Search Results (2,860)

Heterogeneous change detection (HCD) using satellite and Unmanned Aerial Vehicle (UAV) imagery is a pivotal task in remote sensing and Earth observation. However, the effective utilization of such multi-source data is significantly hindered by extreme spatial resolution disparities and distinct radiometric characteristics. Existing deep learning methods, often based on weight-sharing Siamese architectures, struggle to bridge these domain gaps, leading to spectral pseudo-changes and blurred detection boundaries. To address these challenges, we propose a novel Dual-Branch Cross-Scale Fusion Network (DBCF-Net) specifically tailored for heterogeneous satellite–UAV change detection. We introduce a Difference-Aware Attention Module (DAAM) to explicitly align cross-modal feature spaces and suppress domain-related noise through a hybrid local–global attention mechanism. Furthermore, an Adaptive Gated Fusion Module (AGFM) is designed to dynamically weight multi-scale interactions, ensuring the preservation of high-frequency spatial details from UAV imagery while maintaining the semantic consistency of satellite data. Extensive experiments on the Heterogeneous Satellite–UAV Dataset (HSUD) demonstrate that DBCF-Net achieves state-of-the-art performance, reaching an F1-score of 88.75% and an IoU of 80.58%. This study provides a robust technical framework for heterogeneous sensor fusion and high-precision monitoring in complex remote sensing scenarios. Full article

Coastal cities exposed to extreme wind events are facing increasing challenges in emergency management under climate change. Accurate and high-resolution wind environment information over complex urban terrain is essential for disaster risk assessment and evidence-based emergency planning; however, such information is often unavailable in conventional management practices. This study proposes an integrated UAV–CFD framework to support urban wind risk assessment by combining multi-source geospatial data and high-resolution numerical simulation. A refined urban terrain model with a spatial resolution of 0.5 m was constructed through the fusion of Google Earth data and UAV oblique photogrammetry, and subsequently coupled with a computational fluid dynamics (CFD) model to analyze the urban wind environment. Field measurements obtained from a 50 m wind observation tower were used to validate the simulation results. The results reveal significant wind speed amplification caused by complex terrain and building configurations, with a maximum amplification factor of 1.95 due to the canyon effect. The relative errors between simulated and measured wind speeds and turbulence intensity were generally within 15%, demonstrating the reliability of the proposed framework. By providing high-resolution and spatially explicit wind risk information, this study offers practical decision-support for emergency management, urban planning, and resilience-oriented disaster mitigation in coastal cities. Full article

This study explores how demystifying Earth Observation (EO) through co-creation pathways and local language can enhance flood resilience and environmental governance in African informal cities. Using case studies from Maiduguri and Hadejia, Nigeria, the research employed a transdisciplinary mixed-methods design combining rapid evidence assessment, surveys, participatory workshops (n = 50 stakeholders) integrating simplified Sentinel-1/2 demonstrations, indigenous knowledge mapping, and pre-/post-engagement surveys on EO familiarity. Non-expert participants were trained to interpret satellite data using local language, linking distant teleconnections with local flood experiences. The findings revealed significant gains in EO literacy and improvements in interpretive confidence, gender-inclusive participation, and policy engagement. Localizing the curriculum enabled participants to translate technical EO concepts into locally meaningful narratives, fostering cognitive empowerment and practical application in flood preparedness and advocacy. The study demonstrates that data democratization is not only a matter of open access but also of open understanding. It advances a conceptual model linking Demystification, Literacy, Empowerment, Co-Production and Resilience, positioning EO as a social technology that bridges scientific and indigenous knowledge systems. The findings contribute to debates on decolonizing environmental science and propose a potential participatory framework for integrating EO into community-based adaptation, legal accountability, and policy reform across Africa’s rapidly urbanizing landscapes. Full article

Rare earth elements (REEs) play a critical role in provenance tracing and the environmental reconstruction of the Earth. However, systematic investigations into the geochemical behavior and fractionation mechanisms of REEs during halite crystallization in brine–salt systems remain limited. This study reports new trace element and REE data for Late Cretaceous halites from the Thakhek Basin, Laos. Ratios of Sr/Ba, Sr/Cu, and V/Cr indicate a marine origin for the halites, which formed under hot climatic and oscillating oxidizing–anoxic redox conditions. Both primary and secondary halites display uniform Post-Archean Australian Shale (PAAS)-normalized REE distribution patterns, characterized by relative enrichment in medium rare earth elements (MREE) and depletion in light (LREE) and heavy rare earth elements (HREE). Similar REE patterns are also observed in halites from other modern and ancient, continental and marine salt basins worldwide. These observations suggest that the influences of parent brine composition and external provenance supplies on REE fractionation are negligible, given the consistent source, salinity, and redox conditions recorded in these halites. Accordingly, REE fractionation in halite was largely controlled by crystallographic effects, with aqueous MREE preferentially incorporated into halite crystals during deposition. In addition, the relatively lower Zr/Hf ratios in secondary halites compared to primary halites further validate the utility of the Zr/Hf ratio for distinguishing authigenic halite from salt modified by diagenesis, weathering, dissolution, or recrystallization. While our results establish a fundamental REE distribution pattern for halite, further research is needed to better constrain the underlying fractionation mechanisms of REEs in evaporite minerals within brine–salt systems. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. 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

