Search Results (7,496)

Digital elevation model (DEM) generation using Interferometric Synthetic Aperture Radar (InSAR) in mountainous environments encounters challenges including signal acquisition difficulties, decorrelation, and highly variable topography. To address these challenges, we propose a novel approach termed maximum likelihood curved surface estimation (MLCSE), utilizing multi-baseline InSAR to enhance DEM accuracy in mountainous regions. First, multi-baseline InSAR with Sentinel-1 images is employed to acquire more accurate interferometric phases. Second, two strategies are implemented to improve maximum likelihood elevation estimation, which is particularly susceptible to topographic relief and decorrelation. These strategies include replacing fixed neighborhood size with adaptive neighborhood size selection and estimating parameters of the maximum likelihood local curved surface. Finally, the mean error of the MLCSE DEM results and the proportion of errors less than 10 m are 7.89 m and 70.32%, respectively. The results demonstrate that MLCSE surpasses other InSAR methods, achieving higher elevation estimation accuracy. MLCSE exhibits stable performance across the study areas, reducing elevation errors in hilly, mountainous, and alpine regions. Additionally, hydrological analysis of the elevation results reveals that MLCSE, using the adaptive neighborhood size selection strategy, outperforms other methods in both visual inspection and quantitative comparisons. Moreover, the elevation accuracy achieved by MLCSE meets the standards of the American DTED-2, the Level 2 standard of the 1:50,000 DEM (Mountain), and the Level 1 standard of the 1:50,000 DEM (alpine region) for spatial resolution and height accuracy. Full article

Under increasingly stringent global policies aimed at reducing emissions from shipping, the impact of maritime activities on air quality has garnered significant attention. However, the absence of comprehensive macro-evaluation methods and a limited understanding of regional-scale pollutant emissions introduce substantial uncertainties in assessing emission reduction effectiveness and identifying pollution sources. In this study, we utilized Sentinel-5P satellite data from 2019 to 2024 to examine the spatiotemporal characteristics of six air pollutants (SO₂, NO₂, HCHO, O₃, CO, and CH₄) in China’s coastal areas. We further investigated the correlation between ship density and pollutant concentrations and analyzed the distribution of pollutant concentrations in major coastal ports across China. The results indicate the following: (1) The concentrations of SO₂, HCHO, and CH₄ exhibited a continuous increasing trend, whereas NO₂, CO, and O₃ remained relatively stable or showed a slight decline. All six pollutants demonstrated obvious seasonal variations, with NO₂ and HCHO following a double-peak pattern and O₃, SO₂, CH₄, and CO exhibiting a single-peak pattern. (2) Pollutant concentrations were higher along the northern coast (Yellow Sea and Bohai Sea) and relatively lower in the South China Sea region. Specifically, NO₂, SO₂, and O₃ were higher in the Bohai Sea region; HCHO and CO were more concentrated in the northern coastal area; and CH₄ was elevated in the north and certain ports of the Yangtze River Delta. (3) Ship density displayed a significant positive correlation with NO₂, SO₂, HCHO, CO, and CH₄, indicating that ship emissions are an important source of these pollutants. Although O₃ is not directly emitted by ships, a positive correlation was observed in certain ship-dense areas, primarily due to photochemical reactions involving NO₂ and volatile organic compounds (VOCs). (4) Higher concentrations of NO₂, SO₂, HCHO, CO, and CH₄ were observed in northern ports (e.g., Tianjin Xingang, Qinhuangdao, Tangshan, and Dalian), whereas southern Chinese ports (e.g., Shenzhen, Xiamen, and Haikou) exhibited lower pollution levels. These findings provide a scientific foundation for coastal air pollution control and highlight the necessity of ship emission regulation and integrated multi-pollutant management. Full article

