Search Results (2,395)

Embankment structures in civil engineering, such as earth dams and fluvial dikes, have a crucial role in society. These structures, often used for water storage and mining tailing containment, are cost-effective due to their reliance on locally sourced materials. While the failure of concrete structures is not so frequent but often lead to severe consequences, embankment structures, particularly fluvial dikes, are more prone to breach and the consequences vary from mild to catastrophic, depending on the proximity to human populations. Worldwide, some fluvial dike failures have resulted in catastrophic outcomes for human lives, the local economy and the environment. This paper aims to develop a methodology to calculate in situ breach outflow hydrographs, resorting to real-time, non-intrusive and friendly access technology. The goal is to provide a practical platform for developing and testing integrated systems applicable to prototype failure cases. An accurate, real-time hydrograph estimation capacity improves risk assessment. The proposed methodology deploys, in a medium-scale experimental facility, common technology and data processing techniques to characterize the evolution of a fluvial dike failure. The morphodynamic and hydrodynamic components influencing the in situ breach outflow hydrograph are assessed by characterizing, in real-time, the breach morphology at the surface and underwater, the surface velocity maps and the corresponding cartesian coordinates. Full article

Understanding and monitoring benthic habitat distribution is essential for implementing ecosystem-based fisheries management (EBFM). Satellite remote sensing offers a rapid and cost-effective approach to marine habitat assessments; however, its application requires context-specific adjustment to account for environmental variability and differing study aims. As such, predictor variables must be tailored to the specific site and target habitat. This study uses Sentinel-2 Level 2A surface reflectance satellite imagery and stability selection via Random Forest Recursive Feature Elimination to assess the importance of remote sensing indices for mapping moderately deep (<20 m) seagrass habitats in relation to the Marine Stewardship Council-certified Western Australia Enhanced Greenlip Abalone Fishery (WAEGAF). Of the seven indices tested, the Normalised Difference Aquatic Vegetation Index (NDAVI) and Depth Invariant Index for the blue and green bands were selected in the optimal model on every run. The kernelised NDAVI and Water-Adjusted Vegetation Index also scored highly (both 0.92) and were included in the final classification and regression models. Both models performed well and predicted a similar cover and distribution of seagrass within the fishery compared to the surrounding area, providing a baseline and supporting EBFM of the WAEGAF within the surrounding marine protected area. Importantly, the use of indices from freely accessible ready-to-use satellite products via Google Earth Engine workflows and expedited ground truth image annotation using highly accurate (0.96) automatic image annotation provides a rapidly repeatable method for delivering ecosystem information for this fishery. Full article

Regeneration of Pinus brutia (Turkish red pine) after clear-cutting is showing failures in some low-elevation Mediterranean stands, raising questions about long-used silvicultural prescriptions. Because site limitations arise from the combined effects of climate, terrain, and surface thermal conditions that vary over short distances, diagnosing where problems may occur is challenging at operational scales. In this study, we first evaluate the study area (Antalya, Türkiye, 0–400 m elevation band) using open, long-term climatic indicators, along with terrain and surface thermal remote sensing variables, to describe recent environmental conditions relevant to germination and early seedling survival. We then build a transparent environmental-analog screening product that summarizes the degraded reference site as an environmental signature and computes pixel-wise similarity across the landscape at 100 m resolution. The resulting map reports three actionable tiers (≥95th, 90–95th, 85–90th percentiles), delineating compact clusters of very-high analogs surrounded by broader high/elevated belts. Interpreted strictly as a screening layer (not a predictive model), it supports compartment-scale triage: ≥95th areas are first candidates for field checks and adjusted prescriptions, while lower tiers guide targeted site preparation and monitoring. The novelty and importance are practical: widely available Earth observation data are converted into a reproducible, auditable tool that reduces dependence on complex predictive models and large calibration samples, while still requiring careful local interpretation and ground-truthing to inform P. brutia regeneration planning. Full article

