Search Results (8,772)

Ground deformation monitoring is pivotal for enhancing urban resilience and mitigating geohazards. This study presents a synergistic monitoring framework integrating 26 Sentinel-1A (C-band) and 16 LuTan-1 (L-band) SAR scenes acquired between December 2023 and August 2025 to characterize the deformation dynamics in Beijing. Utilizing SBAS-InSAR, we first established a regional deformation baseline using Sentinel-1A observations, identifying critical subsidence and uplift zones in the eastern plains. Subsequently, high-resolution (3 m) LT-1 data were leveraged to achieve refined spatiotemporal characterization of these deformation hotspots. Validation against ground leveling benchmarks confirmed that both satellites yield high accuracy. LuTan-1 (RMSE = 3.810 mm/a) shows slightly better agreement with the ground leveling data than Sentinel-1A (RMSE = 4.853 mm/a). Analysis of the spatiotemporal patterns derived from InSAR revealed that the study area is characterized by widespread gene uplift (averaging ~10 mm/a), interspersed with acute localized subsidence exceeding 40 mm/a. Correlation analysis demonstrates a high spatiotemporal coupling between the extent and rate of surface uplift and groundwater level recovery. To further investigate these dynamics, Terzaghi’s effective stress principle is employed to quantify the contribution of groundwater level fluctuations to the observed surface deformation. A Parametric Harmonic Model was implemented to decouple elastic and trend components, and attribution analysis confirms that the continuous recovery of groundwater levels is the fundamental driver of the regional surface uplift. The inverted elastic skeletal storativity (S_ke), ranging from 1.587 × 10⁻³ to 9.184 × 10⁻³, reveals that regional surface uplift is predominantly driven by the elastic rebound of aquifer systems following groundwater recovery. In contrast, localized subsidence anomalies observed at large-scale engineering construction sites, landfill facilities, major expressway corridors, and high-density residential areas are independent of groundwater fluctuations, instead they are primarily attributed to anthropogenic stressors. This study elucidates a dual-drive mechanism, which comprising macroscopic hydrogeological rebound and localized anthropogenic disturbance, providing a robust scientific basis for differentiated urban hazard management. Full article

Accurate detection of chlorophyll-a (Chl-a) is critical for monitoring water quality in inland waters, where high concentrations of coloured dissolved organic matter (CDOM) complicate retrieval process. Reliable Chl-a estimation depends on the precise determination of the phytoplankton absorption coefficient (a_ph). This study evaluates Chl-a detection from in situ a_ph measurements and assesses the accuracy of phytoplankton absorption retrieval from Sentinel-2/MSI (S2) and Sentinel-3/OLCI (S3) using the Case-2-Regional-Coast-Colour (C2RCC) processor across diverse optical water types (OWTs) in boreal lakes. OWTs were classified based on remote sensing reflectance features, representing Clear, Moderate, Turbid, Very Turbid, and Brown conditions. CDOM absorption strongly influenced the underwater light field, particularly in Brown and Turbid waters. Linear relationships between a_ph and Chl-a were generally strong across OWTs, with improved relationships in the red spectral region (670 nm). Satellite-derived a_pig estimates showed a weak relationship with in situ data (R² = 0.26–0.45). Both sensors overestimated small a_ph values, while S3 underestimated larger ones. S2 underestimated a_ph in Clear and Brown OWTs, with median absolute percentage differences near 100% for all OWTs. These findings emphasize the challenges posed by bio-optical complexity in boreal lakes and highlight the need for OWT-specific algorithms to improve satellite-based absorption and Chl-a retrieval accuracy. Full article

