Search Results (3,802)

Floods are among the extreme events associated with climate variability in the Lacramarca River basin, located in the department of Ancash, Peru. Meteorological phenomena such as El Niño during the periods 1982–1983 and 1997–1998, as well as the Coastal El Niño in 2017, constitute key reference events that motivated the development of the present study, based on a case study conducted in the area between the rural settlements of Santa Clemencia and Pampadura. This research is based on maximum precipitation data derived from historical climate records and from the climate scenarios ACCESS 1-3, HadGEM2-ES, and MPI-ESM-MR, as well as the median projected scenario for 2050, obtained from the National Meteorology and Hydrology Service of Peru (SENAMHI) data platform. This information was analyzed considering the spatial location of the basin and its position relative to the area of interest, using Intensity–Duration–Frequency (IDF) curves. To demonstrate the changes in the river hydrological behavior before and after the 2017 Coastal El Niño event, a Random Forest modeling approach was applied using Sentinel-2 satellite imagery. Design peak discharges for return periods of 50, 100, and 140 years were estimated using the HEC-HMS software. Hydraulic simulation of the Lacramarca River basin, carried out using HEC-RAS version 6.7 beta 3 and IBER version 3.3.1 software, made it possible to identify flood-prone areas affecting agricultural land and areas adjacent to population centers, covering 149,000 m² and 172,000 m² for return periods of 100 and 140 years, respectively, based on information from the historical scenario. In contrast, using data from the 2050 projection scenario, affected areas of 242,000 m² and 323,000 m² were estimated for the same return periods. Full article

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article

As volcanism may be considered synonymous with heat, the MODVOLC system utilizes thermal infrared satellite data from MODIS instruments to monitor the eruptive state of active volcanoes in near-real-time. This automatic hot-spot detection system is used here as the primary data source. We present a 25-year (2000–2024) record of MODVOLC lava discharge rates for Mount Etna in Sicily, Italy. Examples are given for four large flank eruptions occurring in 2001, 2002–2003, 2004–2005, and 2008–2009. Derived “lower” and “upper” erupted volume estimates for these eruptions range from $28-53\times {10}^6 {\mathrm{m}}^3$ to $57-104\times {10}^6 {\mathrm{m}}^3$ respectively. Such values correspond favorably to earlier estimates determined in the field or from other satellite data. Given the similarity of results, we then apply the same approach to the analysis of selected eruptions of five other basaltic volcanoes. Inversion of MODVOLC-derived time averaged discharge rates and volume data for Mount Etna eruptions in this study provides estimated magma chamber dimensions of ~2.2 to 2.6 km in radius for “lower” estimates (2.6 to 2.9 for “upper”), with an estimated total volume between ~45–74 ${\mathrm{k}\mathrm{m}}^3$ (“lower) and ~74–102 ${\mathrm{k}\mathrm{m}}^3$ (“upper”), which can inform plausible depth ranges at approximately 5 km when combined with petrologic/geodetic constraints. Full article

The need for Structural Health Monitoring is evident in order to ensure the safety of civil infrastructure. The goal of vibration monitoring is to derive the eigenfrequencies, mode shapes and damping of a structure. A change in the eigenfrequency over time can indicate deterioration or damage in a structure. The amplitude can be used to calculate the damping ratio. As the damping ratio is amplitude-dependent, it is important to correctly determine the amplitude values. This study focuses on the amplitude correctness of high-rate Global Navigation Satellite System (GNSS) receiver data. In an experiment with controlled oscillations with a shaker and a Laser Triangulation Sensor (LTS) as a reference, the vibration amplitudes derived by GNSS measurements were analyzed, using time-frequency techniques like Short Time Fourier Transform (STFT) and Wavelet Transform (WT). We demonstrate that vibrations in the millimeter range can be derived from the measurements of satellites orbiting 20,000 km above Earth. However, the amplitudes of the determined frequencies show systematic errors up to 60% when compared to independent reference measurements. We introduce a correction method to reduce this error by applying a frequency-dependent correction function. Full article