This study explores the impact of synthetic data, both physically based and generatively created, on deep learning analytics for earth observation (EO), focusing on the detection of photovoltaic panels. A YOLOv8 object detection model was trained using a publicly available, multi-resolution very high resolution (VHR) EO dataset (0.8 m, 0.3 m, and 0.1 m), comprising 3716 images from various locations in Jiangsu Province, China. Three benchmarks were established using only real EO data. Subsequent experiments evaluated how the inclusion of synthetic data, in varying types and quantities, influenced the model’s ability to detect photovoltaic panels in VHR imagery. Physically based synthetic images were generated using the Unity engine, which allowed the generation of a wide range of realistic scenes by varying scene parameters automatically. This approach produced not only realistic RGB images but also semantic segmentation maps and pixel-accurate masks identifying photovoltaic panel locations. Generative synthetic data were created using diffusion-based models (DALL·E 3 and Stable Diffusion XL), guided by prompts to simulate satellite-like imagery containing solar panels. All synthetic images were manually reviewed, and corresponding annotations were ensured to be consistent with the real dataset. Integrating synthetic with real data generally improved model performance, with the best results achieved when both data types were combined. Performance gains were dependent on data distribution and volume, with the most significant improvements observed when synthetic data were used to meet the YOLOv8-recommended minimum of 1500 images per class. In this setting, combining real data with both physically based and generative synthetic data yielded improvements of 1.7% in precision, 3.9% in recall, 2.3% in mAP@50, and 3.3% in mAP@95 compared to training with real data alone. The study also emphasizes the importance of carefully managing the inclusion of synthetic data in training and validation phases to avoid overfitting to synthetic features, with the goal of enhancing generalization to real-world data. Additionally, a pre-training experiment using only synthetic data, followed by fine-tuning with real images, demonstrated improved early-stage training performance, particularly during the first five epochs, highlighting potential benefits in computationally constrained environments. Full article

Semantic change detection has become a key technology for monitoring the evolution of land cover and land use categories at the semantic level. However, existing methods often lack effective information interaction and fail to capture changes at multiple granularities using single-scale features, resulting in inconsistent outcomes and frequent missed or false detections. To address these challenges, we propose a three-branch model HCTANet, which enhances spatial and semantic feature representations at each time stage and models semantic correlations and differences between multi-temporal images through three innovative modules. First, the multi-scale change mapping association module extracts and fuses multi-resolution dual-temporal difference features in parallel, explicitly constraining semantic segmentation results with the change area output. Second, an adaptive collaborative semantic attention mechanism is introduced, modeling the semantic correlations of dual-temporal features via dynamic weight fusion and cross-temporal cross-attention. Third, the spatial semantic residual aggregation module aggregates global context and high-resolution shallow features through residual connections to restore pixel-level boundary details. HCTANet is evaluated on the SECOND, SenseEarth 2020 and AirFC datasets, and the results show that it outperforms existing methods in metrics such as mIoU and SeK, demonstrating its superior capability and effectiveness in accurately detecting semantic changes in complex remote sensing scenarios. Full article

Urban flooding is a major challenge to sustainable development in rapidly urbanizing cities. This study applies an integrated approach that combines Sentinel-1 SAR data, geomorphological analysis, and the DPSIR (Drivers–Pressures–State–Impacts–Responses) framework to assess the relationship between urbanization and flooding in Hanoi during the 2010–2024 period (with Sentinel-1 time-series data for 2015–2024). A time series of Sentinel-1 images (2015–2024) was processed on Google Earth Engine to detect inundation and construct a flood frequency map, which was validated against 148 field survey points (overall accuracy = 87%, Kappa = 0.79). The results show that approximately 80% of newly urbanized areas are situated on geomorphologically sensitive units, including inside- and outside-dike floodplains, fluvio-marine plains, paleochannels, and karst terrains, characterized by low elevation and high flood susceptibility. Meanwhile, about 73% of the total inundated area occurs within newly developed urban zones, primarily in western and southwestern Hanoi, where rapid expansion on flood-prone terrain has intensified hazards. The DPSIR analysis highlights rapid population growth, land use change, and inadequate drainage infrastructure as the main pressures driving both the frequency and extent of flooding. To our knowledge, this is the first study integrating geomorphology, Sentinel-1, and DPSIR for Hanoi, thereby providing robust evidence to support sustainable urban planning and climate-resilient development. Full article