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. Full article

Fine-scale crop type information is essential for agricultural monitoring, irrigation management, and food security assessment. This study mapped three major crops—wheat, corn, and sunflower—in the Hetao Irrigation District, China, using multi-temporal Sentinel-2 optical imagery and Sentinel-1 SAR observations at the parcel scale. A multi-source feature set, including spectral bands, vegetation and red-edge indices, moisture-related variables, radar backscatter coefficients, and derived radar features, was constructed from the full growing season. An LSTM network was used to learn temporal representations of crop phenological dynamics, and the resulting embeddings were then combined with traditional machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for final classification. The results show that the hybrid framework substantially improves classification performance compared with the corresponding non-LSTM classifiers. Among all tested models, XGBoost + LSTM achieved the best performance, with an overall accuracy of 93.61%, a Kappa coefficient of 91.66%, and a mean IoU of 87.41%. The class-wise F1-scores were 85.61% for wheat, 97.22% for corn, and 87.27% for sunflower. Additional experiments further confirmed the advantages of parcel-based aggregation in improving spatial consistency and reducing mixed-field noise. The proposed framework provides a promising parcel-scale workflow for crop type mapping in fragmented irrigation districts, while its transferability across years and regions still requires further validation. Full article

In the context of green energy, the use of lake reeds is becoming an increasingly important factor. Therefore, research into the availability of reeds, determining their area in lakes, predicting the potential biomass volume and calculating the carbon footprint are important. Currently, there have been no significant research results on the availability of reeds and the assessment of the sustainability of reed products in Latvia. However, these aspects are crucial for the development of reed products, as they help to assess their market potential and environmental impact. The main goal of this work is to develop a method for modeling the distribution of lake reeds in order to predict their availability in the future, which would allow assessment of the volume of biomass and its impact on the environment. This research develops an integrated GIS–LCA framework that combines Sentinel-2 satellite data, machine learning-based classification, biomass estimation, and carbon footprint modeling. Using Lake Cirma as a case study, the classification results show that reed stands occupy 2.18–3.51 percent of the lake area in certain years, corresponding to approximately 1158–1861 tons of biomass. The framework enables quantification of harvesting potential while considering ecological constraints that limit annual extraction to approximately 50% of total biomass. The proposed GIS–LCA framework provides a replicable methodology for assessing reed biomass availability and environmental performance across lake ecosystems. It supports evidence-based decision-making for sustainable reed resource management and contributes to the development of low-carbon bioeconomy pathways in line with EU climate and bioeconomy strategies. Full article

Unmanned aerial vehicles (UAVs) have become an important component of multi-sensor remote sensing frameworks for post-fire forest monitoring because they provide ultra-high-resolution imagery for evaluating fine-scale vegetation response. This study assessed early-stage post-fire burn severity and forest health condition in a natural mixed forest affected by the 2024 wildfire in Nanyo, Yamagata, northeastern Japan. Burn severity was quantified using the differenced Normalized Burn Ratio (dNBR) derived from Sentinel-2 imagery acquired five months after the fire (October 2024). High-resolution UAV RGB orthomosaics and field surveys were used to classify trees into healthy, damaged, and dead categories. Mean plot-level burn severity was estimated using a weighted midpoint dNBR approach, and the tree mortality rate was calculated from plot-based tree counts. The results showed that low and moderate–low burn severity classes dominated most plots, with mean dNBR values ranging from 0.085 to 0.386. UAV-based interpretation revealed substantial variability in tree health condition among plots. In 2024, fire effects were expressed mainly as canopy damage rather than immediate stand-level mortality. Mortality rates ranged from 14.9% to 58.6%, and some higher-severity plots contained greater damage. Overall, Sentinel-2 dNBR captured landscape-scale burn severity patterns, whereas UAV imagery improved interpretation of fine-scale health variability in heterogeneous burned forests. Full article

Accurately mapping agricultural land use in fragmented hilly and mountainous areas is crucial for resource management but is severely challenged by spatial heterogeneity. While high-resolution (HR) images excel at delineating fine parcel boundaries, their limited spectral and temporal information often leads to spectral confusion among diverse agricultural types. To address this limitation, this study proposes a novel spatiotemporal feature-driven geographical zoning method integrating vegetation phenology, topography, and human activity. This zoning strategy decouples the complex global classification task into relatively simple local problems, providing explicit geoscientific constraints for subsequent classification. The proposed method was validated by classifying plain open-field croplands, sloping croplands, terraces, and greenhouses in the hilly and mountainous areas of Beijing using 2 m resolution satellite images. Compared to traditional global classification methods, the proposed zoning-based method increased the overall accuracy from 84.81% to 90.81%, the Kappa coefficient from 0.74 to 0.85, and the Intersection over Union (IoU) from 77.85% to 90.85%. The advantages of geographic zoning were particularly evident in mitigating spatial heterogeneity and enhancing boundary precision. These findings indicate that integrating dynamic geographical zoning as a priori knowledge successfully bridges the gap between HR spatial details and environmental contexts, offering a robust solution for mapping fragmented agricultural landscapes. Full article

