Search Results (3,812)

Alternanthera philoxeroides, an invasive alien species, spreads rapidly in river systems via vegetative propagation from stem fragments, requiring river-system-scale monitoring to understand its expansion dynamics and habitat preferences. This study used multi-temporal Sentinel-2 data to analyze spatio-temporal variations in fractional vegetation cover (FVC) within a 3.5 km river reach. FVC estimates derived from vegetation indices were validated against high-resolution aerial images, with an EVI-based model achieving the highest accuracy (RMSE = 9.2%), enabling reliable monitoring even in narrow (~24 m) channels. Time-series analysis from 2019 to 2024 revealed downstream expansion beginning in 2022. Annual maximum FVC (C_max) was used to assess relationships with removal records and bank structures, showing that removal effects were temporary and more pronounced in the first year, while steel sheet-pile banks limited vegetation growth compared to concrete revetments. These results demonstrate that Sentinel-2 data can provide an effective and accessible tool for evaluating invasive plant dynamics and management effectiveness in low-flow river systems where A. philoxeroides dominates the floating vegetation community. Full article

Axillary lymph node management in early-stage breast cancer has undergone a fundamental transformation over the past three decades, shifting from routine radical clearance to precision-guided de-escalation. Ultrasound (US) has emerged as the central imaging modality in this paradigm shift, serving as the first-line tool for preoperative nodal assessment, guidance for biopsy and clip placement, monitoring of response to neoadjuvant chemotherapy (NAC), and localization of marked nodes for targeted axillary dissection (TAD). This review systematically examines the evolution of axillary management in early-stage breast cancer through a US-centric lens. We summarize the historical transition from Halstedian axillary lymph node dissection (ALND) to sentinel lymph node biopsy (SLNB), critically appraise the landmark trials—Z0011, AMAROS, OTOASOR, and SOUND—that have progressively de-escalated axillary surgery, and detail the role of US in preoperative staging (including conventional B-mode, superb microvascular imaging, contrast-enhanced US, elastography, and AI-assisted diagnostics), US-guided biopsy and node marking, and post-NAC TAD. We further explore the integration of US with liquid biopsy, multigene profiling, and molecular subtype-guided systemic therapy to enable individualized decision-making. Finally, we propose a stepwise US-centric clinical algorithm and discuss future directions, including AI-powered real-time interpretation, imaging-omics, and US-guided targeted therapy. This review provides a comprehensive framework for incorporating ultrasound as a key component of multidisciplinary decision-making—alongside pathological, surgical, radiotherapeutic, and molecular inputs—in contemporary axillary management. Full article

Reliable assessment of shorelines extracted from medium-resolution satellite imagery requires independent high-resolution reference data and statistical methods that account for spatial dependence. This study compared three conventional analyst-assisted shoreline-extraction workflows—histogram thresholding, band ratio, and the Normalised Difference Water Index (NDWI)—at Coronation Park, Lake Ontario, Canada, using Sentinel-2 Level-2A imagery. A manually digitised shoreline derived from a UAV-based orthomosaic acquired approximately 27 h before the Sentinel-2 scene served as the independent reference. The UAV-based reference and each Sentinel-2-derived shoreline were divided into 31 ordered segments. For each Sentinel-2-derived segment midpoint, the shortest planar Euclidean distance to the nearest UAV-based reference midpoint was calculated and used to derive mean absolute error (MAE) and root mean square error (RMSE). Residual spatial autocorrelation was assessed using Moran’s I with 9999 permutations. Because the paired differences departed from normality, the Friedman test was treated as the primary overall comparison, while contiguous spatial-block permutation tests across block sizes of two to eight shoreline locations assessed robustness to local spatial dependence. NDWI achieved the highest positional agreement (MAE = 5.645 m; RMSE = 6.429 m), followed by band ratio (MAE = 14.303 m; RMSE = 14.797 m) and histogram thresholding (MAE = 26.167 m; RMSE = 26.910 m). Significant positive residual spatial autocorrelation was identified for all three methods (Moran’s I = 0.587–0.832, all p < 0.001). The Friedman test confirmed a significant extraction-method effect, χ²(2) = 49.226, p < 0.001, Kendall’s W = 0.794, and the effect remained significant across all tested spatial-block sizes, with empirical p-values ranging from 0.000007 to 0.004630. Among the three conventional methods tested at this large-lake site, NDWI provided the highest positional agreement and therefore offers a defensible baseline for evaluating future Sentinel-2 image-enhancement approaches. Full article