Soil moisture is an essential environmental parameter affecting hydrological cycles, agricultural productivity, and climate systems. Conventional in situ measurements are precise but do not provide the spatiotemporal coverage for large applications. This research provides an extensive framework for estimating and mapping surface soil moisture by integrating Sentinel-1 Synthetic Aperture Radar (SAR) data with machine learning in the Google Earth Engine (GEE) cloud platform. The study area is the agricultural region of Perambalur district in Tamil Nadu State, India. The research took place between September 2018 and January 2019. The dual-polarized (VV and VH) Sentinel-1 C-band images were collected in tandem with ground truth soil moisture data collected through the gravimetric method. A set of SAR indices and engineered features were extracted from the backscattering coefficients (σ°). A random forest (RF) machine learning model was used in this study to estimate soil moisture. The RF model incorporating the complete set of engineered features showed a coefficient of determination (R²) of 0.694 and a root mean square error (RMSE) of 1.823 (Soil moisture %). The complete processing and modeling workflow was encapsulated in the GEE-based software tool (version 1) providing an accessible, user-friendly platform for generating near-real-time maps of soil moisture. This research proves that the combination of Sentinel-1 data with clever machine-learning algorithms in the GEE cloud platform provides a scalable, efficient, and potent tool for operational soil moisture mapping serving applications in precision agriculture and in the management of the water resource. Full article

Precise monitoring of rubber plantations is critical for effective management and ecological assessments, enabling optimal resource allocation, disease detection, and mitigation of environmental impacts. This study integrated multi-source remote sensing data—including Landsat 8, Sentinel-1/2, GaoFen-1 (GF-1) optical and SAR imagery, and DEM data of Hainan Island. The rubber plantation areas from 2021 to 2025 were extracted from the Google Earth Engine (GEE) platform by employing a multi-step threshold segmentation method, which utilized the Otsu algorithm to automatically determine optimal thresholds for distinguishing rubber plantations from other land covers. The overall accuracy of the extracted rubber plantations in this study was above 90%; the Kappa coefficient was greater than 0.85; and the F1-score surpassed 0.93. The resulting distribution maps reveal that rubber plantations on Hainan Island are predominantly concentrated in the northwestern and northern regions. The rubber plantation area of Hainan Island remained relatively stable from 2021 to 2023. During 2023–2024, the rubber plantation area experienced a decline. This reduction was particularly pronounced in 2024, when the area decreased by nearly 150 km² compared to the previous year. However, in 2025, this downward trend reversed sharply with an increase of approximately 300 km². These findings provide a critical scientific basis for sustainable rubber production, supporting informed decision-making in irrigation, pest control, and yield optimization. Furthermore, they offer valuable insights for strategic planning to balance economic returns with ecological conservation, thereby ensuring the long-term viability of the industry. Full article

Soil maps are essential for managing Earth’s resources, but the accuracy of widely used global and pan-European digital soil maps in heterogeneous landscapes remains a critical concern. This study provides a comprehensive evaluation of two prominent datasets, ISRIC-SoilGrids and the European Soil Data Centre (ESDAC), by comparing their soil texture predictions against the detailed Greek National Soil Map, which is based on over 10,000 field samples. The results from statistical and spatial analyses reveal significant discrepancies and weak correlations, with a very low overall accuracy for soil texture class prediction (19–21%) and high Root Mean Square Error (RMSE) values ranging from 13% to 19%. The global models failed to capture local variability, showing very low explanatory power (R² < 0.2) and systematically underrepresenting soils with extreme textures. Furthermore, these prediction errors are not entirely random but are significantly clustered in hot spots linked to distinct parent materials and geomorphological features. Our findings demonstrate that while invaluable for large-scale assessments, the direct application of global soil databases for regional policy or precision agriculture in a geologically complex country like Greece is subject to considerable uncertainty, highlighting the critical need for local calibration and the integration of national datasets to improve the reliability of soil information. Full article