Landslides triggered by extreme rainfall often cause severe casualties and property losses. Therefore, it is essential to accurately assess and predict building vulnerability under landslide scenarios for effective risk mitigation. This study proposed a quantitative framework for vulnerability assessments of structures. It integrated extreme rainfall analysis, landslide kinematic assessment, and the dynamic response of structures. The study area is located in the northern mountainous region of Tianjin, China. It lies within the Yanshan Mountains, serving as a key transportation corridor linking North and Northeast China. The Sentinel-1A satellite imagery consisting of 77 SLC scenes (from October 2014 to November 2023) identified a slow-moving landslide in the region by using the SBAS-InSAR technique. High-resolution topographic data of the slope were first acquired through UAV-based remote sensing. Next, historical rainfall data from 1980 to 2017 were analyzed. The Gumbel distribution was used to determine the return periods of extreme rainfall events. The potential slope failure range and kinematic processes of the landslide were then simulated by using numerical simulations. The dynamic responses of buildings impacted by the landslide were modeled by using ABAQUS. These simulations allowed for the estimation of building vulnerability and the generation of vulnerability maps. Results showed that increased rainfall intensity significantly enlarged the plastic zone within the slope. This raised the likelihood of landslide occurrence and led to more severe building damage. When the rainfall return period increased from 50 to 100 years, the number of damaged buildings rose by about 10%. The vulnerability of individual buildings increased by 10% to 15%. The maximum vulnerability value increased from 0.87 to 1.0. This model offers a valuable addition to current quantitative landslide risk assessment frameworks. It is especially suitable for areas where landslides have not yet occurred. Full article

The ACOSOG Z0011 and IBCSG 23-01 trials demonstrated that axillary lymph node dissection (ALND) offers no prognostic benefit in breast cancer patients with clinically negative axillae and low disease burden (one to two positive nodes) on sentinel lymph node biopsy (SLNB). However, uncertainty remains regarding the management of patients with clinically negative axillae (cN0) who are found to have suspicious lymph nodes on imaging that are subsequently confirmed positive by biopsy. The current practice often directs these patients to upfront ALND, potentially exposing them to unnecessary surgical morbidity. This study aimed to assess the role of axillary ultrasound in predicting high axillary nodal burden and guiding surgical management. Using the Alberta Cancer Registry, we identified 107 cN0 breast cancer patients from 2010 to 2017 who underwent preoperative axillary ultrasound with positive biopsy followed by ALND. Our findings reveal that 42% of these patients had low axillary nodal burden on final pathology, meeting Z0011 criteria, and might potentially have avoided ALND. Furthermore, axillary ultrasound findings were not predictive of high axillary burden. These results highlight that many patients undergoing upfront ALND based on positive ultrasound-guided biopsy could benefit from SLNB alone. This supports the 2023 NCCN guidelines advocating for more selective use of ALND to minimize overtreatment and associated morbidity. Full article

Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteodrones, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteodrones to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA’s integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management. Full article

Sentinel lymph node (SLN) mapping has been investigated as part of surgical staging in women with early-stage cervical cancer (CC); however, pelvic lymphadenectomy (PLND) remains the standard of care. This study aimed to assess feasibility and safety of SLN detection using superparamagnetic iron oxide (SPIO) nanoparticles as a tracer in CC. Thirty CC patients presumed to be stage I were included in this study with SPIO administered intracervically as a tracer for SLN mapping using a magnetometer and followed by PLND. The endpoints of the study included the proportion of successful SLN detection, the average number of SLNs per patient, and the proportion of pathologically positive results per patient and per node. The diagnostic accuracy of SPIO nanoparticles for detection of metastatic SLNs was evaluated by Receiver Operating Characteristic (ROC) curve analysis, with the area under the ROC curve (AUC) used to demonstrate the studied method’s sensitivity. Safety endpoints were a summary of all reported adverse events. SLNs were detected in all cases, bilaterally in 27 patients (90%). The median number of SLNs per patient was 3.5. Four cases had metastatic SLNs. The general malignancy rate per patient was 13.3%, and per node, it was 0.8%. The malignancy detection rate of SLNs was 100% per patient and 80% per node. The AUC of 1.0 (p < 0.001) confirmed the diagnostic value of the SPIO technique for the detection of metastatic SLNs, with a sensitivity of 100%. No adverse events related to the SPIO administration were reported. SPIO nanoparticles, as a tracer for SLN mapping in early-stage CC patients, demonstrated satisfactory accuracy parameters and safety; however, these data need to be evaluated by further research. Full article