Accurate mapping of near-surface air temperature ($T_{\mathrm{air}}$) at the fine spatial resolution is required for city-scale monitoring and remains a critical challenge in Earth Observation (EO). Reliance on ground-based measurements is constrained by their sparse spatial coverage and high operational costs. We present a novel, scalable machine learning framework designed to overcome this limitation. Our method utilizes interpretable Convolutional Neural Networks (CNNs) to fuse high-resolution Landsat data, integrating both thermal and reflective spectral bands, with contextual spatiotemporal metadata. This approach allows for inference, at 30 m resolution, of $T_{\mathrm{air}}$ fields without relying on dense, localized ground monitoring networks. Our hybrid CNN architecture is optimized for spatial generalization, maintaining strong and transferable performance (station-wise $R^2\approx 0.88$) across diverse environments from humid coasts ($R^2\approx 0.89$) to arid interiors ($R^2\approx 0.84$). Although focused on a specific geographical region, our results suggest a robust and reproducible pathway for generating spatially consistent temperature fields from globally available EO archives, directly supporting urban heat island mitigation, climate policy development, and high-resolution public health assessment worldwide. Full article

Hyperspectral remote sensing (HRS) is gaining widespread adoption within the geoscience and Earth observation communities. It fosters diverse applications, including precision agriculture, soil science, mineral exploration, and carbon detection, to name a few. Recent technological advancements facilitated a growing number of satellite missions as well as an increase in the availability of commercial sensors and platforms, such as drones. A significant challenge in deploying the varied platforms and sensors is the design and optimization of the hyperspectral surveys. Forward modelling simulators are valuable for optimizing mission parameters and estimating imaging performance. Limited accessibility of open-source simulators presents an obstacle for users who seek to benefit from such tools. To bridge this gap, HySIMU (Hyperspectral SIMUlator) was developed and described herein. It is an open-source, forward modelling toolkit that combines and integrates a primary processing pipeline with various open-source packages into a transparent and modular workflow. It offers a cost-effective approach to evaluating the performance of hyperspectral surveys. HySIMU is designed to simulate hyperspectral imagery based on user-defined targets, platforms, and sensor parameters. Features include (i) a ground truth data cube builder for customizable input parameters, (ii) a terrain-based solar and view geometry calculator for illumination modelling, (iii) integrated open-source radiative transfer models for incorporating atmospheric effects, and (iv) spatial resampling filters. In this manuscript, the initial framework for HySIMU is presented with some example applications, including two validation studies with real hyperspectral images. As remote sensing technologies advance, forward modelling toolkits such as HySIMU play a crucial role in refining mission designs and assessing survey feasibility. The scalability for arbitrary hyperspectral sensors, platforms, and spectral libraries ensures broad applicability. Of particular importance is support for parameter optimization for both scientific and commercial HRS campaigns. Full article

The urban heat island effect is well recognized and has been quantified using ground observations within and outside urban areas. Earth Observation has further revealed small-scale local spatial differences, especially in urban surface temperatures, that have been shown to be highly correlated with differences in the urban fabric. However, surface temperatures do not directly translate to human-experienced temperatures, and hence high-resolution air temperature data is of high relevance. However, air temperature is not easily measured from space, and seldom do ground measurements allow for small-scale differences to be quantified to a satisfactory degree. In the present study, we assessed the added value of an air temperature product downscaled using machine learning compared to the high-resolution reanalysis model that formed its foundation. The downscaled product was developed using satellite data, local observations from privately owned weather stations, and high-resolution reanalysis. The comparison focused on Denmark’s four largest urban areas and examined the two data product’s ability to describe the urban heat island effect at the city scale as well as intra-city differences in air temperatures. Both data products show similar urban heat island effects at the city scale, while the downscaled product shows greater intra-city variance in air temperature, with patterns that are somewhat correlated with both urban density and urban green spaces. Generally, the downscaling product offers city planners a better data basis for evaluating where to prioritize contingency and mitigation measures within the urban space. Full article