Accurate estimation of evapotranspiration (ET) is critical for precision irrigation in hyper-arid perennial systems. This study quantified ET in an 8 ha olive orchard in La Yarada–Los Palos (Tacna, Peru) by integrating the METRIC satellite-based energy-balance model (Landsat 8/9, Google Earth Engine) with the process-based AquaCrop model, using ET_FAO-56 as an empirical benchmark. Sixteen cloud-free Landsat scenes from two contrasting seasons—2021–2022 (high-yield) and 2023–2024 (water-limited)—were processed to derive daily ET maps and model simulations aligned with satellite overpasses. Results revealed marked intra-parcel heterogeneity and clear seasonal dynamics. METRIC detected local ET peaks of ~6–7 mm d⁻¹ in densely vegetated central blocks and orchard-mean values up to 4.25 ± 1.76 mm d⁻¹. During the high-yield season, ET_METRIC and ET_AQUACROP showed excellent agreement (R² = 0.94; RMSE = 0.21 mm d⁻¹; bias μ = 0.11 mm d⁻¹), whereas FAO-56 consistently underestimated ET (R² = 0.88; RMSE = 0.82 mm d⁻¹). Under water-limited conditions, model correspondence remained strong but attenuated (ET_METRIC–ET_AQUACROP: R² = 0.75; RMSE = 0.64 mm d⁻¹; ET_METRIC–ET_FAO-56: R² = 0.95; RMSE = 0.59 mm d⁻¹), with METRIC exhibiting a persistent positive bias (μ = 0.43–0.56 mm d⁻¹) attributable to localized soil evaporation and micro-advection. Overall, METRIC provided high-resolution spatial diagnostics of canopy stress, while AquaCrop offered daily continuity and explicit evaporation/transpiration (E/Tr) partitioning, enabling a coherent multiscale assessment of ET. The integrated framework enhances operational monitoring of water use and supports deficit-irrigation optimization in hyper-arid olive systems. Full article

Urban heat island pose a significant threat to public health and urban livability. UHI maps are created using satellite thermal data, a crucial source for earth monitoring and for delivering mitigation strategies. Nowadays there is still a resolution gap between high-resolution optical data and low-resolution satellite thermal imagery. This study introduces a novel deep learning approach—named Dilated Spatio-Temporal U-Net (DST-UNet)—to bridge this gap. DST-UNET is a modified U-Net architecture which incorporates dilated convolutions to address the multiscale nature of urban thermal patterns. The model is trained to generate high-resolution, airborne-like thermal maps from available, low-resolution satellite imagery and ancillary data. Our results demonstrate that the DST-UNet can effectively generalise across different urban environments, enabling municipalities to generate detailed thermal maps with a frequency far exceeding that of traditional airborne campaigns. This framework leverages open-source data from missions like Landsat to provide a cost-effective and scalable solution for continuous, high-resolution urban thermal monitoring, empowering more effective climate resilience and public health initiatives. Full article

Xiaochikan Village, located in Guangdong Province in South China, is one of the few remaining traditional rammed earth dwellings of the Cantonese ethnic group in the Lingnan region. However, the influence of Zhuhai’s subtropical maritime monsoon climate has led to continuous physical and chemical erosion of the rammed earth walls. For example, cracking occurs due to high temperatures and heavy rain, accelerated weathering occurs due to salt spray deposition, and biological erosion occurs due to high humidity and high temperatures. Therefore, two experimental analysis techniques, X-ray diffraction (XRD) and scanning electron microscopy-energy dispersive spectrometer (SEM-EDS), were used to explore the structural anti-erosion mechanism of the ancient, rammed earth buildings in Xiaochikan Village. The results show that (1) the morphological characteristics of the east and west walls of the rammed earth dwellings in Xiaochikan Village are more similar. The particles on the east wall are regular spherical or polygonal, small, and evenly distributed, while the particles on the west wall are mainly spherical and elliptical, with consistent size and less agglomeration. The surfaces of the particles on both walls are relatively smooth and flat. (2) The core element bases of the four wall samples are consistent, with C, Si, Al, Ca, and Fe as the core, accounting for more than 93%, reflecting the base characteristics of the local alluvial soil “silicate skeleton–carbonate cementation–organic matter residue” and reflecting the “local material” attribute of rammed earth. Except for the south wall sample, the Cl content of the remaining samples exceeds 1%. In the thermal map, Cl shows “pore/interstitial enrichment”, which confirms that the salinization process of marine aerosols with rainwater infiltration and evaporation residue is a common influence of marine climate. (3) The rammed earth walls in Xiaochikan Village consist of three main minerals: Quartz (SiO₂, including alpha-type SiO₂), Calcite (CaCO₃, including synthetic calcite), and Gibbsite (Al(OH)₃). Full article

