Search Results (1,468)

The Amazon Basin and its rivers play a vital role in regional biodiversity, the carbon cycle, and socio-economic security. Through erosion and deposition, river planforms change over time, affecting local infrastructure, food security, and changes to ecosystems. Long-term monitoring is essential for observing these dynamics. Synthetic Aperture Radar (SAR) provides a method to consistently map river planform dynamics across large areas because it is largely independent of atmospheric conditions. This study presents an approach for deriving river planform metrics across the entire Amazon Basin using Sentinel-1 C-band SAR data. This approach followed three main steps: water mask generation, validation of the data and river metrics extraction. Sentinel-1 imagery from 2017 to 2025 was composited into quarterly mean images, after which Otsu thresholding was applied to derive water classifications. Additional post-processing steps were applied to reduce terrain- and seasonal effects. The final water masks were divided into water-change classes, validated using stratified sampling and achieved an overall accuracy of 98.5%. Quarterly river planform metrics, including sinuosity, mean channel width and migration rate, were derived using channel centerline extraction, but due to a lack of in situ validation data the river metric values have not been validated. The resulting time series provide insights into how river planform changes across all Amazon sub-basins from 2017 to 2025 can be monitored using SAR-based methods. The results reveal spatial differences in river dynamics between tributaries, mostly depending on flow pattern, up- or downstream path and location in the upper, middle or lower Amazon Basin. These findings demonstrate the potential of SAR time series for monitoring large-scale river planform dynamics. Full article

This work aims to determine whether pre-seismic ground dislocations extracted from interferometric satellite synthetic aperture radar (InSAR) imagery contain predictive information for distinguishing between two magnitude classes of an upcoming earthquake, assuming that an earthquake’s occurrence is already imminent. For this reason, twenty-three earthquakes of various magnitudes that occurred in Greece during the year 2020 were analyzed using SAR data to construct a time-series of five six-day InSAR images for each earthquake, spanning a total 24-day period before the earthquake. For each earthquake, four ground dislocation images covering the area around each earthquake were derived from the interferograms, each showing the dislocation during a six-day time interval. Images showing the total ground dislocation during the entire 24-day period before the earthquake were also produced by fusing the four images. Three machine learning classifiers were used to relate the earthquake magnitude class to pre-seismic ground dislocations. High accuracies were obtained with both support vector machine (SVM) and random forest (RF), yet they were highly dependent on the type of images used. In a subsequent analysis, five regression models were applied to estimate the earthquakes’ epicenters from dislocation images. The results reveal that the proposed approach is able to achieve well-localized epicentral area prediction, indicating the potential predictive value of this tool for seismic hazard assessment and emergency planning. Full article

Introduction: Vaccination against COVID-19 has been crucial in controlling the pandemic. While side effects are typically mild, rare neurological complications have been reported. This is a case series of ten patients who reported of persistent fasciculations after COVID-19 vaccination. Methods: We describe the clinical presentation and diagnostic work-up of ten patients with new-onset fasciculations in temporal proximity to COVID-19 vaccination. Patients with prior SARS-CoV-2 infection or known alternative causes of fasciculations were excluded. Routine clinical data, including neurological examination, laboratory results, and electrophysiology (electromyography and nerve conduction studies), were analyzed. Results: Ten patients (5 male, 5 female; mean age 42.4 years) reported fasciculations beginning within 6 h to 13 days post-vaccination and persisting for 2–12 months at the time of presentation. Fasciculations were accompanied by additional symptoms such as paresthesia and fatigue. Laboratory results were mostly unremarkable; two patients had positive myositis antibodies without clinical correlates. Electrophysiology was unremarkable in six patients, while fasciculation potentials were detected in four patients. Nine were diagnosed with probable benign fasciculation syndrome (BFS), and one met diagnostic criteria for amyotrophic lateral sclerosis (ALS). Discussion: In this small, retrospective case series, most cases of post-vaccination fasciculations were benign and compatible with BFS. Whether BFS onset was causally linked to vaccination or due to a nocebo effect remains unclear. One patient was diagnosed with ALS, though a causal link remains speculative given the study’s limitations and rarity of similar reports. Larger, prospective studies are needed to validate these observations and explore underlying pathophysiological mechanisms. Full article