To address the suboptimal radiometric calibration accuracy observed in specific beam codes of the GaoFen-3 (GF-3) series satellite for sea surface wind speed (SSWS) retrieval, this study introduces a calibration constant correction method based on the geophysical model function (GMF). This approach enables high-precision SSWS retrieval from GF-3B data. Conventional SAR-based SSWS retrieval models typically rely on pointwise mapping relationships, which overlook the spatial characteristics inherent in dynamic sea surface wind fields. To overcome this limitation, this study proposes an attention-guided dual-branch feature-fusion network (ADBFF-NET). The first branch, implemented as a backpropagation neural network (BPNN), learns nonlinear mappings between the normalized radar cross-section (NRCS, σ⁰), incidence angle, azimuth look direction, and wind vectors (speed and direction). The second branch, designed as a residual convolutional neural network, extracts spatial features of wind fields. An attention mechanism fuses the outputs of both branches, thereby enhancing retrieval accuracy. Experiments conducted with GF-3 series satellite data were validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5), Advanced Scatterometer (ASCAT) wind fields, and altimeter-derived wind speeds. The results indicate that the SSWS retrieved from GF-3B SAR data using the corrected calibration constants achieve a root mean square error (RMSE) of 1 m/s against ERA5 wind speeds, representing an approximately 40% reduction compared with the RMSE obtained using the original calibration constant. Furthermore, compared to ERA5 and ASCAT data, the RMSE of the wind speeds retrieved by the ADBFF-NET model reaches 1.17 m/s and 1.03 m/s, respectively. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. Full article

Zenith Tropospheric Delay (ZTD) and its associated atmospheric water vapor information constitute essential environmental variables for Earth observation (EO)-based atmospheric monitoring and environmental variable retrieval. High-quality ZTD products are therefore of great importance for the post-processing, refinement, and reconstruction of atmospheric environmental variables at regional scales. Among existing observation techniques, Global Navigation Satellite System (GNSS) measurements provide high-precision ZTD estimates and have become an important means for retrieving tropospheric delay and water vapor. However, the sparse and uneven spatial distribution of GNSS stations limits their direct applicability for continuous environmental monitoring. Reanalysis-based products, such as ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), offer EO big data with excellent spatiotemporal continuity but suffer from pronounced systematic biases compared to precision GNSS retrievals, restricting their direct use in high-accuracy regional applications. To address these limitations, this study proposes a Residual Correction Kriging method for ZTD (RK ZTD) that integrates GNSS ZTD and ERA5 ZTD grids through a multi-source data fusion framework. High-precision GNSS ZTD is treated as reference data, and the differences between GNSS ZTD and ERA5 ZTD at modeling stations are defined as residuals to characterize the systematic bias in ERA5 ZTD grids. A Kriging interpolation algorithm is then employed to model the spatial distribution of these residuals and generate residual correction grids. By superimposing the interpolated residual grids onto the ERA5 ZTD grids, a refined and high-precision regional ZTD product is reconstructed. Experiments were conducted using observations collected in 2023 from 36 GNSS stations in the Netherlands, including 10 modeling stations and 26 independent validation stations, together with concurrent ERA5-derived ZTD grids. The results demonstrate that the proposed RK ZTD model provides spatially robust and high-precision ZTD products across the study region. The RK ZTD achieves a Root Mean Square Error (RMSE) of 5.70 mm, representing improvements of 58.4% and 35.4% compared with the original ERA5 ZTD (13.69 mm) and the GNSS-Kriging ZTD (8.82 mm), respectively. Moreover, the absolute bias is reduced to 0.41 mm, in contrast to 5.15 mm for the ERA5 ZTD, indicating that systematic biases are effectively mitigated. Spatial and seasonal analyses further confirm that the proposed method maintains stable performance across all seasons and significantly alleviates interpolation inaccuracies caused by sparse GNSS stations, even under extreme weather conditions such as Storm Ciarán, proving its value for advanced Earth environmental science applications. Full article