CerMapp is a multi-platform and system application designed to address a critical gap in veterinary public health: the lack of a standardized, national-scale geodatabase for wildlife diseases. This gap has long hindered the effective application of GIS and remote sensing in spatial epidemiology. Currently deployed at the prototype level in Aosta Valley, NW Italy, the application’s core innovation is its ability to generate a structured, analysis-ready data repository, which serves as a foundational resource for One Health initiatives. Developed by the National Reference Center for Wildlife Diseases on the ESRI ArcGIS Survey123 platform v.3.24, CerMapp enables veterinarians, foresters, and wildlife professionals to easily collect and georeference field data, including species, health status, and photographic evidence using flexible methods such as Global Navigation Satellite System or manual map entry. Data collected via CerMapp are stored in a centralized geodatabase, facilitating risk analyses and detailed geospatial studies. This data can be integrated with remote sensing information processed on cloud platforms like Google Earth Engine or within traditional GIS software, contributing to a comprehensive and novel wildlife health registry. By promoting the rational and standardized collection of essential geospatial data, CerMapp data may support predictive disease modeling, risk assessment, and habitat suitability mapping for wildlife diseases, zoonoses, and vector-borne pathogens. Its scalable, user-friendly design ensures alignment with existing national systems like the Italian Animal Disease Information System (SIMAN), making advanced geospatial analysis accessible without requiring specialized digital skills from field operators or complex IT maintenance from institutions. Full article

The ongoing launch of high-resolution satellites has led to a significant increase in the volume of synthetic aperture radar data, resulting in a high-resolution and high-revisit Earth observation that efficiently supports subsequent high-resolution SAR change detection. To address the issues of speckle noise interference, insufficient integrity of change targets and blurred boundary location of high-resolution SAR change detection, we propose a coarse-to-fine framework based on the multi-scale segmentation and hybrid structure graph (HSG), which consists of three modules: multi-scale segmentation, difference measurement, and change refinement. First, we propose a graph-based multi-resolution co-segmentation (GMRCS) in the multi-scale segmentation module to generate hierarchically nested superpixel masks. And, a two-stage ranking (TSR) strategy is designed to help GMRCS better approximate the target edges and preserve the spatio-temporal structure of changed regions. Then, we introduce a graph model and measuring difference level based on the HSG. The multi-scale difference image (DI) is generated by constructing the HSG for bi-temporal SAR images and comparing the consistency of the HSGs to reduce the effect of speckle noise. Finally, the coarse-scale change information is gradually mapped to the fine-scale based on the multi-scale fusion refinement (FR) strategy, and we can get the binary change map (BCM). Experimental results on three high-resolution SAR change detection datasets demonstrates the superiority of our proposed algorithm in preserving the integrity and structural precision of change targets compared with several state-of-the-art methods. Full article

This study integrates geospatial modeling with multi-criteria decision analysis for an improved approach to landslide susceptibility mapping (LSM). This approach addresses key challenges in LSM through sophisticated multicollinearity analysis and machine learning strategies. We compared three machine learning models for weighting, and of them the Permutation-Weighted model yielded the best prediction results, with an Area Under Curve (AUC) of 95%, an accuracy of 69%, and a recall of 66%. To resolve perfect multicollinearity (r = 1) between land use land cover (LULC) and geological factors, we implemented Principal Component Analysis (PCA). The selected factors demonstrated strong predictive power, with the PCA-derived features exhibiting the best performance, having a Variation Inflation Factor (VIF) of 1.004. Slope appeared as the most influential factor (51.7% contribution), while the Topographic Wetness Index (TWI) was less dominant with only 6.6%. Multiple landslide susceptibility mapping methods yielded consistent results, with 29.8–30.1% of the study area showing moderate susceptibility and 35.2–36.9% in the high to very high susceptibility class. The model also incorporated vulnerability parameters weighted by the United Nations Office for Disaster Risk Reduction (UNDRR) indicators, including farmland, buildings, bare land, water bodies, roads, and amenities to generate hazard, vulnerability, and risk maps. The results were verified through visual comparison with high-resolution Google Earth imagery. The Permutation-Weighted model performed better than others, categorizing 12.4% at high-risk, while Random Forest (RF) categorized 7.2% at high risk. This study makes three key contributions: (1) It establishes the effectiveness of PCA/VIF for variable selection, (2) it provides a comparison of machine learning weighting techniques, and (3) it validates a workflow applicable to data-scarce regions. Full article