarker indices provides a robust framework for diagnosing metal-related toxicity in P. oceanica under realistic multi-element exposure scenarios. Full article

Deep mining of geologically challenging deposits, such as narrow, steeply dipping orebodies, is increasingly pursued to meet the rising demand for mineral resources. However, the geotechnical stability of operations in such environments remains a persistent challenge. A paramount concern is the insufficiently understood mechanisms governing the surface subsidence and stability of underground excavations, which diverge significantly from those in flat or gently dipping deposits. This study bridges this gap through an integrated methodology applied to a deep cut-and-fill gold mine in China. We combined nine years (2016–2025) of SBAS-InSAR monitoring, utilizing 120 Sentinel-1 images corrected with precise orbit and atmospheric correction data, with a comprehensive three-dimensional (3D) numerical simulation. The results reveal a unique subsidence pattern: surface subsidence is highly localized, forming an elliptical basin directly above the orebodies, with a footwall movement angle exceeding 90°. Furthermore, the subsidence magnitude showed minimal progression despite increasing mining depth, with a maximum cumulative subsidence of only 9.3 mm. Numerical simulation confirmed these findings and demonstrated that underground shafts and tunnels remained stable under the sequential extraction of multiple orebody levels. This exceptional geotechnical response is attributed to a synergistic mechanism involving the intrinsic geomechanical advantages of the steeply dipping geometry, the low-disturbance nature of narrow-vein mining, and the crucial structural support provided by the backfilling. This study demonstrates the efficacy of cut-and-fill mining for ensuring operational safety and minimizing surface environmental impact in the deep mining of narrow, steeply dipping orebodies, providing critical insights for the sustainable exploitation of deep mineral resources. Full article

Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which can reduce the accuracy of detecting even moderate anomalies. This paper presents a novel integration of multi-criteria hierarchical labeling with dual-branch deep learning for enhanced vegetation anomaly detection. We combined MODIS (2000–2015) and Sentinel-2 (2015–2025) images and NASA POWER weather records to study a 25-year vegetation record using multi-source satellite data in 5 of Morocco’s ecologically diverse zones. We introduced a three-class hierarchical labeling scheme (normal, moderate, severe) for dynamic vegetation models based on combined vegetation indices (NDVI, EVI, NDWI) and meteorological thresholds. The proposed dual-branch architecture uses independent data streams for unfused data, which include temporal multi-scale CNNs (TMSCNN) for spatiotemporal modeling and bidirectional LSTMs for weather-integrated vegetation data. Systematic ablation studies show improvements from using NDVI (68.98%) to multispectral indices (77.74%), meteorological integration (81.02%), and a final accuracy of 82.34% ± 0.88%. The moderate anomaly class exhibits lower precision (65%), demonstrating the challenge of operationalizing severity-based anomaly classification. This work integrates hierarchical, multi-criteria labeling and hybrid deep learning for solar photovoltaic vegetation monitoring. Full article

Microplastic (MP) and nanoplastic (NP) pollution has emerged as a pervasive and still insufficiently quantified pressure on terrestrial ecosystems, yet its consequences for wild herbivores remain incompletely understood. As key links between primary producers and higher trophic levels, wild herbivores occupy a critical ecological position and may serve both as exposed receptors and as biological vectors of plastic contamination. This manuscript presents a narrative review that synthesizes recent advances in understanding the physiological, behavioural, and ecological implications of MP and/or NP exposure in free-ranging herbivorous mammals, integrating evidence from field surveys, experimental studies, ecological modelling, and supportive mechanistic findings from livestock and experimental mammalian systems. Available evidence indicates that MPs and NPs are consistently detected in wild herbivores from both human-modified and protected landscapes, demonstrating widespread terrestrial exposure. Reported biological effects include oxidative stress, digestive dysfunction, inflammatory and immune responses, altered gut microbial communities, impaired nutrient assimilation, and organ-level damage, although much of the mechanistic evidence derives from controlled laboratory or livestock-based studies rather than direct wildlife investigations. Behavioural responses remain comparatively underexplored, particularly in large-bodied herbivores, with limited evidence for altered foraging, habitat use, and stress-related behaviours. At the ecosystem level, emerging studies suggest that herbivores may contribute to the landscape-scale redistribution of MPs and NPs through movement and faecal deposition, with potential downstream effects on soil processes, nutrient cycling, and plant–herbivore interactions. However, the current evidence base is constrained by major methodological and conceptual limitations, including the lack of standardized detection and reporting protocols, limited ecological realism in exposure studies, taxonomic and geographic biases, and poor resolution of long-term population-level and food-web consequences. Overall, the available literature indicates that MP and NP pollution represent a multifaceted and emerging risk to wild herbivores and the ecosystems they inhabit. Future research should prioritize standardized contamination-controlled monitoring, non-invasive faecal surveillance, ecologically realistic chronic exposure studies, and integrated conservation frameworks that recognize wild herbivores as sentinel species for terrestrial plastic pollution. Full article