Search Results (5,502)

Multisource remote sensing and Earth observation (EO) products provide scalable covariates for regional mineral prospectivity mapping, but their integration with incomplete and preferentially sampled occurrence records can produce biased prediction maps. We present a bias-aware machine learning workflow for porphyry copper prospectivity mapping that integrates satellite-derived alteration proxies, topographic variables, regional geology, structural context, and accessibility-related EO layers on a harmonized 1 km grid. The workflow separates remote sensing/geological predictors from survey-effort proxies and combines this decomposition with positive-unlabeled learning, stacked ensembling, rank-optimized blending, fold-wise calibration, and spatial block cross-validation. The case study covers the eastern Central Asian Orogenic Belt (CAOB) and uses porphyry Cu occurrences together with covariates derived from ASTER short-wave infrared information, Landsat 8 reflectance, SRTM topography, VIIRS night-time lights, GHSL population data, geological units, and active fault information. Across held-out spatial folds, the final RO-BAB ensemble provides a modest but exploration-relevant improvement in ranking relative to the all-covariate XGBoost baseline, increasing PR-AUC from 0.0297 to 0.0364 and recovering 26.75% of known deposits within the top 5% of ranked cells. The resulting maps delineate coherent remote sensing-supported prospective corridors while exposing regions where predictions may be influenced by historical accessibility and recording effort. The study demonstrates how machine learning that accounts for sampling bias can improve the reliability and interpretability of remote sensing mineral prospectivity products in the presence of only reference data. Full article

The Yasuní National Park in Ecuador’s Amazon, one of Earth’s most biodiverse regions, faces unprecedented threats from oil extraction and increasing risks to Kichwa communities. This paper provides a baseline analysis of off-the-grid hydrocarbon extraction affecting ecosystems and communities living within Oil Blocks 12 and 43. Our aim is to integrate analysis of per- and polyfluoroalkyl substances (PFAS) and total petroleum hydrocarbons (TPH) to better understand the impacts of oil-extractive contamination at off-the-grid sites in sensitive Amazonian ecosystems. To achieve that, we center the Yasuní Park and Kichwa communities as a case study. Despite Kichwa environmental concerns about contamination, conventional total hydrocarbon testing has failed to detect elevated levels due to hydrocarbon degradation, necessitating testing for other contaminants associated with extractive activities, such as PFAS, a forever chemical commonly used in drilling fluids, and other contaminants from petroleum transportation via pipelines. This research was conducted at the request of and with the participation of Kichwa residents, who needed to understand the nature of contaminants in their environment. Two participatory mapping exercises were conducted in Oil Block 12 to pinpoint 16 sampling locations, given the block’s long history of contamination. In Oil Block 43, where extraction is more recent, we sampled 5 sites where community members had observed contamination in the last year. TPH and PFAS analyses were performed using EPA methods 1633 and 1664. Results revealed 7 PFAS compounds across Oil Blocks, 11 TPH compounds in Oil Block 12, and overlap between TPH and PFAS at 6 sampling locations. Contamination was detected near community housing, food gardens, and swamped forest, which is concerning because communities rely on traditional subsistence activities, including forest gathering, fishing, and gardens for survival. This is the first environmental assessment to examine the combined presence of hydrocarbons and PFAS in the Yasuní Park and the Ecuadorian Amazon, providing communities with empirical evidence of environmental contamination. Full article

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making. Full article

Windthrow and the associated damage to forests have significant economic, social, and ecological impacts including increased harvesting costs and lost revenue, safety concerns for forest workers, and restriction on public access. The impacts of wind damage also directly affect greenhouse gas profiles associated with forest lands. This paper describes a two-stage forest monitoring approach that was devised for the purposes of assessing the impacts of the storms of winter 2024/2025, which included Storms Darragh and Éowyn, on the Irish forest estate. A range of Earth Observation (EO) datasets were used to assess the extent of windthrow damage within both public and private forests across the Republic of Ireland. The total area damaged was ca. 27,400 ha out of a total forest area of ca. 800,000 ha mainly affecting the north-west of the country. Based on scenarios developed to analyse the level of harvest in conjunction with the salvage operations, it was found that there was a decline in the sink capacity of the forest estate over the period 2025–2030. However, beyond this period, the sink capacity is restored as a result of the regeneration of the forests. Full article