Rare-metal pegmatite–greisen systems are commonly small, structurally controlled, and difficult to delineate using conventional mapping alone. This study proposes a multiscale remote-sensing workflow for prospecting Li–Nb–Ta–Cs mineralisation in the Kalba–Narym rare-metal belt (East Kazakhstan) by integrating Sentinel-2 multispectral imagery, UAV-derived centimeter-scale orthomosaics, structural (lineament) analysis, and field-based mineralogical–geochemical validation. Sentinel-2 responses were first calibrated using known occurrences to derive alteration proxies related to greisenisation, silicification, Na-metasomatism, and oxidation. These proxies were combined into an Integrated Hydrothermal Alteration Index (IHAI) to highlight areas where multiple alteration processes overlap. Lineament mapping from Sentinel-2 and DEM products indicates dominant NW–SE and NE–SW structural trends, zones of elevated lineament density and intersection systematically coincide with high IHAI values. UAV orthomosaics refine satellite-scale anomalies by resolving quartz-vein networks, fracture corridors, and surface-alteration textures that are not detectable at 10–20 m resolution. Mineralogical and geochemical data confirm that high-IHAI targets correspond to albitised pegmatites and greisenised rocks enriched in Li, Nb, Ta, and Cs. The results demonstrate that combining freely available Sentinel-2 data with UAV observations and targeted ground validation provides a cost-effective and transferable framework for reducing false positives and prioritising exploration targets in structurally complex granitoid terranes. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

Digital Twins for cultural heritage buildings commonly depend on high-fidelity 3D scanning, detailed onsite surveys, and unrestricted data acquisition. In many countries, however, legal, regulatory, and conservation constraints render such methods inaccessible or explicitly prohibited, significantly limiting the deployment of digital-heritage technologies in real settings. This paper introduces HeritageTwin Lite, a regulation-compliant workflow for constructing low-detail yet operational Digital Twins of protected cultural heritage buildings using only publicly permissible data sources. The proposed approach relies on a GIS-based 2D application through which users select a site and manually delineate building footprints and structural outlines. These 2D sketches are combined with satellite imagery, publicly available photographs, archival records, and open datasets to generate a massing-level 3D model. Building height and volumetric characteristics are estimated using contextual cues such as surrounding structures, known architectural typologies, and scale references derived from people or urban elements. The resulting Digital Twin prioritizes geometric plausibility over fine architectural detail, enabling simulation, analysis, and decision-support tasks, such as environmental modeling, airflow and CFD approximation, and high-level Heritage BIM integration, while fully respecting cultural heritage restrictions. Three case studies illustrate the proposed workflow and systematically identify which components of conventional smart-building and Digital Twin pipelines remain feasible and which become infeasible under heritage regulations. The results demonstrate a practical and scalable path toward compliant Digital Twins for protected buildings, positioning low-detail modeling not as a limitation but as a regulatory necessity. Full article

Accurate mapping of grass species in biodiverse ecosystems, such as wetlands, is critical for ecological protection. Rapid advancements in remote sensing have established satellite data as a critical tool for wetland grass species mapping; however, its relatively coarse spatial resolution and susceptibility to cloud contamination limit the distinction of co-occurring species at fine scales. While Unmanned Aerial Vehicle (UAV) remote sensing offers high resolution and operational flexibility, relying on single-source features is often insufficient for fine-scale wetland species mapping due to the spectral similarity of co-occurring species. On the other hand, the fusion of multi-source remote sensing features (i.e., spectral, textural, and geometric features) likely provides a promising solution for achieving accurate, fine-scale grass species mapping in biodiverse ecosystems. In this study, we developed a wetland grass species mapping framework integrating spectral, textural, and geometric features derived from UAV RGB and multispectral imagery. Using a dataset of 95,880 image objects representing 24 wetland grass species classes collected in two years in Dajiu Lake National Wetland Park of China, we evaluated three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—across various feature combinations. We found that while spectral features (i.e., red edge, normalized green–red difference index [NGRDI], and normalized difference vegetation index [NDVI]) (related to leaf pigment concentrations and cellular structures) exhibited the highest importance in wetland grass species mapping, textural (i.e., contrast) and geometric features (i.e., aspect ratio) significantly enhanced classification performance as complementary information, yielding improvements of up to 10.5% in overall accuracy (OA) and 0.103 in Macro-F1 scores. Specifically, the fusion of spectral, textural, and geometric features achieved optimal performance with an OA of 81.9% and a Macro-F1 of 0.807. Furthermore, the XGBoost model outperformed SVM and RF, improving OA by 9.4% and 2.8%, and Macro-F1 by 0.08 and 0.035, respectively. By identifying the optimal feature combination and machine learning algorithm, this study establishes an accurate method for wetland grass species mapping, offering new opportunities for ecological assessment and precision conservation in biodiverse landscapes. Full article

