Search Results (166)

Hyperspectral (HSI) remote sensing has emerged as a transformative technology for Earth Observation, enabling detailed assessments across different domains. The current PRISMA-aligned systematic review aims to compare classical physics-based algorithms with emerging machine learning (ML), deep learning (DL) and hybrid approaches across two relevant application domains (vegetation and raw materials), analyzing over 350 peer-reviewed studies (194 after the screening) sourced from Scopus and Web of Science and accessed in July 2025. Specific domain-related studies have been considered, excluding duplicates and studies not strictly related to HSI. Risk of bias was assessed qualitatively based on different criteria. The efficiency of the techniques was analyzed by comparing the accuracy metrics reported in the studies. The heterogeneity of the evaluation metrics used across the different categories of the studies and the underrepresentation of some application domains is the final baseline of the work. The results were synthesized, grouping by application domains and algorithm category: ML and DL models dominate vegetation applications, and physics-based methods remain prevalent in raw materials. Hybrid models achieve the highest performances across all domains. This review highlights the importance of the hyperspectral operational requirements identified for upcoming missions (CHIME, SBG and IRIDE) and points out the opportunity for algorithm development. Full article

Agriculture is a major global source of methane (CH₄), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH₄ emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH₄ flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine learning (AutoML) framework to compare their performance and process-level interpretability. The daily model demonstrated high predictive accuracy with correlation coefficients (CC) of 0.897 in 5-fold cross-validation and 0.819 in Leave-One-Year-Out (LOYO) cross-validation. Shapley Additive Explanations (SHAP) analysis revealed that while soil temperature is the dominant predictor for daily emissions (explaining ~50% of the variance), variable importance shifts significantly at finer resolutions. The hourly model exhibited a more complex multivariate structure. In this high-resolution context, although Normalized Difference Vegetation Index (NDVI) remains constant diurnally, its importance strengthens as a critical regulator of emission sensitivity, interacting with hourly meteorological fluctuations to capture short-term dynamics. The resulting 500 m daily gridded maps provide a robust foundation for national inventory refinement and spatially targeted mitigation planning. Our findings suggest that while the daily model offers optimal computational efficiency for long-term monitoring, the hourly model is superior for mechanistic understanding and detecting episodic emission events. This multi-resolution framework establishes an empirical basis for selecting appropriate temporal scales in operational greenhouse gas monitoring systems. Full article

Methane is the second most important contributor to global warming, and monitoring super-emitters from space is critical for climate mitigation. Despite the advancements in hyperspectral remote sensing, comparing methane observations across diverse imaging spectrometers remains a challenging task. Different retrieval algorithms, plume segmentation techniques and uncertainty treatments make it very hard to perform fair comparisons between different products. To overcome these difficulties, this study presents HyGAS (Hyperspectral Gas Analysis Suite), a unified, open-source framework for sensor-agnostic methane retrieval and flux estimation. Starting from the established clutter-matched-filter (CMF) formalism and a physical calibration in concentration–path-length units (ppm·m), we propagate both instrument noise and surface-driven background variability consistently from methane enhancement to Integrated Mass Enhancement (IME) and flux. The framework further includes a spectrally matched background-selection strategy, scale-aware segmentation with fixed physical criteria across resolutions, and emission-rate estimation via an IME–$U_{eff}$ approach informed by Large Eddy Simulation (LES). We demonstrate the framework on near-simultaneous observations of landfills and gas infrastructure in Argentina, Turkmenistan, and Pakistan, spanning Level-1 radiance workflows (PRISMA, EnMAP, Tanager-1) and Level-2 methane products (EMIT, GHGSat). The standardised chain enables systematic inter-comparison of methane enhancement products and reduces methodological bias, supporting robust multi-mission assessment and future global monitoring. Full article