Neuroblastoma remains a formidable pediatric malignancy characterized by profound metabolic plasticity and limited therapeutic responsiveness in high-risk disease. Emerging evidence positions the interplay between Reactive Oxygen Species (ROS) and the metabolic sentinel AMP-activated protein kinase (AMPK) as a critical regulator of tumor metabolic stress and apoptotic susceptibility, with additional implications in the systemic pathology of Cancer Cachexia. Building on our previous work demonstrating that 1,1-Diethoxyethane (1,1-DEE; Biosacetalin), a volatile aroma compound inhibits mitochondrial complex I, induces ROS production, and activates AMPK-PGC1α-mediated mitochondrial biogenesis accompanying enhancement of aerobic respiration, leading to anti-Warburg effect. We identify 1,1-DEE as a previously unrecognized metabolic modulator with potent antitumor activity. 1,1-DEE triggers ROS-induced AMPK activation, leading to apoptotic elimination of neuroblastoma cells (SH-SY5Y), robust suppression of tumor growth, and significant prolongation of survival (median survival 77 days) in tumor-bearing NSG mice. Strikingly, 1,1-DEE simultaneously alleviates cancer-associated cachexia by preserving body weight. Mechanistically, our findings reveal a ROS–AMPK–centered signaling axis through which 1,1-DEE integrates tumor-selective cytotoxicity with systemic metabolic protection, highlighting a unified therapeutic strategy for targeting both tumor progression and cachexia in neuroblastoma. Full article

Conserved lands play a central role in sustaining ecological functions within working agricultural regions, yet their capacity to maintain wetland conditions varies widely depending on hydrologic persistence and seasonal dynamics. This study assesses the hydrologic performance of Nebraska’s major conservation programs using multi-year Sentinel-2 satellite observations spanning from 2018 to 2024. Five land-protection categories were evaluated: the Wetlands Reserve Program (WRP), Wildlife Management Areas (WMAs), Waterfowl Production Areas (WPAs), the Conservation Reserve Program (CRP), and additional protected lands mapped in the Protected Areas Database of the United States (PAD-US). To capture hydrologic dynamics across scales, we quantified parcel-level inundation percentages alongside program-level wetness metrics that represent cumulative surface-water extent. Lands enrolled in WRP and WPA generally exhibited higher inundation levels at the 0% threshold across annual and seasonal periods, with variability across programs, reflecting their role in wetland restoration and habitat provision. WMAs showed greater seasonal variability but retained water under higher persistence thresholds (≥25% and ≥50%), underscoring their importance in maintaining semi-permanent wetland conditions during drier periods. Wetland-associated CRP lands provide essential short-duration wetness that supports regional hydrologic connectivity across working agricultural landscapes. Similar seasonal patterns were observed across other protected lands, which generally contributed to episodic surface water rather than long-term hydrologic storage. Seasonal analyses highlighted strong intra-annual variability driven by snowmelt, precipitation regimes, and evapotranspiration. Collectively, the results demonstrate substantial differences in hydrologic function among conservation programs and provide an empirical basis for prioritizing investments toward lands that most effectively sustain wetland habitats and water-quality benefits. Full article