The tourism sector in the Sultanate of Oman is central to “Oman Vision 2040”, with a strategic focus of the government on its dynamic transformation. Coastal regions, vital to tourism, are affected by changes to the coastline due to flash floods, sea-water flooding, and erosion. Despite its implications for tourism and the economy, this topic remains relatively under-explored, especially as to use of Sentinel-1 satellite images. This study assesses water-level changes due to erosion in the urban coastal region of the Al-Batinah governorate via land cover classification. Using the Support Vector Machine (SVM) classification technique, the overall accuracy is found to be 97.7% and the Kappa coefficient value for the year 2018 is 1.0. Although, when using the Random Forest (RF) classification technique, the accuracy is nearly identical, there is varying precision for the water area. A critical observation is made, showing significant increase of the water area from 2.99% in the year 2017 to 12.36% in the year 2025, suggesting water encroachment. With fixed-effect and combined-effect size meta-analysis models, the confidence levels were identified as 95.0% and 0.37, respectively, indicating a consistent variation in water area that supports the outcomes of image classification. This study offers a valuable insight for policymakers as to managing coastal regions, along with providing assistance to vulnerable coastal communities. The study focuses on a particular governorate, given the satellite images, whereas a broader regional comparison would address the limitation of the generalizability of results. In the future, the research could integrate surveys from coastal communities and businesses for a comprehensive qualitative data perspective on the region’s tourism sector. Full article

Sentinel lymph node (SLN) mapping plays a pivotal role in oncological staging in veterinary medicine. However, conventional techniques often depend on radioactive tracers or specialized imaging equipment that is unavailable in many clinical settings. This brief report describes the use of indirect radiographic lymphography with the non-ionic iodinated contrast agent iohexol for SLN identification in a dog with auricular melanoma. A 12-year-old spayed female mixed-breed dog presented with an ulcerated cutaneous mass affecting the left auricle. Cytological evaluation was consistent with a melanocytic neoplasm. Immediately before surgery, iohexol was administered intradermally at four peritumoral sites (0.75 mL per cm²). Radiographic images acquired 3 and 4.5 min after injection demonstrated lymphatic drainage to the superficial ventral and superficial dorsal cervical lymph nodes, which were identified as sentinel lymph nodes. The patient underwent vertical ear canal ablation, conchectomy, partial auriculectomy, and excision of the mapped sentinel lymph nodes. Histopathological examination confirmed a mixed-type cutaneous melanoma with macrometastatic involvement of the superficial ventral cervical sentinel node and micrometastatic deposits in both the superficial dorsal cervical and left parotid lymph nodes. Although the parotid lymph node was not enlarged radiographically, afferent lymphatic channels indicated contrast uptake, and histopathology confirmed metastatic involvement. This finding supports considering elective regional lymphadenectomy alongside sentinel lymph node biopsy in similar cases. An incidental cutaneous hemangiosarcoma was identified at a distant abdominal site, excised with complete margins, and was unrelated to the primary tumor. No systemic adverse effects associated with iohexol administration were observed, and only transient mild-to-moderate localized edema developed at the injection site, resolving spontaneously without treatment. These findings suggest that iohexol-based indirect radiographic lymphography is a practical, safe, and accessible technique for sentinel lymph node identification and may represent a promising, though not yet validated, alternative for lymphatic staging in veterinary oncology, although further prospective studies are needed to establish its diagnostic accuracy. Full article