Management of food organics and garden organics (FOGO) has emerged as a critical policy priority due to methane emissions from landfilled organics in Australia. Here, the responsibility for organics recovery rests with state and local governments, resulting in fragmented implementation, differing regulatory settings, and variable landfill levy designs. This study examines the viability of FOGO systems by drawing on three Queensland regional case studies: Lockyer Valley, Rockhampton, and Bundaberg. The study uses qualitative document analysis and comparative case study methods, supported by systems mapping, to examine interactions between policy, governance, infrastructure, and community factors. Seven key domains were identified as being central to system performance: (1) government waste strategy, (2) waste regulation, (3) political acceptance, (4) collection systems, (5) cost and funding, (6) community acceptance, and (7) compost processing. Examining these components collectively demonstrated that effective FOGO delivery relies on their alignment, with each layer reinforcing or constraining the others. To highlight waste regulation tools, the study compared landfill levies as a central economic and governance instrument in two contrasting Australian jurisdictions. In Queensland, the levy operates primarily as a fiscal tool rather than as a behavioural driver, limiting councils’ ability to invest in new services. By contrast, New South Wales’s mandatory FOGO implementation and a more mature regulatory framework have driven widespread service rollout but have also revealed the complexities of enforcing a universal policy in diverse regional contexts. The paper offers new insights into the financial and governance dynamics shaping regional waste policy, demonstrating how whole-of-system coherence is essential for advancing circular economy transitions in dispersed local contexts. Full article

Three-dimensional computational fluid dynamics (CFD) simulation was performed using the eddy-dissipation concept coupled with detailed hydrogen oxidation kinetics and a reduced two-step methane mechanism for a newly proposed W-shaped radiant tube burner (RTB). The effects of the hydrogen volume fraction (0–100%) and excess air ratio (0%, 10%, 20%) on the flame morphology, temperature distribution, and NO_X emissions are systematically analyzed. The results deliver three main points. First, a flame-shape transformation was identified in which the near-injector flame changes from a triangular attached mode to a splitting mode as the mixture reactivity increases with the transition occurring at a characteristic laminar flame speed window of about 0.33 to 0.36 m/s. Second, NO_X shows non-monotonic behavior with dilution, and 10% excess air can produce higher NO_X than 0% or 20% because OH radical enhancement locally promotes thermal NO pathways despite partial cooling. Third, a multi-parameter coupling strategy was established showing that hydrogen enrichment raises the maximum gas temperature by roughly 100 to 200 K from 0% to 100% H₂, while higher excess air improves axial temperature uniformity and can suppress NO_X if over-dilution is avoided. These findings provide a quantitative operating map for balancing stability, uniform heating, and NO_X–CO trade-offs in hydrogen-enriched industrial RTBs. Full article

This article provides an in-depth overview of mine gas emission monitoring practices in the Ostrava-Karviná Coalfield (OKR), one of the most significant regions in Central Europe affected by post-mining methane leakage. The study presents field measurement techniques, including atmogeochemical surveys, systematic methane screening in soil air, and surface emission rate monitoring using accumulation chambers. Over the course of several long-term projects, more than 43 km² of land were surveyed, and risk classification maps were developed based on measured methane concentrations and surface release rates. These data support land-use planning, the design of degasification measures, and the verification of their effectiveness. Results confirm that methane emissions persist even decades after mine closures and vary depending on atmospheric pressure and local geological conditions. The OKR methodology was also compared to international practices in Poland, Canada, and China. The article concludes with future research directions focused on automation, integration of sensor networks, and predictive modeling of gas migration in post-mining environments. Full article

This study examines the high-temperature degradation of Hastelloy C276, a corrosion-resistant nickel-based alloy, during exposure to combustion products generated by methane and 99% cracked ammonia. Using a high-pressure optical combustor (HPOC) at 4 bar and exhaust temperatures of 815–860 °C, standard tensile specimens were exposed for five hours to fully developed post-flame exhaust gases, simulating real industrial turbine or burner conditions. The surfaces and subsurface regions of the samples were analysed using scanning electron microscopy (SEM; Zeiss Sigma HD FEG-SEM, Carl Zeiss, Oberkochen, Germany) and energy-dispersive X-ray spectroscopy (EDX; Oxford Instruments X-MaxN detectors, Oxford Instruments, Abingdon, United Kingdom), while mechanical properties were evaluated by tensile testing, and the gas-phase compositions were tracked in detail for each fuel blend. Results show that exposure to methane causes moderate oxidation and some grain boundary carburisation, with localised carbon enrichment detected by high-resolution EDX mapping. In contrast, 99% cracked ammonia resulted in much more aggressive selective oxidation, as evidenced by extensive surface roughening, significant chromium depletion, and higher oxygen incorporation, correlating with increased NO_x in the exhaust gas. Tensile testing reveals that methane exposure causes severe embrittlement (yield strength +41%, elongation −53%) through grain boundary carbide precipitation, while cracked ammonia exposure results in moderate degradation (yield strength +4%, elongation −24%) with fully preserved ultimate tensile strength (870 MPa), despite more aggressive surface oxidation. These counterintuitive findings demonstrate that grain boundary integrity is more critical than surface condition for mechanical reliability. These findings underscore the importance of evaluating material compatibility in low-carbon and hydrogen/ammonia-fuelled combustion systems and establish critical microstructural benchmarks for the anticipated mechanical testing in future work. Full article