Coastal alluvial plains underlain by unconsolidated deposits are prone to land subsidence, a geohazard that can damage infrastructure and alter drainage patterns. One such example is the Venetian–Friulian coastal plain (NE Italy), where natural sediment compaction and anthropogenic activities have led to ground deformation across multiple zones. From this perspective, this study presents a 30-year analysis of land subsidence across the Venetian–Friulian plain, particularly highlighting municipalities such as Portogruaro, Concordia Sagittaria, San Stino di Livenza, Eraclea, and Caorle. The dataset comprises multi-source SAR data from ERS, Envisat, COSMO-SkyMed (CSK), Sentinel-1, and the European Ground Motion Service (EGMS), covering the period from 1992 to 2021. The study integrates multi-platform SAR observations with ADAFinder-based extraction of Active Deformation Areas (ADAs), data quality evaluation using the Quality Index (QI), building-scale analysis based on LOS-derived vertical displacement time series, and orthophotos to confirm the building’s presence and evolution. By using the adopted extraction thresholds, a total of 57, 16, 83, 33, and 72 ADAs were identified from the ERS, ENVISAT, COSMO-SkyMed, Sentinel-1, and EGMS datasets, respectively. The result suggests that the strongest deformation occurred during the earlier observation periods in Zones 1 to 3, then progressively stabilized, whereas some parts of Zone 4 remained active and showed renewed deformation during the later periods. The research highlights the importance of conducting long-term analysis using multi-platform interferometric datasets to refine and personalize outcomes in geohazard monitoring. The findings from this research offer invaluable insights into the ongoing surveillance of geohazards, which are progressively related to urban development and planning. Full article

The carbonate massifs of the southern Italian Apennines host extensive karst aquifers, which represent the principal drinking water resources. This study focuses on the Dragone Plain polje, a vast closed karst depression located in the main recharge sector of the Terminio–Tuoro carbonate massif. The polje drains a ~55 km² endorheic catchment and may be flooded during the cold and wet season, forming a temporary lake. We employed continuous hydroclimatic time series (rainfall, groundwater level, spring discharge, and river level) together with sparse Sentinel-2 true color satellite images for the period 2020–2024 to analyze the flooding process in the polje and its hydraulic connection with the saturated zone of the karst aquifer. Results indicate that lake formation depends on the balance among soil moisture, rainfall intensity, and runoff development, which were modeled on a daily scale. Daily recharge was also estimated and compared with groundwater level time series from the deep karst aquifer. The modeling was integrated with cross-correlation analysis of the time series, providing insights into the propagation of precipitation pulses through the hydrogeological system. This case study represents an important example for understanding the relationship between karst polje hydrological functioning and climate in a Mediterranean area. Full article

Crop planting structure adjustments in irrigated agricultural regions alter irrigation and drainage regimes, with potential consequences for regional surface water dynamics. However, the nature and scale dependence of these linkages remain insufficiently understood. This study investigates the spatiotemporal dynamics of crop planting structure and surface water bodies in the Ningxia Plain from 2004 to 2023, and systematically quantifies their scale-dependent coupling mechanisms. Annual crop maps were generated using a Random Forest classifier (Sentinel-2, 2019–2023) and a Transformer-based model applied to multi-source satellite imagery (2004–2018). Surface water bodies were derived from long-term remote sensing datasets covering the full study period. Results show that the agricultural system underwent a pronounced transition toward maize dominance. Maize area expanded by 50.8%, whereas wheat and rice declined by 74.3% and 44.6%, respectively. Crop diversity also decreased, with the Shannon Diversity Index declining from 1.41 to 1.06 in 2023, indicating progressive system simplification. Meanwhile, surface water bodies exhibited a sustained downward trend, decreasing at an average rate of −5.32 km² per year after 2013 and reaching a minimum in 2022. The Yellow River water surface area also contracted by 14.41% (p = 0.001), indicating a basin-scale reduction in surface water extent. Lake classification results reveal strong scale-dependent hydrological responses. Small lakes (≤18 ha), accounting for 73.2% of lake numbers, are primarily controlled by local irrigation–drainage processes. Medium lakes (18–80 ha) are influenced by both anthropogenic regulation and natural variability. Large lakes (>80 ha), although representing only 4.9% of lake numbers but 62.9% of total water area, are mainly sustained by climatic variability and ecological water supplementation. Principal component analysis explains 84.44% of total variance, highlighting agricultural structural change and irrigation–drainage dynamics as key system drivers. Correlation analysis further reveals strong climate sensitivity of large lakes and the Yellow River (ρ = 0.50, p = 0.031), while small lakes are predominantly influenced by agricultural drainage processes. Overall, crop planting structure affects regional water dynamics through scale-dependent processes, with maize expansion altering irrigation and diversion patterns and local irrigation–drainage processes controlling small water bodies. Full article