The objective of this study is to explore the time-varying shock transmission mechanism between aggregated financial stress indices (FSIs) of developed economies (the U.S., the U.K., the European Union (EU) and Japan) and the emerging economy of China. We employ a novel Time-Varying Parameter Vector Auto-Regression (TVP-VAR)-based “connectedness approach” to capture dynamic shock spillovers without the limitations of arbitrarily chosen rolling windows, loss of observations, or excessive sensitivity to outliers, as it is grounded in a multivariate Kalman filter structure. The aggregated measures of the FSIs of China, the U.S., the U.K., the EU and Japan are incorporated from the Asian Development Bank’s data repository by using time-series observations from January 2010 to September 2023. The findings indicate that the FSI of China is influenced by financial stress shocks originating from Japan (18.35%) and the U.S. (16.86%) the most, whereas the U.K. (EU) contributes to only 8.42% (6.54%) of FSI shocks in China. This research article significantly captures China’s heightened vulnerability to external financial stress shocks from developed economic systems and underscores the critical importance of reinforcing financial resilience, strengthening macro-prudential regulations and early-warning systems, and expanding financial buffers during episodes of trade uncertainty like restrictions on China’s rare earth exports and solar panels, U.S. restrictions on industrial metal imports, Brexit, supply chain disruptions amid COVID-19, and geopolitical uncertainties like the Russia–Ukraine war. Overall, this study provides actionable guidance for mitigating the impact of global financial stresses, improving risk management, and safeguarding economic stability in an increasingly interconnected and volatile international environment. Full article

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

Oil spills are severe environmental disasters that cause long-lasting damage to marine ecosystems and impose significant economic costs, underscoring the urgent need for efficient detection and monitoring technologies. Conventional field-based observation methods, while valuable, are constrained by limited spatial coverage, high costs, and labor-intensive processes, making them impractical for large-scale or rapid-response applications. To overcome these challenges, satellite remote sensing has been used as an effective alternative for oil spill monitoring. In particular, the advent of Google Earth Engine (GEE), a cloud-based geospatial platform, has transformed oil spill research by enabling scalable management and analysis of large satellite remote sensing datasets. This review synthesizes studies employing GEE for oil spill detection, across marine environments and interconnected aquatic systems, focusing on methodologies based on optical imagery and synthetic aperture radar data and approaches that integrate machine learning techniques. The analysis underscores that GEE enhances oil spill monitoring by facilitating rapid data processing, supporting reproducible workflows, and expanding access to multi-source satellite data. Furthermore, this review highlights the necessity of incorporating very-high-resolution satellite data and achieving tighter integration of external deep learning framework within GEE to improve detection accuracy and the operational applicability in complex marine and coastal contexts. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

As global climate volatility intensifies, the built environment requires passive capacity to decouple habitability from external extremes. While vernacular architecture is a cited bioclimatic model, research often lacks long-term quantitative validation. This study addresses this gap through a multiscale framework applied to Montesinho Natural Park, Portugal. Integrating a typological survey with a one-year in situ monitoring campaign (2024–2025), the study utilises Python-based data processing to calculate statistical cross-correlations and benchmarks thermal resilience against the Portuguese Adaptive Comfort Model. Results substantiate a “Hierarchy of Filtration”: (1) Geological Scale: Location correlates statistically with lithological availability; (2) Settlement Scale: Topographical shielding suppresses the Diurnal Temperature Range (DTR) by 20.5%; (3) Envelope Scale: Traditional Stone-on-Earth assemblies exhibit a 16.5 h thermal lag, while vertical functional stratification dampens 47% of external annual temperature extremes. The study concludes that retrofitting must shift to “Balancing Inertia and Connectivity”. This approach mitigates the ‘maladaptation’ risks observed in modern lightweight interventions, providing an empirical template for passive thermal resilience applicable to resilient urban design in a warming climate. Full article

The Earth’s lithosphere, the rigid outermost layer of the planet, is composed of numerous tectonic plates of varying sizes that move over the underlying asthenosphere. The motion and interaction of these plates give rise to a wide range of geodynamic processes. Accurate monitoring of these processes is essential for maintaining a stable, up-to-date, and reliable terrestrial reference frame. This study investigates the horizontal and vertical motions of the Antarctic Plate resulting from its interactions with adjacent plates. Tectonic plate movements can be determined using several space-geodetic techniques, including Global Navigation Satellite Systems (GNSS), Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR), and Interferometric Synthetic Aperture Radar (InSAR). Among these methods, GNSS is currently the most widely used, as plate motions can be derived from continuous observations recorded at permanent stations and processed using scientific or commercial software. Within the scope of this research, GNSS data collected between 2020 and 2023 were processed using the GAMIT/GLOBK V.10.7 software package to estimate the coordinates and velocities of stations located on the Antarctic, South American, African, and Australian Plates in the ITRF14 reference frame. Furthermore, plate-fixed solutions were generated to analyze the relative motion of the Antarctic Plate with respect to neighboring plates. The results indicate that the Antarctic Plate moves at an average velocity of approximately 4–18 mm/year in the ITRF14 frame. The plate diverges from both the African and Australian Plates and exhibits predominantly strike-slip motion relative to the South American Plate. A comparison with existing global plate motion models demonstrates that the obtained velocities are consistent within 0–5 mm/year. Full article