In this study, changes in the surface area of Lake Burdur during the 2015–2025 period and the spatial direction of the associated shrinkage were examined using Sentinel-2 Level-2A satellite images. A total of 111 satellite images, each representing a monthly period, were analyzed using a fixed study window and a lake vicinity mask; a three-cluster unsupervised K-means segmentation method was applied to separate the water surface from bare/drained areas and vegetation classes. The resulting binary water masks were used to convert the lake surface area to km² on a pixel-by-pixel basis, and the geometric center of the lake mass was calculated for each observation date. The unique aspect of this study is that it evaluates lake shrinkage not only through a decrease in surface area but also as a directional spatial process via the movement of the centroid center. In this context, the cumulative displacement was decomposed into X/West and Y/South components using the initial centroid point as a reference; OLS-based linear and logarithmic trend models were established for both directions. Model performances were compared using a 15-fold Monte Carlo cross-validation approach with R², adjusted R², NSE, KGE, MAE, MAPE, MSE, and RMSE metrics; additionally, the statistical significance of model differences was assessed using the Wilcoxon signed-rank test. The findings indicate that the logarithmic model yields more balanced and reliable results in the X/West direction, while the linear model does so in the Y/South direction. Based on this model structure, spatial projections were generated for the 2026–2035 period, and it was observed that the projection bands remained stable despite Monte Carlo-based coefficient uncertainty. In conclusion, the study demonstrates that lake drawdowns in semi-arid closed basins can be monitored in a more interpretable and statistically robust manner using Sentinel-2-based segmentation, centroid kinematics, and cross-validated trend modeling. Full article

Cloud removal is an indispensable preprocessing step in optical remote sensing. Reconstructing cloud-free imagery by combining multi-temporal optical observations with cloud-penetrating synthetic aperture radar (SAR) has become a mainstream approach. However, the existing studies mostly adopt simple composites, such as per-pixel least-cloudy selection or the temporal median, as baselines, and average accuracy metrics over entire scenes; together, these two practices may overstate the true gains of deep-learning methods. This paper proposes a temporal cross-modal cloud removal method (TCM-CR). In a multi-temporal sequence, the acquisition with the lowest cloud fraction retains true surface reflectance at its cloud-free pixels and is itself a high-accuracy baseline. TCM-CR exploits this baseline in two ways. First, on clear and light inputs, cloud-free pixels are taken unchanged from the reference image, so the true reflectance is preserved without loss, independent of training. Second, only cloud-covered pixels receive a bounded correction, in which SAR supplies the surface structure beneath clouds and multi-temporal observations are integrated along time while suppressing heavily clouded acquisitions. Experiments on the SEN12MS-CR-TS dataset show that TCM-CR maintains accuracy on par with the reference image on clear and light samples and improves the peak signal-to-noise ratio on heavy samples by 7.93 dB. In a cross-region experiment where one region is excluded from training entirely and used only for testing, heavy samples still improve by 7.27 dB. Full article

Background and Objectives: Accurate preoperative assessment of lymph node metastasis is essential for surgical staging and treatment planning in endometrial carcinoma. Magnetic resonance imaging (MRI) is widely used for local staging; however, its nodal performance is limited by reliance on morphologic criteria. 18F-FDG PET/CT provides whole-body functional assessment and may support decision-making when sentinel lymph node (SLN) mapping is unavailable. This study compared MRI and 18F-FDG PET/CT for preoperative detection of pelvic and paraaortic nodal metastases across pragmatic histologic risk groups. Materials and Methods: This retrospective diagnostic-accuracy study included 255 consecutive surgically treated patients with histologically confirmed endometrial carcinoma who underwent both preoperative pelvic MRI and whole-body 18F-FDG PET/CT at a tertiary gynecologic oncology center between December 2023 and December 2025. All patients underwent pelvic lymphadenectomy, 111 patients (43.5%) also underwent paraaortic lymphadenectomy, and SLN mapping was not performed. Patients were categorized as Group 1 (Grade 1–2 endometrioid adenocarcinoma) or Group 2 (Grade 3 endometrioid or non-endometrioid/aggressive histology). Diagnostic estimates were reported with 95% confidence intervals, paired comparisons were performed using exact McNemar tests, and multivariable bias-reduced logistic regression was used to assess independent imaging associations with nodal metastasis. Results: Any nodal metastasis was documented in 24 patients (9.4%). 18F-FDG PET/CT showed higher sensitivity than MRI for patient-level nodal detection (91.7%, 95% CI 74.2–97.7 vs. 37.5%, 95% CI 21.2–57.3), with similarly high specificity (97.0%, 95% CI 93.9–98.5 vs. 97.8%, 95% CI 95.0–99.1). The NPV of 18F-FDG PET/CT was 99.1% (95% CI 96.8–99.8), compared with 93.8% (95% CI 90.0–96.2) for MRI. MRI sensitivity was particularly low in Group 1 (18.2%, 95% CI 5.1–47.7), whereas PET/CT retained high sensitivity in both groups, though the perfect performance observed in Group 2 had wide confidence limits. Conclusions: 18F-FDG PET/CT demonstrated superior sensitivity and NPV compared with MRI for preoperative nodal assessment in endometrial carcinoma. These results support the selective use of PET/CT as an adjunctive staging tool in settings without SLN mapping, but they should not be interpreted as evidence that PET/CT can replace histologic nodal staging. Full article