6G promises ultra-low latency, high data throughput, and seamless global connectivity. However, providing uninterrupted connectivity in remote and underserved regions remains a critical challenge for Terrestrial Networks (TNs), where the cost of deploying infrastructure is difficult to justify against sparse user density. Standardized under 3GPP Release 17, Non-Terrestrial Networks (NTNs) have emerged as a viable solution to close this digital divide. Among NTN platforms, High-Altitude Platform Stations (HAPS) occupy a strategic middle ground, as they deliver lower propagation delays than Low-Earth Orbit (LEO) satellites while achieving far broader coverage than TN-based Base Stations (BS). Despite these advantages, battery-powered Internet of Things (IoT) devices communicating via HAPS face a fundamental energy efficiency (EE) challenge: transmit power must be carefully managed to maximize data throughput while preserving battery life and minimizing packet queuing delays. To address this, we propose a Q-learning-based Reinforcement Learning (RL) framework. The RL agent observes the instantaneous battery level and queue state of the IoT device, and dynamically selects optimal power levels from a discrete action space across successive time slots. Unlike traditional heuristic algorithms, such as Round Robin (RR), Max Single-to-Noise Ratio (Max-SNR), and fixed-power allocation, which rely on static rules or greedy channel-based decisions, the proposed Q-learning agent learns adaptive, long-term optimal policies through direct interaction with the environment, without requiring explicit mathematical modeling of the channel or traffic dynamics. Extensive simulations demonstrate that the proposed framework achieves up to 40% higher average EE compared to all benchmark schemes, maintains consistently lower power consumption, and exhibits superior statistical reliability as evidenced by a right-shifted Cumulative Distribution Function (CDF) of EE. These results demonstrate Q-learning as a promising candidate for scalable, energy-aware power control of next-generation HAPS-assisted IoT deployments in 6G NTN ecosystems. Full article

AMn₇O₁₂ perovskites (with A = divalent elements) show complex structural and magnetic transitions including incommensurate orbital density waves and coupled/decoupled modulated spin helicity originating from charge-ordered Mn³⁺/Mn⁴⁺ cations with the 3:1 ratio at the B perovskite sites and unusual apically compressed Jahn–Teller distortions of MnO₆ octahedra. The same Mn³⁺:Mn⁴⁺ ratio can be achieved in RCuMn₆O₁₂ compositions, where R is a trivalent rare-earth cation. Therefore, the comparison in behavior of AMn₇O₁₂ and RCuMn₆O₁₂ is of interest. In this work, the A-site-ordered quadruple perovskite NdCuMn₆O₁₂ was prepared by a high-pressure high-temperature method. Its structural properties were investigated by synchrotron powder X-ray diffraction between 100 K and 350 K and laboratory powder X-ray diffraction between 5 K and 300 K. It shows a first-order structural phase transition from Im-3 symmetry (at high temperatures) to R-3 symmetry near 292 K. The structural transition is accompanied by charge (Mn³⁺/Mn⁴⁺) and unusual orbital (on the Jahn–Teller active Mn³⁺ cations located in MnO₆ octahedra) orders. However, no additional structural/orbital modulations were found at lower temperatures in comparison with AMn₇O₁₂. Magnetic properties were investigated by temperature- and field-dependent magnetization and specific heat measurements, where a ferrimagnetic transition was found near 120 K. In addition, low-temperature magnetic anomalies were observed near 20 K, probably originating from the Nd sublattice. Full article