Accurate identification of cultivated land planting types is essential for agricultural resource management and national food security. Traditional optical remote sensing approaches are susceptible to weather interference in cloudy regions, making continuous crop growth monitoring challenging to achieve. To address this limitation, this study proposes a crop classification framework based on time-series Sentinel-1A SAR imagery combined with Recurrent Neural Networks (RNN), using Chongming Island, Shanghai as the experimental area. The framework integrates backscattering coefficients (VV, VH, VV/VH ratio) with polarimetric decomposition parameters (entropy H, scattering angle alpha, anisotropy A) as multi-dimensional temporal input features, and employs decision-level voting to obtain plot-level classification results. Experiments on three classification tasks (Rice versus Non-Rice, Wheat versus Non-Wheat, and multi-class rotation patterns) demonstrate that the proposed method achieves pixel-level accuracies of 99.72%, 99.60%, and 98.39% respectively using the six-dimensional BSPD model, with plot-level F1 scores exceeding 0.990 across all tasks. The fusion of polarimetric decomposition features reduces classification errors by up to 70% compared with backscattering-only features, particularly improving discrimination of phenologically overlapping crop categories. These results confirm that multi-dimensional temporal features extracted from dense time-series SAR imagery significantly enhance crop classification accuracy in all-weather conditions. Full article

This study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to investigate surface deformation dynamics in the hyper-arid Eastern Province of Saudi Arabia, with emphasis on quantifying sand dune migration and identifying areas susceptible to wind erosion. Utilizing Sentinel-1 SAR data and the MintPy toolbox, ground deformation was quantified with millimeter-scale precision. Results reveal significant subsidence, up to 15 cm/year in landfills, linked to waste compaction and groundwater depletion. Localized uplift of ~4 cm/year on northern peripheries is directly attributed to aeolian sand accumulation from seasonal Shamal winds, providing quantitative evidence of dune migration. While direct measurement of wind erosion (net deflation) remains challenging due to the dominance of depositional signals and the spatial heterogeneity of erosion processes, areas of potential erosion are inferred from negative displacement patterns outside landfill zones and from coherence characteristics indicative of surface instability. The integration of SBAS-InSAR with GPS and ERA5 wind reanalysis resolves the combined influence of aeolian deposition, hydrogeological changes, and anthropogenic activity, offering insights into both components of aeolian dynamics and a replicable model for sustainable land management in arid environments. Full article

C. burnetii (Cb) is an obligate intracellular bacterial pathogen that replicates within alveolar macrophages following aerosol infection. Unlike most intracellular bacteria, Cb establishes a lysosome-derived replicative niche (Coxiella-containing vacuole or CCV) through the action of its Type IVB secretion system (T4BSS). This system translocates a large repertoire of effector proteins into the host cytoplasm after phagosome acidification. These effectors interfere with diverse signaling pathways to co-opt host processes, such as vesicle trafficking, ubiquitylation, gene expression and lipid metabolism, promoting pathogen survival without triggering robust proinflammatory signaling or host cell death pathways. This effector-triggered immune silencing is particularly unique given the central role of macrophages as innate immune sentinels. In this review, we examine Cb T4BSS effectors that have been characterized as central determinants of innate immunity modulation. We discuss innate immune sensing pathways potentially engaged during infection, including Toll-like receptors, NOD-like receptors, RIG-I-like receptors, inflammasomes, and interferon signaling pathways, and highlight evidence indicating that these pathways are actively suppressed. Emphasis is placed on effector-mediated regulation of NF-κB signaling, type I interferon responses, and inflammasome activation. Finally, we address unresolved questions related to effector-triggered immunity, redundancy in immune suppression, and discrepancies between in vitro and in vivo infection models. Full article