We present a novel framework for hyperspectral satellite image classification that explicitly balances spatial nearness with spectral similarity. The proposed method is trained on closed-set datasets, and it generalizes well to open-set agricultural scenarios that include both class distribution shifts and presence of novel and absence of known classes. This scenario is reflective of real-world agricultural conditions, where geographic regions, crop types, and seasonal dynamics vary widely and labeled data are scarce and expensive. The input data are projected onto a lower-dimensional spectral manifold, and a pixel-wise classifier generates an initial class probability saliency map. A kernel-based spectral-spatial weighting strategy fuses the spatial-spectral features. The proposed approach improves the classification accuracy by $7.22$–$15\%$ over spectral-only models on benchmark datasets. Incorporating an additional unsupervised learning refinement step further improves accuracy, surpassing several recent state-of-the-art methods. Requiring only 1–$10\%$ labeled training data and at most two tuneable parameters, the framework operates with minimal computational overhead, qualifying it as a data-efficient and scalable few-shot learning solution. Recent deep architectures although exhibit high accuracy under data rich conditions, often show limited transferability under low-label, open-set agricultural conditions. We demonstrate transferability to new domains—including unseen crop classes (e.g., paddy), seasons, and regions (e.g., Piedmont, Italy)—without re-training. Rice paddy fields play a pivotal role in global food security but are also a significant contributor to greenhouse gas emissions, especially methane, and extent mapping is very critical. This work presents a novel perspective on hyperspectral classification and open-set adaptation, suited for sustainable agriculture with limited labels and low-resource domain generalization. Full article

Pockmarks are ubiquitous seafloor depressions formed by the fluid/gas seepage through marine sediments, with implications for geohazards, benthic ecosystems, and climate-related processes. Despite extensive research, the mechanisms controlling the formation and spatial distribution of pockmarks are not completely understood, owing to the diverse and site-specific geo-environmental conditions. In this study, we provide a first review of over 7500 pockmarks mapped across the Mediterranean and Black seas, showing their relationship with depth range, slope gradient, seafloor lithology, proximity to tectonic faults, and sediment thickness. Our analysis reveals that pockmarks are predominantly located at intermediate water depths (100–700 m), with two main clusters around 100–200 and 500–700 m. They are commonly found on gently sloping seafloor (<4°), often clustering around slope breaks. In detail, two slope-related peaks around 1.5° and 3.5° suggest distinct geological settings for pockmark formation: sediment-rich and low-energy environments versus more dynamic slope domains. Fault proximity plays a critical role, with over 40% of pockmarks occurring within 1 km of mapped faults, indicating that structural discontinuities act as preferential fluid pathways. Pockmarks concentrate in areas with moderate Plio-Quaternary sediment thickness (300–600 m), suggesting an optimal window for overpressure generation and fluid expulsion. A strong lithological control is evident: 74% of pockmarks occur on muddy sand or sand-rich substrates. In terms of ongoing to recent seepage/activity, ~27% of pockmarks show evidence of ongoing fluid seepage (e.g., acoustic gas flares, seismic wipeouts), particularly in regions such as the Black Sea, Aegean, and Central Tyrrhenian, where faulting, salt tectonics, or hydrothermal systems enhance permeability. Conversely, pockmarks in the Western Mediterranean appear to be generally inactive and buried. These findings underscore the influence of tectono-sedimentary architecture on seafloor fluid escape and provide essential insight into methane seepage, slope stability, and benthic habitats. This pedagogic review enhances our understanding of pockmark systems and establishes a foundation for future geohazard assessment, climate studies, and marine resource exploration. Full article