Urbanisation-driven housing demand and the environmental burden of sewage sludge disposal highlight the need for low-carbon, circular construction materials. This study evaluates incinerated sanitary sludge ash (ISSA) as a supplementary cementitious material in stabilised earth blocks, aiming to reduce the use of cement and lime while valorising waste sludge. Lateritic soil blocks were produced with a binder-to-soil ratio of 1:7 by mass, in which ISSA partially replaced the primary stabilising binder (cement or lime) at a replacement level of 10–40% within the binder fraction. ISSA’s mineralogical characteristics were analysed using XRD and XRF. The compressive strength and density of earth blocks were measured at 7 and 28 days under curing conditions (29–36 °C; 60–75% humidity). Cement-stabilised blocks were water-cured to support cement hydration, whereas lime-stabilised blocks were air-cured to promote carbonation and pozzolanic reactions. The results, therefore, compared practical binder-specific curing regimes rather than strictly identical curing environments. ISSA exhibited moderate pozzolanic potential, and its incorporation enabled substantial partial replacement of both binders. Cement-stabilised blocks achieved higher strengths, up to 7.7 MPa, after 28 days of curing, whereas lime-stabilised blocks developed strength more gradually, reaching 4.8 MPa. Optimal mixtures were identified at 40% cement + 60% ISSA and 30% lime + 70% ISSA, balancing mechanical performance and binder reduction. A positive density–strength relationship was observed, but chemical bonding predominated over densification effects. ISSA-based stabilised earth blocks show promising structural performance and reduced binder use, but durability and life-cycle assessment need further evaluation before large-scale implementation. Full article

Digital twin (DT) technology has expanded far beyond its industrial origins, increasingly finding application across environmental and territorial domains. This review provides a structured mapping of DT deployments at environmental and territorial scales over the period 2020–2025, examining 117 peer-reviewed publications (109 applied studies and 8 review articles) through a structured 16-parameter classification framework. The review traces three major conceptual shifts in the DT paradigm: from industrial assets to living entities, from discrete systems to Earth-scale representations, and from closed deterministic models to ecological and systemic frameworks, as reflected in the emergence of ecological digital twins (EcoDTs), environmental digital twins (EDTs), and territorial digital twin (TDT) definitions. The results reveal a clear growth trajectory in DT applications across themes, with urban systems as the most consolidated application domain, and progressive diversification into marine, coastal, forestry, river/lake, and Earth system applications from 2022 onward. Institutional actors dominate production in this space, aligned with European flagship initiatives such as Destination Earth (DestinE) and the European Digital Twin of the Ocean (EDITO). The findings position and expand the notion of territorial digital twins as an evolving paradigm, underscoring both the momentum generated by EU digital and environmental policy and the need for integrated tools to answer and respond to key environmental challenges. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

Distant Retrograde Orbits (DROs) and Near-Rectilinear Halo Orbits (NRHOs), as categories of Lagrange orbits, have been selected for the construction of future deep-space navigation constellations in the Earth-Moon space due to their unique orbital trajectories and dynamical characteristics. To obtain high-precision orbit and clock solutions, the orbit determination (OD) and time synchronization (TS) performance of DRO and NRHO based on Beidou Navigation Satellite System (BDS) L-band and Ka-band signals were analyzed. Considering the constraints of onboard resources and cost, it may be infeasible to establish Ka-band links with all BDS satellites. Therefore, multiple experiments with different link configuration schemes were designed. The results show that an orbit determination accuracy of about 500 m and the time synchronization accuracy of 50 ns can be achieved using only L-band observations. In contrast, much higher accuracy can be obtained with full Ka-band links, with orbit and clock accuracy reaching 80 m and 7 ns, respectively. Moreover, higher orbit and clock accuracies can be obtained with more Ka-band links based on L-band observations. Furthermore, with the addition of the DRO-NRHO links, the orbit determination and time synchronization performance of each scheme was further improved by 15%. And the orbit determination accuracy can be better than 65 m, while the time synchronization accuracy can be better than 5 ns. Although the analysis is based on BDS signals, the proposed framework is general in nature and can be extended to other GNSS-based or future space navigation systems, providing a reference for the design of high-precision cislunar navigation and timing architectures. Full article