Accurate and transferable land cover mapping from satellite imagery is a prerequisite for national-scale agrienvironmental monitoring and climate change impact assessment. The joint use of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery offers complementary structural and spectral information, yet systematic evaluation of fusion strategies and their geographic transferability remains limited. We trained and tested five U-Net fusion architectures S1-only, S2-only, early (input-level), feature-level (middle), and decision-level (late) alongside a SegFormer-b2 transformer baseline over two German cities (Munich and Berlin) using the Multi-Sensor Land Cover Classification (MSLCC) dataset (single-date 2017 Sentinel-1B/Sentinel-2A acquisitions) at 10 m resolution. Three cross-city transfer protocols (Munich → Berlin, Berlin → Munich, and combined training) quantify model transferability across contrasting urban–rural gradients. Early fusion achieved the highest in-city macro-averaged F1 score among U-Net variants (0.8278), a small but statistically significant improvement over the optical-only baseline (0.8236; patch-level paired bootstrap, $p=0.006$); feature-level (middle, 0.8164) and decision-level (late, 0.8196) fusion were, by contrast, significantly worse than the optical-only baseline ($p<0.001$ and $p=0.030$, respectively), and the SAR-only model (0.6864) trailed substantially. The built-up class was the primary beneficiary of SAR inclusion under early fusion. SegFormer-b2 (0.8214) was numerically close to, but statistically significantly below, the best convolutional configuration ($p=0.005$), and exhibited strong cross-city transfer (0.8632–0.8608 macro-F1), consistent with the geographic invariance conferred by its ImageNet-pretrained encoder. Combined training across both cities improved over the single-direction transfer average by 0.012 macro-F1 points for U-Net and 0.006 points for SegFormer, offering a practical route to national-scale deployment without requiring explicit domain adaptation. Spectral index augmentation (NDVI, NDWI, ExG) and SE channel attention did not significantly improve over plain early fusion when derived from percentile-normalized inputs, with the best variant statistically indistinguishable from the baseline at macro-F1 = 0.8268 ($p=0.365$); the result is attributable to a specific preprocessing dependency: NDVI, NDWI, and ExG are only physically meaningful when computed from calibrated reflectance, whereas here they were derived after scene-level 2nd–98th percentile stretching, which strips the absolute radiometric referencing the indices rely on; practitioners combining spectral indices with percentile-normalized (rather than physically calibrated, e.g., Level-2A surface-reflectance) inputs should expect a similar null result. Full article

Vulvar cancer is a rare gynecologic malignancy with a bimodal age distribution, predominantly affecting older women while demonstrating increasing HPV-related incidence in younger populations. Accurate staging is essential for individualized treatment strategies to optimize outcomes while minimizing morbidity. Positron Emission Tomography (PET) has emerged as an important imaging modality in oncology; however, its precise role in vulvar cancer requires critical evaluation given the limited prospective evidence. This review focuses on the role of ¹⁸F-FDG PET/CT in vulvar cancer based on the literature published to date. Current evidence suggests that PET/CT provides high sensitivity for primary tumor detection and is particularly valuable for nodal and distant staging in locally advanced disease, where it may significantly influence management. However, its limited spatial resolution precludes detection of micrometastases, and it cannot replace sentinel lymph node biopsy for groin staging. PET/CT also contributes to radiotherapy planning through improved target delineation and demonstrates utility in detecting recurrence, although false positives remain a limitation. MRI remains superior for local staging, supporting a complementary multimodality imaging approach. Emerging tracers, including Fibroblast Activation Protein Inhibitor (FAPI), along with artificial intelligence-based radiomics, represent promising but still investigational directions. In conclusion, ¹⁸F-FDG PET/CT is a useful adjunct in vulvar cancer, especially for advanced disease, but its interpretation should be integrated within a multimodal framework due to limited supporting evidence. Full article