Anaerobic co-digestion of agro-industrial and municipal biowastes can enhance methane production, but the optimal mixture depends on nonlinear interactions among substrates. This study evaluated swine wastewater (SW), cheese whey (CW), and the organic fraction of municipal solid waste (OFMSW) under mesophilic batch conditions to quantify composition–response relationships and identify a robust operating window. A restricted simplex-centroid mixture design was tested; linear, quadratic, and special cubic models were fitted and evaluated using ANOVA, diagnostic plots, and optimization with desirability mapping. Cumulative methane yield (CMY) ranged between 251 and 295 NmL CH₄ g VS⁻¹ in the mixtures, outperforming SW as single component. All mixtures maintained neutral pH and moderate alkalinity ratios. The special cubic model provided the best performance (high R² and R²pred) and revealed significant ternary interaction. The optimization indicated a composition near 63% SW, 10% CW, and 27% OFMSW with a predicted CMY of 300 NmL CH₄ g VS⁻¹; a high-performance band (desirability 0.90–1.00; corresponding to CMY ≥ 294.8) defined a robust window of ~60–66% SW, 6–20% CW, and 20–31% OFMSW. Overall, balanced ternary co-digestion showed synergistic effects beyond additive expectations, and the response surface model based on mixture design proved effective in capturing interactions and providing flexible guidance for practical implementation. Full article

Digital Twin (DT) technology has rapidly matured from pilot projects to integral components of advanced asset management and process optimization in the oil and gas (O&G) industry. This review provides a structured synthesis of the current state of digital twin frameworks, with a focus on offshore and topside gas-processing systems, such as those found on Floating Production Storage and Offloading (FPSO). Emphasis is placed on high-fidelity process simulations and scalable architectures integrating real-time data with advanced analytics. Drawing on over 85 peer-reviewed sources and industrial frameworks, the paper outlines modular DT architectures, encompassing steady-state and dynamic process simulations (e.g., Aspen HYSYS), reduced-order and hybrid machine learning models, co-simulation environments, and advanced equation-of-state packages (e.g., GERG-2008). Special attention is given to compressor map integration, Equations of State (EOS) selection, ISO/IEC standard compliance, and digital thread continuity. Additionally, the review explores economic and sustainability-driven DT implementations, including flare and methane mitigation, ISO 50001-aligned energy optimization, and lifecycle/decommissioning strategies. It concludes with a technical and economic assessment of DT maturity for gas compression facilities, identifying research gaps in standardization, long-term validation, and cybersecurity integration. The insights provided are intended to support decision-makers, engineers, and researchers in deploying scalable, auditable, and high-impact DT solutions across the O&G value chain. Full article

This study aimed to evaluate the incorporation of graphene-based additives, graphene nanoplatelets (GNPs) and graphene oxide quantum dots (GOQDs), into polymeric fiber matrices used as biofilm supports in anaerobic digestion systems, determining additive specific effects by benchmarking the impregnated matrices against the same nylon carrier without additives under identical operational conditions. Modified matrices were assessed through BMP assays using the liquid fraction of fruit and vegetable waste (LF-FVW) as substrate. Intermediate GNP and GOQD loadings (FM50 and FMDOT50) achieved the highest methane yields (317.9 ± 20.2 and 348.4 ± 20.0 mL CH₄/g COD_(rem)) compared with the control fiber matrix (301.0 ± 20.1 mL CH₄/g COD_(rem)). Scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) analyses confirmed nanomaterial retention on the matrix surface and interaction with microbial aggregates. Embedding the nanostructures within the fiber enhanced biofilm formation and methane yield while minimizing nanomaterial washout. Future work will focus on advanced physicochemical characterization (XRD, XPS, BET, and EDX mapping), leaching tests to assess long term stability, and scale up evaluation for full scale anaerobic digestion applications. Full article