Floodplain wetlands are globally important ecosystems, yet altered hydrological regimes increasingly disrupt the balance between woody and non-woody vegetation. In Australia’s regulated Murray–Darling Basin, it remains unclear whether woody plant encroachment represents a persistent shift toward terrestrialisation or a dynamic process that can be periodically reversed by flooding. This study quantified long-term patterns of woody-vegetation encroachment and retreat across 32,000 ha of mapped wetlands in the mid-Murrumbidgee River floodplain from 1988 to 2023, and assessed how hydrological variability and floodplain connectivity mediate these dynamics. Using open, analysis-ready Earth observation data from Digital Earth Australia (DEA) within the Open Data Cube (ODC) framework, we combined DEA Land Cover for transition mapping, Water Observations for hydrological masking, Landsat surface reflectance for Enhanced Vegetation Index (EVI)-based spectral plausibility testing, and the Wetlands Insight Tool for qualitative temporal context. Woody-vegetation dynamics were strongly non-linear and closely linked to alternating drought and flood phases. During the Millennium Drought (2001–2009), mapped woody-cover decline exceeded 50% of wetland area in some sub-regions, whereas the post-drought recovery interval (2008–2013) produced encroachment exceeding 40% in the most affected areas. Across the full 35-year record, mean encroachment rates ranged from 85 to 250 ha yr⁻¹ among sub-regions, summing to approximately 865 ha yr⁻¹ of woody expansion across the floodplain, while retreat rates were lower overall (approximately 634 ha yr⁻¹), resulting in a net expansion of woody cover. Local hydrological connectivity strongly mediated these responses: infrequently inundated wetlands showed persistent terrestrialisation, whereas more frequently inundated, better-connected wetlands experienced periodic flood-driven retreat. Landsat-derived EVI broadly supported the mapped transitions, indicating general consistency with canopy greening and canopy decline, supporting the ecological plausibility of the detected changes. This open DEA–ODC workflow provides a transparent, transferable framework for operational wetland monitoring and demonstrates that maintaining natural flood frequency, duration, and connectivity is essential for sustaining the resilience of regulated floodplain systems. Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

The Dead Sea, the Earth’s lowest major surface water body, serves as the terminal basin for surface and groundwater flow in its surrounding region. However, anthropogenic activities and natural processes contribute to significant alterations in the lake’s area. The scope and implications of these changes remain insufficiently documented, necessitating further investigation. The CA-Markov model was used to project the Dead Sea’s surface area for 2034 and 2050. Time series of observed and future climate data, especially temperature data, under Representative Concentration Pathways (RCPs) 4.5 and 8.5, were analyzed to track climate change. Statistical analyses of the Kendall correlation matrix were performed on the observed and predicted surface areas, water levels, and temperatures. This study revealed that the Dead Sea decreased by 41.8% from 1971 to 2022, and the sea level is expected to decrease by 12.63 m and 33 m by 2034 and 2050, respectively. In addition, there were significant inverse relationships between surface area, water level, and temperature, with correlations of r = −0.79 (p = 0.001) and r = −0.82 (p = 0.001), respectively. Notably, from 2022 to 2050, the mean annual temperature is expected to increase by at least 1 °C. The long-term strategic vision for stabilizing Dead Sea water levels involves a twofold approach: (1) augmenting natural inflow by introducing 300–400 million cubic meters (MCM) from manufactured sources and channeling them into the Jordan River, and (2) reducing water extraction by Dead Sea industries by a maximum of 330 MCM. Full article

Accurate urban building energy modeling (UBEM) is constrained by mismatches between standard climate inputs and actual urban microclimate conditions. This study introduces a scalable, bottom-up, framework that integrates EnergyPlus building energy modeling simulation outputs with Earth observational and geographical-based urban morphology data, which are enhanced through machine learning techniques to improve energy demand predictions in urban settings. Applied to Los Angeles (LA), California, we evaluate the representativeness of typical meteorological year (TMYx) sampling sites against actual urban environmental conditions. We find that while satellite-derived surface temperatures show reasonable alignment with average city conditions, significant discrepancies are observed in urban form metrics such as tree cover, street cover, and building density, suggesting that TMYx stations should be placed in denser urban areas. We augment EnergyPlus simulations for 19 single-family buildings, with remote sensing data using machine learning models, to generate city-wide residential energy consumption heatmaps corrected for microclimate conditions. Models capture substantial intra-urban variation, with predicted energy use differing by approximately 10% between neighborhoods. Feature importance analysis highlights land surface temperature as a key predictor, underscoring its relevance to building energy research. We also find the majority of TMY3 sampling sites to be in low-vulnerability areas, underscoring the structural mismatch that is embedded in urban form and climate. This framework offers a scalable path for integrating urban microclimate effects into energy modeling to enable more precise and equitable energy policy and planning. Full article