Slope stability in open-pit mining is not a static condition but evolves continuously as excavation progresses and geomechanical conditions change. In this study, an integrated approach combining ground-based radar monitoring, satellite-based InSAR time-series analysis, and numerical stability modeling was applied to evaluate slope behavior in a large-scale open-pit copper mine with complex geological and structural characteristics. Radar data revealed progressive and episodic deformation concentrated in specific slope sectors, while InSAR observations showed that deformation continued at lower rates after the main movement phase, providing a longer-term perspective of slope response. Stability analyses using limit equilibrium and finite element methods indicate that the slope operates close to a limit equilibrium condition, particularly under saturated scenarios where factors of safety approach critical levels and strain localization becomes more pronounced. The results show a clear link between observed deformation patterns and calculated stability conditions, with structural discontinuities and groundwater playing a dominant role in controlling slope behavior. Based on these findings, an integrated workflow is proposed that links monitoring data with stability assessment, enabling the identification of critical zones and supporting the evaluation of slope conditions during ongoing mining operations. This approach contributes to more reliable decision-making and supports safer and more sustainable open-pit mining practices. Full article

Sustainable small hydropower (SHP) is central to the clean-energy transition of mountainous Central Asia, yet its long-term reliability depends on a rapidly changing cryosphere. Winter river-ice dynamics—an underappreciated control on run-of-river generation—remain poorly characterized owing to the collapse of in situ hydrometeorological networks since 1991. We use a 112-month Sentinel-1 C-band SAR time series (February 2017–May 2026) over a 5320 km² headwater catchment of the Chu River basin, northern Tien Shan, Kyrgyzstan, to quantify river-ice phenology at 20 m resolution using a per-pixel summer-baseline anomaly approach. Mid-winter (December–February) ice cover declined significantly at −0.51%·yr⁻¹ (p = 0.013; Mann–Kendall p = 0.029), with the 2026 winter recording an unprecedented 2.6–2.8 σ departure from the 2017–2025 climatology. Contrasting the cold 2022 and warm 2026 winters revealed bidirectional climate sensitivity—early breakup versus persistent thin ice—posing distinct SHP hazards. ERA5-Land reanalysis (1992–2026) showed significant winter warming with no precipitation or snowfall trend, indicating thermally forced ice decline. Interpreted within a conceptual Peak Water scenario, this signals a closing window of opportunity for SHP generation, with direct relevance to sustainable water–energy management and the UN Sustainable Development Goals (SDG 7; SDG 13). Our results provide the first decadal, satellite-based evidence of river-ice loss for Central Asian mountain rivers and a transferable monitoring framework to support climate-resilient, sustainable hydropower planning in ungauged basins. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

Synthetic aperture radar (SAR) can acquire navigation data to correct inertial navigation system (INS) errors even under global navigation satellite system (GNSS)-denied conditions. However, when unmanned aerial vehicles (UAVs) may deactivate the SAR system to maintain radio silence, or the SAR sensor may be subjected to transient interference, the INS/SAR integrated navigation system transitions to degraded scenarios without SAR navigation data. Furthermore, the irregular sampling characteristics of SAR navigation data pose significant challenges to the localization performance of the INS/SAR integrated navigation system. In order to address the above challenges faced by UAVs, we propose a liquid time-constant (LTC) network-enhanced INS/SAR integrated localization method. The method adopts a loosely coupled integration strategy with training and prediction modes. During training, an LTC-assisted localization prediction network (LTC-ALPN) is designed to model input–output relationships using prior flight data while explicitly accounting for the non-uniform temporal sampling characteristics of SAR measurements. In prediction mode, the trained LTC-ALPN forecasts missing SAR navigation information, which is subsequently fused with INS outputs via a Kalman filter to maintain high-precision positioning during SAR outages. Experimental results demonstrate that, compared to pure INS localization in degraded scenarios, the proposed method reduces northward error MAE and RMSE by approximately 92.8% and 93.9% and eastward error MAE and RMSE by 54.1% and 67.1%. Against suboptimal network baselines, further improvements of 50.8%/38.1% (north) and 17.1%/16.7% (east) in MAE/RMSE were achieved. Full article