Wildfires are significant ecological and environmental disasters, impacting forests, ecosystems, climate stability and human life. The visible-spectrum imagery-based traditional wildfire monitoring system can fail to perform well in the presence of smoke, haze and low lighting. A number of machine learning and deep learning techniques have been proposed, but most of the studies do not provide an integrated Short-Wave Infrared (SWIR) optical-electronic payload framework along with an intelligent optimization technique. The objective of this research is to design an intelligent SWIR-based optical-electronic payload architecture for accurate detection and remote sensing of wildfire and Earth applications via deep learning and optimization techniques. The proposed framework is based on Sentinel-2 SWIR satellite data layers with wildfire and non-wildfire samples. To enhance the quality of the images and the representation of their spectral domain, the following preprocessing operations are carried out: resizing, image normalization, SWIR band extraction, and data augmentation. The following spectral feature extraction techniques are then used: burn area analysis, vegetation stress analysis, and thermal anomaly detection. The framework also incorporates SWIR optical payload design, electronic subsystem development and SWIR InGaAs sensor modeling. Finally, a Hybrid Convolutional Neural Network (CNN)–Residual Network 50 (ResNet50) model optimized by Grey Wolf Optimization (GWO) is used for wildfire classification and hyperparameter tuning. The proposed framework achieved an accuracy of 91.03%, precision of 91.27%, recall of 91.03%, and F1-score of 91.01%. The wildfire detection capability, classification robustness, and convergence performance were enhanced through the integration of SWIR spectral analysis, hybrid deep learning and GWO. The proposed framework offers an effective and trustworthy solution for intelligent wildfire monitoring and Earth remote sensing applications with enhanced spectral sensing and classification performance. Full article

Remote sensing has become a core technology for environmental and climate monitoring, supported by expanding sensor constellations, advanced processing capabilities, and coordination frameworks established by the European Space Agency (ESA), Global Earth Observation System of Systems (GEOSS), and Committee on Earth Observation Satellites (CEOS). Ensuring consistency across missions requires robust geometric and radiometric calibration and validation. However, traditional reliance on ground control points (GCPs) is limited by sparse global coverage, temporal instability, and dependence on surveyed accuracy. While alternative geospatial datasets, including satellite and aerial imagery, Light Detection and Ranging (LiDAR) point clouds, and vector databases, can serve as references, challenges remain in data access, automation, and cross-sensor applicability. This study proposes a generative adversarial network (GAN)-based approach to generate geometrically consistent image chips from vector maps. Two models were trained at 50 cm and 10 m resolution within the ESA-supported Generative Ground Control Point (GenCP) study, using Sentinel-2 and very-high-resolution RGB imagery. The generated GenCP image chips are evaluated using image similarity (radiometric consistency), as well as geometric and model-performance metrics. The results demonstrate their suitability for automated Cal/Val workflows and their potential as scalable, fit-for-purpose reference datasets. Full article