Livestock represent a significant source of methane (CH₄) emissions, particularly in pastoral regions. However, in Xinjiang—a pivotal pastoral region of China—the spatiotemporal patterns of livestock CH₄ emissions remain poorly characterized, constraining regional mitigation actions. Here, a detailed CH₄ emissions inventory for livestock in Xinjiang spanning the period 2000–2020 is compiled. Eight livestock categories were covered, gridded livestock maps were developed, and the dynamic emission factors were built by using the IPCC 2019 Tier 2 approaches. Results indicate that the CH₄ emissions increased from ~0.7 Tg in 2000 to ~0.9 Tg in 2020, a 28.5% increase over the past twenty years. Beef cattle contributed the most to the emission increase (59.6% of total increase), followed by dairy cattle (35.7%), sheep (13.9%), and pigs (4.3%). High-emission hotspots were consistently located in the Ili River Valley, Bortala, and the northwestern margins of the Tarim Basin. Temporal trend analysis revealed increasing emission intensities in these regions, reflecting the influence of policy shifts, rangeland dynamics, and evolving livestock production systems. The high-resolution map of CH₄ emissions from livestock and their temporal trends provides key insights into CH₄ mitigation, with enteric fermentation showing greater potential for emission reduction. This study offers the first long-term, high-resolution CH₄ emission inventory for Xinjiang, providing essential spatial insights to inform targeted mitigation strategies and enhance sustainable livestock management in arid and semi-arid ecosystems. Full article

There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at Melbourne Water’s Western Treatment Plant (WTP) to routinely monitor high-density polyethylene floating covers on anaerobic lagoons. The proposed approach integrates LiDAR and photogrammetry data to enhance the accuracy and efficiency of generating digital elevation models (DEMs) and orthomosaics by leveraging the strengths of both methods. Specifically, the photogrammetric images were orthorectified onto LiDAR-derived DEMs as the projection plane to construct the corresponding orthomosaic. This method captures precise elevation points directly from LiDAR, forming a robust foundation dataset for DEM construction. This streamlines the workflow without compromising detail, as it eliminates the need for time-intensive photogrammetry processes, such as dense cloud and depth map generation. This integration accelerates dataset production by up to four times compared to photogrammetry alone, while achieving centimetre-level accuracy. The LiDAR-derived DEM achieved higher elevation accuracy with a root mean square error (RMSE) of 56.1 mm, while the photogrammetry-derived DEM achieved higher in-plane accuracy with an RMSE of up to 35.4 mm. An analysis of cover deformation revealed that the floating cover had elevated rapidly within the first two years post-installation before showing lateral displacement around the sixth year, which was also evident from a significant increase in wrinkling. This approach delivers valuable insights into cover condition that, in turn, clarifies scum accumulation and movement, thereby enhancing structural integrity management and supporting environmental sustainability at WTP by safeguarding methane-rich biogas for renewable-energy generation and controlling odours. The findings support the ongoing collaborative industry research between Monash University and Melbourne Water, aimed at achieving comprehensive structural and prognostic health assessments of these high-value assets. Full article

Accurate assessment of coalbed methane (CBM) content is essential for characterizing subsurface reservoir distribution, guiding well placement, and estimating reserves. Current methods for determining coal seam gas content mainly rely on direct laboratory measurements of core samples or indirect interpretations derived from well log data. However, conventional coring is costly, while log-based approaches often depend on linear empirical formulas and are restricted to near-wellbore regions. In practice, the relationships between elastic properties and gas content are highly complex and nonlinear, leading conventional linear models to produce substantial prediction errors and inadequate performance. This study introduces a novel method for predicting gas content in deep coal seams using a Conditional Generative Adversarial Network (CGAN). First, elastic parameters are obtained through pre-stack inversion. Next, sensitivity analysis and attribute optimization are applied to identify elastic attributes that are most sensitive to gas content. A CGAN is then employed to learn the nonlinear mapping between multiple fluid-sensitive seismic attributes and gas content distribution. By integrating multiple constraints to refine the discriminator and guide generator training, the model achieves accurate gas content prediction directly from seismic data. Applied to a real dataset from a CBM block in the Ordos Basin, China, the proposed CGAN-based method produces predictions that align closely with measured gas content trends at well locations. Validation at blind wells shows an average prediction error of 1.6 m³/t, with 83% of samples exhibiting errors less than 3 m³/t. This research presents an effective and innovative deep learning approach for predicting coalbed methane content. Full article