Background and Objectives: The SARS-CoV-2 pandemic disrupted oral and maxillofacial surgery (OMS) services worldwide because of the high aerosol-generating nature of head-and-neck procedures, restricted access to elective dental care, and systemic reallocation of hospital resources. Continuous longitudinal multi-year data covering both the pandemic and the post-pandemic phases from regional Romanian (and more broadly central and southeastern European) emergency centers remain scarce. We aimed to quantify the impact of the pandemic on OMS activity in a large Romanian regional referral center and to evaluate post-pandemic resilience. Materials and Methods: We conducted a retrospective single-center study of all inpatient admissions to the OMS Clinic of a tertiary emergency hospital in western Romania between 1 January 2018 and 31 December 2024. Three periods were pre-specified: pre-pandemic (2018–2019), pandemic (2020–2022) and post-pandemic (2023–2024). A Newey–West segmented interrupted-time-series (ITS) regression and a negative-binomial monthly count model with Fourier seasonality were fitted; length of hospital stay was further analyzed with a multivariable gamma-log generalized linear model adjusted for age, sex, county, primary ICD-10 chapter and total ICD-10 codes. Variables analyzed included case volume, demographics, primary and secondary ICD-10 diagnoses, length of hospital stay (LOS), case complexity (total ICD-10 codes per admission) and in-hospital mortality. Results: A total of 11,628 inpatient admissions corresponding to 8084 unique patients (56.5% male; mean age 52.2 ± 19.2 years) were analyzed. Compared with the pre-pandemic baseline (mean 2037 admissions/year), annual volume dropped by 45.1% in 2020, 44.0% in 2021 and 32.3% in 2022, with a nadir of −76% during the first state of emergency (April 2020; n = 34 admissions). Recovery was rapid; 2024 exceeded the pre-pandemic baseline by +10.1% on raw counts and by +16.2% on admissions per 100,000 catchment population using year-specific INS denominators. The segmented ITS regression confirmed an immediate level drop of −114.2 admissions/month in March 2020 (95% CI −133.1 to −95.3; p < 0.001) and a positive post-intervention slope of +2.06 admissions/month (95% CI 1.23–2.88; p < 0.001), with observed monthly volume returning to the counterfactual projection by October 2023. The case mix shifted significantly (χ² = 406.9, p < 0.0001); elective benign neoplasm admissions were reduced from 7.2% to 2.0%, while neoplasms of uncertain behavior nearly doubled from 15.7% to 27.5%. Case complexity increased during the pandemic (mean ICD codes 4.08 ± 2.42 vs. 3.44 ± 2.30; p < 0.001); after exclusion of administrative codes (whole Z chapter and U07.x), the difference attenuated to 3.34 vs. 3.17 codes (still p < 0.001 by Kruskal–Wallis), indicating that the largest portion of the unadjusted increase was driven by the new mandatory pre-admission SARS-CoV-2 screening code Z11.5 rather than true clinical complexity. Notably, the clinically interpretable proxy R63.3 (feeding difficulty) independently rose from 41.5% to 53.1%. The crude median LOS did not differ between the pre-pandemic and pandemic periods (3.07 vs. 3.06 d; p = 0.19) and dropped significantly post-pandemic (2.22 d; p < 0.001); however, after multivariable adjustment for case mix, age, sex, county and code count, the LOS was 15.7% shorter during the pandemic (adjusted ratio 0.84, 95% CI 0.82–0.87; p < 0.001) and 22.8% shorter post-pandemic (adjusted ratio 0.77, 95% CI 0.75–0.80; p < 0.001) relative to baseline. Conclusions: The pandemic caused a severe but transient contraction of OMS activity accompanied by increased case complexity and a marked shift away from elective surgery. Inpatient volume returned to and exceeded the pre-pandemic baseline by 2024. These results support the value of standing pandemic-preparedness protocols, sustained access to preventive dental care, and integrated tele-triage pathways for future public-health crises. Full article

Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions. Full article

Addressing the critical challenges of insufficient precision in landslide hazard identification and the lack of dynamic stability assessment for high-steep slopes in open-pit mines, this study innovatively proposes an integrated technical framework that deeply fuses time-series Interferometric Synthetic Aperture Radar (InSAR) with machine learning (ML), achieving an intelligent analysis framework that integrates deformation monitoring and stability assessment for open-pit mine landslide hazards. The main contributions include: (1) overcoming the limitations of InSAR technology in low-coherence areas, an improved Small Baseline Subset InSAR (SBAS-InSAR) algorithm was adopted to extract slope deformation, increasing the monitoring point density on complex rock slopes by a factor of 2.18, obtaining high-precision deformation fields, and significantly enhancing the deformation capture capability of high-steep slopes; (2) a new paradigm of dynamic-static multi-factor coupled stability assessment was proposed, which deeply fuses time-series InSAR deformation characteristics with multi-source heterogeneous data, including geological, mining, and environmental factors, employing a dual-model collaborative strategy of Random Forest (RF) and XGBoost achieving an Area Under the Curve (AUC) exceeding 0.88, with the InSAR dynamic factor contributing the highest importance, thereby validating the core role of dynamic monitoring data in stability assessment. The empirical study at a large open-pit mine in northern China demonstrates that high- and very-high-risk zones are precisely localized to specific benches, providing an operational technical support system for mine safety production and offering significant demonstration value for promoting the deep application of InSAR technology in the field of mine disaster early warning. Full article

Accurate time series forecasting is fundamental to geohazard early warning, yet remains a major challenge. Conventional in situ geotechnical monitoring remains costly and spatially constrained, whereas deep learning applied to remote sensing data has become increasingly prevalent but often suffers from opacity of model decision-making. To address this issue, we propose a Transformer-based forecasting framework, namely PatchTST-Fusion, adapted for multivariate Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time series. The framework integrates model interpretability analysis through TimeSHAP, providing temporal and feature-level attributions across the input sequence. Landslide deformation time series are first derived from Copernicus Sentinel-1 SAR data. Variational Mode Decomposition is then applied to decompose the non-linear signals into trend, seasonal, and noise components. The denoised displacement series are modeled and forecast using the proposed PatchTST-Fusion, which incorporates rainfall and reservoir water level fluctuations as feature-level drivers. Application to the Daping landslide cluster in the Three Gorges Reservoir Area in China demonstrates that our method captures both the long-term and transient non-linear coupling between deformation and its triggers, surpassing state-of-the-art models including CNN-BiGRU-Attention, Informer and original PatchTST with 7–55% improvements in MAE and 10–52% improvements in RMSE. Beyond predictive gains, feature attribution of environmental triggers via TimeSHAP reveals that rainfall and reservoir regulation exert temporally distinct influences on slope kinematics, with high relative importance concentrated in specific periods and characteristic lagged responses. This interpretable framework provides both enhanced forecasting accuracy and process-based insights, offering a broadly applicable tool for landslide early warning in reservoir regions. Full article

Surface deformation induced by underground coal mining shows a clear time-lag effect, with persistent residual deformation in old goafs under strip pillar mining conditions, drawing significant research attention. This study focuses on the Gucheng mining area, where 210 Sentinel-1A SAR images (May 2017–January 2025) were processed using SBAS-InSAR to derive 7.5 years of time-series surface deformation. Based on these results, five strip pillar mining panels with different cessation times were selected. Through comparative analysis, a time-progressive sequence was constructed to identify post-mining residual deformation and stage-wise stabilization characteristics, and to reveal long-term deformation responses occurring years after cessation, thereby reconstructing the long-term evolution of surface deformation in old goafs. Furthermore, a stacking ensemble prediction model was developed to predict subsidence trends at representative feature points. The results indicate that subsidence mainly ranges from −20 to −10 mm/a, with a maximum of approximately −64 mm/a and cumulative subsidence of about −515 mm. Surface deformation follows a stage-wise evolution pattern of “residual subsidence—stage-wise stabilization—secondary subsidence—deformation stabilization”, with durations of approximately 2, 2, and 14 years, respectively, and overall stabilization occurring after approximately 18 years. The predicted results from the stacking model are highly consistent with the SBAS-InSAR monitoring data and can reliably describe the evolution trend of surface subsidence. The findings provide important evidence for understanding long-term surface deformation in old goafs of strip pillar mining areas. Full article