Mediterranean and semi-arid mountain forests are increasingly affected by recurrent drought, wildfire, overgrazing, and anthropogenic pressure, with direct implications for forest sustainability. This study assesses the spatio-temporal dynamics of vegetation cover in the Ouled Yagoub Forest, Khenchela Province, northeastern Algeria, from 1994 to 2025, using GIS and remote sensing. Multi-temporal satellite images, including Landsat data for historical periods and Sentinel-2 data for recent years, were processed to calculate NDVI, classify NDVI-derived vegetation-cover classes, and detect vegetation changes before and after the 2021 wildfire. Vegetation-cover classes were quantified in hectares and percentages, and NDVI change maps were produced for the periods 1994–2000, 2000–2010, 2010–2020, 2020–2021, 2021–2022, 2021–2025, and 1994–2025. Results showed that dense vegetation increased from 14.15% in 1994 to 20.71% in 2020, indicating improved pre-fire vegetation conditions. After the 2021 wildfire, dense vegetation decreased to 17.44% in 2021 and 13.44% in 2022, while very low vegetation increased sharply to 29.79% in 2022. The 2021–2022 period showed the strongest negative vegetation response, with 32.65% of the mapped area classified as vegetation decrease. By 2025, partial recovery was observed, with vegetation increase covering 20.14% of the mapped area between 2021 and 2025. However, low vegetation remained dominant, indicating incomplete and spatially heterogeneous recovery. These findings highlight the usefulness of NDVI-based multi-temporal analysis for monitoring forest degradation, post-fire recovery, and priority areas for restoration planning in semi-arid Mediterranean mountain forests, while also supporting sustainability-oriented forest management in other fire-prone regions with comparable ecological constraints. Full article

Mining-induced land subsidence and geohazards have become increasingly prominent in loess mining areas. Accurate identification of Active Deformation Areas (ADAs) is of great significance for mining safety, ecological protection, and geohazard prevention. The study selected the Binchang mining and used SBAS-InSAR based on Sentinel-1 SAR images from 2020 to 2025 to derive cumulative deformation and velocity. Three ADA identification approaches, including ADAfinder, MCAS-TO, and Light-UNet deep learning method, were quantitatively evaluated using IoU, F1-score, Boundary_F1-score, Precision, Recall, Area Error (%), and comparatively analyzed. ADAfinder identifies subsidence centers but produces fragmented results. MCAS-TO improves boundary continuity but is constrained by a 2% area threshold, while the deep learning method achieves the highest boundary detection accuracy and captures ADAs expansion and merging processes. Spatially, the identified ADAs are mainly distributed in the central and southern parts of the mining area and are highly consistent with underground coal mining faces. Comprehensive analysis of the three ADA identification results reveals the spatiotemporal evolution of mining-induced deformation. The results demonstrate that mining activities are the dominant driving factor controlling the formation and evolution of ADAs. The findings can provide important technical support for geohazard prevention in loess mining areas. Full article

Satellite -derived environmental features can predict county-level life expectancy (LE) across the contiguous United States with a mean absolute error of 1.08 years over two decades, without using any census or sociodemographic inputs. We assembled 61,680 county-year observations across 3084 counties from 2000–2019, integrating features from 11 satellite and gridded data streams. The data streams include the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, Sentinel-1 synthetic aperture radar, Sentinel-2 and Landsat optical imagery, the United States Department of Agriculture (USDA) Cropland Data Layer, the European Commission Joint Research Centre (JRC) Global Surface Water layer, the Copernicus Digital Elevation Model, the European Space Agency Climate Change Initiative (ESA CCI) soil moisture record, and the Food and Agriculture Organization (FAO) gridded livestock densities. After a supervised pruning step that removed low-importance variables, a Random Forest regressor was trained and evaluated using 5-fold cross-validation grouped by county. The grouping places all 20 years of each county exclusively in either the training set or the test set, which prevents spatial information leakage between folds. Coefficient of determination, mean absolute error, and root mean squared error are reported as $R^2=0.631\pm 0.013$, MAE $=1.08\pm 0.02$ years, and RMSE $=1.48\pm 0.04$ years. Moran’s I, a measure of residual spatial autocorrelation, is $0.0988$ ($p=0.001$), which supports geographic generalisation. Multimodal fusion reduces unexplained variance by approximately one-third relative to the strongest single-modality baseline (MODIS land surface temperature alone, $R^2=0.442$). TreeSHAP attribution analysis reveals a feature hierarchy in which nighttime land surface temperature features carry roughly $6.16\times$ the cumulative attribution weight of all daytime channels combined. The model response shows a protective inflection near a minimum overnight temperature of about 7.5 °C. Because all input streams are globally available, the framework is architecturally extensible to regions where civil registration and vital statistics systems are incomplete; however, the trained model and its thresholds require recalibration against local mortality data before application outside the contiguous United States. With that caveat, the approach supports satellite-based monitoring of United Nations Sustainable Development Goal (UN SDG) Target 3.9. Full article