Accurate and interpretable air quality prediction remains a critical challenge for environmental health management due to complex, nonlinear interactions among emissions, meteorology, and atmospheric chemistry. This study presents a hybrid physics informed and multimodal deep learning framework for city-scale air quality and health risk prediction. The framework combines a Gaussian plume dispersion model with a residual CNN-LSTM network that learns data driven corrections while preserving physical consistency. Multimodal open datasets, including ground based pollutant sensors, meteorological records, and satellite derived aerosol and temperature features, are jointly fused to improve spatiotemporal fidelity. An Exposure Health Index module further links predicted pollutant fields with respiratory morbidity indicators, providing a quantitative bridge between atmospheric variability and health outcomes. Using open source datasets from Riyadh, Jeddah, and Dammam, the proposed approach achieves up to 25% lower mean absolute error and $R^2$ values above 0.85 compared with physics only and purely data driven baselines. Explainability analyses using SHAP and spatial attention highlight physically plausible drivers and confirm feature relevance. The results demonstrate that physics guided residual learning can unify deterministic dispersion modeling and multimodal inference, providing a transparent, scalable, and reproducible foundation for air quality forecasting and health risk assessment. Full article

The world is undergoing rapid transformations driven by climate change, socio-economic pressures, and geopolitical tensions. Monitoring these dynamics is essential to understand and anticipate territorial change. Although initiatives such as the European Union’s Open Data program promote spatiotemporal datasets (e.g., population, land use), analyzing and interpreting these data over time remains complex and requires technical expertise, limiting their accessibility. This research proposes Semantic Web-based methods to detect and annotate trends in spatiotemporal series, thereby assisting in the systematic analysis of temporal patterns. We introduce the SETT ontology (SEmantic Trajectory of Territory) and its OFF-SETT framework (Ontological Framework For SETT), enabling the formal description of territorial trends and their publication as semantic trajectories in the Linked Open Data cloud. The study delivers (i) a generic methodology for detecting and describing trajectories in spatiotemporal datasets; (ii) a framework for automatically generating knowledge graphs capturing these trajectories; (iii) a knowledge graph describing trajectories of demographic and satellite-derived variables (e.g., temperature, water, vegetation) for study areas in France and Switzerland; and (iv) a web-based geovisualization platform. The approach shows that Semantic Web technologies bridge complex spatiotemporal analysis and public accessibility. By publishing territorial trajectories as knowledge graphs, it fosters transparency, interoperability, and reuse of data, supporting informed decision-making and citizen engagement. Full article

Tightly coupled GNSS/INS is a widely adopted architecture for UAVs and ground vehicles. In this study, a Kalman-filter-based fusion framework integrates inertial data with satellite observables, including pseudorange and Doppler-derived range rate, to sustain precise navigation when GNSS quality degrades. A key bottleneck is that many pipelines rely on fixed or overly simplified measurement-noise covariance models, which cannot track the nonstationary statistics of real observations. To address this issue, we develop an adaptive covariance estimator built on a Transformer enhanced with three modules: a Linear-Attention layer, a Residual Sparse Denoising Autoencoder (R-SDAE), and a lightweight residual channel-attention block (LRCAM). The estimator predicts the measurement-noise covariance online. R-SDAE distills sparse, outlier-resistant features from noisy ephemeris; LRCAM reweights informative channels via residual gating; and Linear Attention preserves long-range spatiotemporal dependencies while reducing attention cost from O(N²) to O(N). A predictive factor further modulates the covariance for improved efficiency and adaptability. Experimental results on real road-test data show that the proposed method achieves sub-meter positioning accuracy in open-sky conditions and preserves meter-level accuracy with improved robustness under GNSS-degraded urban scenarios, outperforming the compared adaptive-filtering baselines and neural covariance estimators and thereby demonstrating superior positioning accuracy and stability. Full article