In alpine mountain regions, significant seasonal surface changes reduce InSAR coherence over long time spans, hindering geohazard identification. This study proposes a method for geohazard detection based on InSAR seasonal coherence variation. First, time-series interferograms and coherence maps are generated from Sentinel-1 imagery. Each year is then partitioned into summer, transition, and winter seasons by analyzing the spatial migration of high-coherence zones. Interferometric pairs from the transition season are further screened and reassigned to summer or winter groups according to their coherence characteristics. Stacking-InSAR is applied separately to the summer and winter datasets to derive seasonal deformation rates; long-temporal-baseline pairs (60–120 days) that maintain sufficient coherence are selectively incorporated to improve the detectability of slow-moving slopes. Finally, geohazards are identified by combining the summer and winter deformation results. Applied in the eastern Himalayan syntaxis, the method showed that less than 19% of geohazards were detectable in both seasons, indicating seasonal variations in geohazard activity. Moreover, it identified approximately 29% more geohazards on average than traditional Stacking-InSAR using all interferograms. Thus, the proposed approach enables more accurate and effective geohazard detection in cold mountains, supporting disaster prevention and mitigation. Full article

Highway slope instability under coupled traffic and environmental loading poses critical threats to transportation safety in mountainous regions, where dynamic vehicular forces interact with complex geological conditions in ways that single-modality monitoring cannot fully resolve. This study proposes MMDF-DEWS, a multi-modal data fusion and deep learning-based early-warning system that, for the first time, treats quantified traffic-loading parameters as a first-class input modality alongside Interferometric Synthetic Aperture Radar (InSAR) displacement, Global Navigation Satellite System (GNSS) measurements, and embedded geotechnical sensor outputs. A hybrid Transformer–bidirectional LSTM backbone with hierarchical attention-guided fusion enables the model to capture both long-range temporal deformation trends and short-term dynamic responses triggered by heavy-vehicle passage. To guard against over-fitting on a limited number of instability events, we adopt chronological training/validation/test partitioning, five-fold cross-validation for hyper-parameter selection, stratified focal-loss training, and cross-dataset evaluation on two independent public benchmarks: the Three Gorges Reservoir Area Landslide Monitoring Dataset (TGRA-LMD) and the European Ground Motion Service Sentinel-1 (EGMS-S1) dataset. The framework outperforms six state-of-the-art baselines by 4.7–11.2% in F1-score, and ablation studies confirm that the explicit inclusion of traffic-loading features alone improves Warning-class recall by 6.3 percentage points, demonstrating a direct and physically grounded link between cyclic vehicular loading and slope-state prediction. The system satisfies operationally relevant engineering targets for warning lead time and false-alarm rate, and provides interpretable attention maps suitable for transportation-authority decision support. Full article

Interpreting complex, non-linear Interferometric Synthetic Aperture Radar (InSAR) displacement time-series data for infrastructure risk assessment remains a significant geotechnical challenge. This is particularly evident in regions with established road and railway infrastructures, where the primary objective is monitoring the entire network to ensure safety and operational continuity. Because landslide displacement is a highly complex process affected by a combination of internal geological conditions and external triggers, time-series data inherently encode non-linear trends and periodic fluctuations. To address this, a data-driven framework utilizing a sliding-window transformation to engineer temporal-kinematic features is proposed, providing a broader framework for the contextualization of slope stability assessment from a network perspective. This is paired with Principal Component Analysis (PCA) for dimensionality reduction and evaluated across four unsupervised architectures: K-means, Self-Organising Maps (SOMs), Hybrid SOM-K-means, and PCA-K-means. The comparative evaluation reveals that the PCA-K-means pipeline performed best, offering a highly efficient and scalable workflow. The analysis revealed that the optimized PCA-K-means architecture successfully captured 79.20% of the kinematic variance across the first two principal components. Furthermore, it achieved a robust Between-Cluster-to-Total-Sum-of-Squares (BCSS/TSS) ratio of 71.70%, an optimal Silhouette Score of 0.320, and a low Quantisation Error (QE) of 0.90, demonstrating superior spatial separation and geometric accuracy compared to traditional heuristic methods. When cross-validated against static topographic susceptibility models, the dynamic kinematic clusters exhibited a 23% spatial convergence at the polar bounds of risk, successfully grounding the algorithm’s predictions in physically verified geomorphological features. Relying on the statistical volatility of displacements, this optimal model successfully partitioned the data into five distinct geotechnical risk classes, ranging from stable (Class A) to extreme risk (Class E). The results demonstrate that the developed dynamic framework provides a highly reliable, actionable tool for proactive, large-scale slope stability and infrastructure risk assessment. Full article