In recent years, Kazakhstan has faced the problem of sustainable development in the field of operation of a number of reservoirs: periods of drought lead to a systematic decrease in accumulated fresh water reserves, and the flood of 2024 led to the flooding of a number of settlements. The article raises questions about the real state of the region’s reservoirs (using the example of the Karatomar reservoir), the accuracy of the conducted bathymetric studies, and the correctness of estimating the required step (or distance between the control points being taken) of the tacks (trajectory lines) of the measurement, which was carried out using the Apache 3 bathymetric drone. The study of the patterns of modeling accuracy from the frequency of tacks (trajectory lines) was carried out using kriging methods. Reservoir models were built in QGis and Surfe. When analyzing the coastline, Sentinel-2 space images and Kazvodkhoz (Kazakhstani state enterprise) data were used. The result of the study was an algorithm for determining the step of tacks (trajectory lines) for modern bottom geomorphology. The conducted research has shown that over 78 years of use, the reservoir’s parameters have undergone significant changes. A similar situation of significant deterioration in parameters is characteristic of other hydrotechnical structures in the region. Full article

Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 10⁸ m³, which closely aligns with contemporaneous statistical records. The model yielded an R² of 0.727, an RMSE of 537.566 m³, and a MAE of 239.767 m³, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R² from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area. Full article

Mineral elements can either be pollutants or essential dietary components. Monitoring their levels in the environment and living organisms is crucial because excessive amounts can become toxic. Dogs, due to their proximity to humans, shared habitats, and similar organ structures, can be effective indicators of environmental pollution by toxic elements. This study aimed to assess the levels of 11 mineral elements in 80 dog carcasses (49 males and 31 females), aged between 2 and 16 years, from the Ionian-Etnean volcanic region of the province of Catania, where the dogs had died under unknown circumstances. A direct mercury analyzer (DMA-80) was used to measure Hg, and an inductively coupled plasma mass spectrometer (ICP-MS) was used for the other elements. A one-way ANOVA, followed by Bonferroni’s multiple comparison for post hoc analysis, was conducted to evaluate significant differences between the organ samples and different minerals and between the weight and metal levels. The statistical significance was set at p < 0.05. The study indicates that high concentrations of metals like cadmium, mercury, lead, and chromium are present in the liver, kidneys, and other organs. These elevated concentrations suggest that the local volcanic emissions contribute to soil, water, and atmospheric contamination. The data showed differences in the metal concentrations between the sexes, which could be attributed to biological and environmental factors. Full article

Interferometric synthetic aperture radar (InSAR) technology has been widely employed in the rapid monitoring of earthquakes and associated geological hazards. With the continued advancement of InSAR technology, the growing volume of satellite-acquired data has opened new avenues for applying deep learning (DL) techniques to the analysis of earthquake-induced surface deformation. Although DL holds great promise for processing InSAR data, its development progress has been significantly constrained by the absence of large-scale, accurately annotated datasets related to earthquake-induced deformation. To address this limitation, we propose an automated method for constructing deep learning training datasets by integrating the Global Centroid Moment Tensor (GCMT) earthquake catalog with Sentinel-1 InSAR observations. This approach reduces the inefficiencies and manual labor typically involved in InSAR data preparation, thereby significantly enhancing the efficiency and automation of constructing deep learning datasets for coseismic deformation. Using this method, we developed and publicly released a large-scale training dataset consisting of coseismic InSAR samples. The dataset contained 353 Sentinel-1 interferograms corresponding to 62 global earthquakes that occurred between 2015 and 2024. Following standardized preprocessing and data augmentation (DA), a large number of image samples were generated for model training. Multidimensional analyses of the dataset confirmed its high quality and strong representativeness, making it a valuable asset for deep learning research on coseismic deformation. The dataset construction process followed a standardized and reproducible workflow, ensuring objectivity and consistency throughout data generation. As additional coseismic InSAR observations become available, the dataset can be continuously expanded, evolving into a comprehensive, high-quality, and diverse training resource. It serves as a solid foundation for advancing deep learning applications in the field of InSAR-based coseismic deformation analysis. Full article

Canopy closure is a critical indicator reflecting forest structure, biodiversity, and ecological balance. This study proposes an estimation method integrating U-Net segmentation with machine learning, significantly improving accuracy through multi-source remote sensing data and feature selection. Covering eight U.S. continental states, the study utilized 13,000 stratified samples equally split for model training and validation. Four states were used to train models based on XGBoost, random forest (RF), LightGBM, and support vector machine (SVM), while the remaining four states served for validation. The results indicate that (1) U-Net effectively extracted tree crowns from aerial imagery to construct the sample dataset; (2) among the tested algorithms, XGBoost achieved the highest accuracy of 0.88 when incorporating Sentinel-1, Sentinel-2, vegetation indices, and land cover features, outperforming models using only Sentinel-2 data by 25.7%; and (3) XGBoost-estimated tree canopy cover (Model TCC) showed finer spatial details than the National Land Cover Database Tree Canopy Cover (NLCD TCC), with R² against the true tree canopy closure from U-Net (True TCC) up to 49.1% higher. This approach offers a cost-effective solution for regional-scale canopy monitoring. Full article

Sentinel-1 cross-polarization (cross-pol) SAR data, known for their unsaturated backscattering characteristics, hold strong potential for high-wind-speed retrieval in tropical cyclones (TCs). However, significant inherent noise in cross-pol data limits retrieval accuracy, especially under moderate-to-high wind conditions. Existing noise suppression methods remain insufficient due to their limited consideration of spatially varying noise characteristics within different TC structural regions. To address these challenges, this study proposes an enhanced two-dimensional noise field reconstruction framework based on Bayesian estimation, tailored to the structural features of TCs. The method begins by statistically characterizing cross-pol SAR backscatter to differentiate structural regions within TCs. Noise-scaling coefficients are then calculated to suppress scalloping artifacts, followed by the computation of power balance coefficients in sub-swath transition zones to mitigate abrupt inter-strip power variations through signal power equalization. Comparative assessments against the European Space Agency (ESA) noise vectors show that the proposed approach achieves an average signal-to-noise ratio (SNR) improvement of 2.54 dB. Subsequent sea surface wind speed retrievals using the denoised cross-pol data exhibit significant improvements: wind speed bias is reduced from −2.69 m/s to 0.65 m/s, accuracy is improved by 2.04 m/s, and the coefficient of determination (R²) increases to 0.88. These findings confirm the effectiveness of the proposed method in enhancing SAR-based wind speed retrieval under complex marine conditions associated with tropical cyclones. Full article

Remote sensing applications for marine placer deposit exploration remain limited due to the mineralogical complexity and dynamic coastal processes. This study presents the first medium- to high-level detailed multi-scale remote sensing analysis of placer deposits in the Rías Baixas, NW Spain, focusing on five beaches within the Vigo Estuary. Ten beach samples were analyzed for their heavy mineral (HM) content and spectral signatures, using bromoform separation and FieldSpec 4 spectroradiometer equipment, respectively. The spectral signatures of beach samples with a high HM content were characterized and resampled for the Sentinel-2 application, employing the Spectral Angle Mapper (SAM) algorithm. Field validation and an unmanned aerial vehicle (UAV) survey confirmed surface placer occurrences and the SAM’s results. Santa Marta Beach exhibited significant placer anomalies (up to 30% HM), correlating with low SAM values (minimum value–0.10), indicating high spectral similarity. The SAM-derived anomaly patches aligned with the field observations, demonstrating Sentinel-2’s potential for placer deposit mapping. This work highlights the application of Sentinel-2 in the exploration of placer deposits and the use of a specific spectral range of these deposits in coastal environments. These tools are non-invasive, more environmentally friendly, and sustainable, and can be extrapolated to other regions of the world with similar characteristics. Full article

This study evaluates remote sensing features to resolve problems associated with feature redundancy, low efficiency, and insufficient input feature analysis in bushfire detection. It calculates spectral features, remote sensing indices, and texture features from Sentinel-2 data for the Blue Mountains region of New South Wales, Australia. Feature separability was evaluated with three measures: J-M distance, discriminant index, and mutual information, leading to an assessment of the best remote sensing features. The results show that for post-fire smoke detection, the best features are the normalized difference vegetation index (NDVI), the B1 band, and the angular second moment (ASM) in the B1 band, with respective scores of 0.900, 0.900, and 0.838. For burned land detection, the best features are NDVI, the B2 band, and correlation (Corr) in the B5 band, with corresponding scores of 1.000, 0.9436, and 0.9173. These results demonstrate the effectiveness of NDVI, the B1 and B2 bands, and specific texture features in the post-fire analysis of remote sensing data. These findings provide valuable insights for the monitoring and analysis of bushfires and offer a solid foundation for future model construction, fire mapping, and feature interpretation tasks. Full article

Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R² ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time. Full article