Search Results (195)

Understanding post-fire visible greenness change is important for assessing spatial heterogeneity in mountainous burned landscapes, but satellite observations often cannot capture local variation. This study developed a workflow using Unmanned Aerial Vehicle (UAV) Red–Green–Blue (RGB) imagery for RGB-interpreted burn severity classification and Green Leaf Index (GLI)-derived visible greenness change analysis three years after fire. The workflow integrated object-based Random Forest (RF) classification, bi-temporal GLI difference ($\Delta$GLI) detection, and terrain-stratified analysis under RGB-only conditions. Object-based multi-feature representation, including a 41-dimensional (41D) feature set of color, texture, and gradient metrics, supported local burn severity mapping, although performance gain over the 23-dimensional (23D) set was modest and not statistically significant. The burned area was dominated by high and moderate severity classes. GLI-derived analysis showed limited visible greenness increase (mean $\Delta$GLI = 0.0058), with slightly more than half of pixels being positive; high severity areas had higher $\Delta$GLI, while low severity areas showed limited or negative values. $\Delta$GLI also varied across terrain, being higher on steeper slopes, mid-to-upper elevations, and east-facing aspects. The workflow provides a practical local-scale approach for post-fire analysis using high-resolution UAV RGB imagery, with results interpreted as case-specific visible greenness patterns rather than comprehensive ecological recovery. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

Background: Experimental animal models remain central to burn research and soft-tissue reconstruction/repair, but method heterogeneity compromises reproducibility, comparability, and translation for depth/area endpoints. Objective: We aimed to map burn-induction methods and examine reproducibility, intentional depth modulation, wound-area stability, validation, and operator-dependent variability. Methods: A PRISMA-ScR review, informed by JBI guidance, was conducted without registration but with predefined questions, criteria, and charting domains. PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar were searched from inception to 30 January 2026. Eligible studies were English peer-reviewed full-text original in vivo animal studies. Two reviewers independently screened records; one charted data, another checked it. Evidence was mapped by modality, exposure-control architecture, validation, and operator-sensitive steps. Results: Studies varied by species, modality, device design, exposure settings, and severity verification. Modalities were contact, scald, steam, and radiant/infrared. Wound area was more reproducible than depth, which depended on temperature, duration, force/pressure, geometry, equilibration, anatomical site, and assessment timing. Histopathology was the main standard, sometimes complemented by morphometry, optical, or perfusion techniques. Operator-sensitive variability involved force, alignment, contact stability, template integrity, exposure geometry, source stability/environmental control. Conclusions: Burn induction is a measurement-system problem; constraining operator-sensitive variables, predefined validation timing, and quantitative variability reporting may improve validity, comparability, and translation. Full article

Wildfires are an increasingly recurrent disturbance in Mediterranean forest landscapes, yet fire severity assessment remains limited in data-scarce regions such as Lebanon. This study aims to assess wildfire severity patterns and identify the main environmental drivers influencing fire severity across the forests of Akkar, northern Lebanon, within a Digital Landscapes framework. Fire severity was mapped using the Differenced Normalized Burn Ratio (dNBR) derived from multi-temporal Landsat-8 imagery (2013–2024) processed in Google Earth Engine. Vegetation productivity was assessed through annual Net Primary Productivity (NPP), while topographic variables (elevation, slope, and aspect) were derived from a Digital Elevation Model. The results reveal heterogeneous fire severity patterns over the study period and pronounced spatial variability in NPP, with no consistent linear relationship between productivity and fire severity. Principal Component Analysis (PCA) was applied to explore multivariate relationships between fire severity, productivity, and terrain. PCA results show that the first two components explain 77.4% of the total variance, indicating that fire severity is primarily structured by topographic factors, particularly elevation and solar exposure, while vegetation productivity plays a secondary role. These findings highlight the dominant influence of terrain on wildfire severity in Mediterranean mountainous landscapes, and demonstrate the value of integrating remote sensing, cloud-based platforms, and multivariate analysis for fire assessment in data-scarce regions. The study contributes to the advancement of Digital Landscapes approaches by providing a scalable and data-driven framework for understanding fire dynamics and supporting future landscape management and risk assessment strategies. Full article

Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R² = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights. Full article

This study investigates long-term wildfire emissions and smoke-plume geospatial characteristics in Greece by analyzing a multi-pollutant dataset spanning January 2003 to August 2025. Details of emissions of carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), particulate matter (PM_2.5), organic carbon (OC), and black carbon (BC) were derived from the Global Fire Assimilation System (GFAS), which converts MODIS fire radiative power into trace gas and aerosol fluxes at 0.1° resolution, and also accounts for the land type. Burned-area statistics from the European Forest Fire Information System (EFFIS) were used for cross-validation. Data were processed into daily, monthly, annual, and cumulative time series, with spatial mapping at the municipality scale and information regarding long-term trends. The analysis shows that while there are several sizeable wildfire events in the country every year, the bulk of the total of Greek wildfire emissions for the last 23 years is attributable to a few extreme fire seasons (2007, 2021, and 2023) that produced abrupt emission surges and accounted for a disproportionate share of national totals. Analysis of spatial data identifies the areas of Evia, East Attica, Messinia, and Evros as persistent emission hotspots. Although wildfire CO₂ emissions are generally a minor fraction of Greece’s anthropogenic totals (<5%), they reached 15–17% during peak fire years. Plume-injection height analysis reveals that most smoke remains below ~1 km but can reach 3–6 km during extreme events, facilitating long-range transport. Overall, the dataset demonstrates a shift toward more intense and concentrated wildfire events in recent years, highlighting both their growing climatic relevance and their acute impacts on regional air quality. Full article

Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) providing the best discrimination of dead grassland. In spatially grouped cross-validation, NBR achieved very high accuracy for dead versus non-dead grassland, with AUC = 0.9996, precision = 1.00, recall = 0.82, and F1-score = 0.90 for Sentinel-2, and AUC = 0.9982, precision = 1.00, recall = 0.62, and F1-score = 0.76 for Landsat 9. Retrospective mapping revealed four dieback events since 2000: two short-term episodes with rapid within-season recovery (2000, 2003) and two long-term events characterized by persistent degradation and slow regeneration (2012, late 2018–2019). The largest short-term event, in 2003, affected 42.19 ha of total dieback and 96.95 ha including partially damaged or regenerating grassland. Dieback extent was negatively associated with water balance deficit, strongest for SPEI-12 (ρ = −0.548, p = 0.002), while winter frost under shallow-soil conditions likely contributed to long-term damage in 2012. Geomorphological analysis indicated that elevation, terrain curvature, and, to a lesser extent, wind exposure are the primary controls on dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change. Full article

Estimating wildfire greenhouse gas (GHG) emissions in Mediterranean landscapes is challenging due to heterogeneous fuel mosaics and limited scalability of field-based approaches. This study presents a Geographic Information System (GIS) based framework that integrates land-cover data, pre-fire biomass estimates, fire severity mapping, and established emission factors to produce spatially explicit estimates of biomass consumption and GHG emissions. Fire severity was derived from multitemporal Sentinel-2 imagery using the differenced Normalized Burn Ratio (ΔNBR) and combined with land-cover information to define vegetation–severity classes for emission estimation. A key innovation is the identification of co-occurring vegetation types within the same spatial units, allowing emissions to be quantified across vegetation mixtures rather than single classes, providing a more realistic representation of Mediterranean forests. Applied to the 2022 Bejis wildfire, pre-fire biomass within the burned area was 673,601 tons. Coniferous forests dominated, but co-occurrence with shrubland and herbaceous layers produced the highest emission contributions, highlighting the role of vegetation interactions. Total emissions were estimated at 625,938 tons of equivalent CO₂, and comparison with large-scale datasets (CAMS Global Fire Assimilation System, Global Fire Emissions Database) shows general coherence. This severity-driven, vegetation-explicit framework demonstrates robust potential for quantifying wildfire emissions across heterogeneous Mediterranean landscapes, though uncertainties remain due to pre-defined biomass, burning efficiency, emission factors, assumptions in fire severity mapping, and limited field validation. The approach can support improved regional GHG inventories and wildfire management strategies. Full article

Wildfires have become more frequent and intense worldwide due to climate change and anthropogenic activities, which is why accurate and timely burned area mapping is essential for estimating damage and effective post-fire recovery planning. Synthetic Aperture Radar (SAR) data, which operates under all weather conditions and day-night cycles, offers a reliable source for burned area mapping. In this context, several studies have explored the use of dual-polarization SAR imagery and machine learning, yet the influence of multi-date, dual-orbit pass data and texture features remained unexplored. Therefore, this study aims to assess the Sentinel-1 acquisition configurations, varying in temporal depth and orbital direction, for wildfire burned area mapping, considering the recent Palisades wildfire event as a study area. A comparative study was conducted across different scenarios to evaluate the effectiveness of using single-date versus multi-date SAR imagery, the integration of ascending and descending orbit passes, and the contribution of Grey-Level Co-occurrence Matrix texture features. The performance of Random Forest (RF) and Extreme Gradient Boosting classifiers was analyzed through the scenarios mentioned above. The single-date configuration using RF achieved an accuracy of 82.34%, F1-score of 81.43%, precision of 83.07%, recall of 80.84%, and ROC-AUC of 90.88%, whereas the multi-date approach reached 85.78%, 85.15%, 86.45%, 84.56%, and 93.28%, respectively. Our study highlights the importance of acquisition configuration and texture information for reliable SAR-based wildfire burned area assessment. Full article

The Gilé National Park (PNAG for its acronym in Portuguese), located in central Mozambique is one of the most important protected areas in the country. It is one of the last remnants of intact Miombo woodlands, providing critical habitat for endemic biodiversity. Fires are an important ecological factor in Miombo, but changes in fire regimes may compromise the stability of this ecosystem and thus, the conservation value of PNAG. This study assessed fire patterns and mapped fire risk in support of adaptive management in the PNAG. We investigated Miombo fire regime over 23 years (2001 to 2023) in terms of return interval, frequency, temporal distribution, spatial density and intensity, extent, and severity, by using two Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite products (MCD14ML active fire; MCD64A1 burned area). Primary risk drivers were established and spatial fire likelihood mapped, using the Random Forest algorithm. Analysis revealed pronounced late dry season burning (August–October) affecting approximately 60% of the PNAG annually, especially in central-northern and eastern landscapes. Remarkably, 88% of the park maintains a 1-to-2-year fire return interval across the entire fire season (May–October) while only 7% maintains return frequencies of 3-to-4-year cycles. The latter is important for maintaining Miombo ecosystem functionality. Medium to medium–high fire severity covered 98% of the total fire extension. Climate-related drivers and hunting activities were identified as key fire initiators, especially in central areas of the park. The findings demonstrate an urgent need for spatially differentiated fire management action through prescribed burning to maintain PNAG’s ecological resilience and conservation value. Full article

Wildfires pose increasing challenges for Mediterranean landscapes, making rapid and reliable mapping of burn severity essential for management and recovery planning. This study applies an integrated geospatial workflow to wildfires that occurred in Greece during the 2024 summer season. Sentinel-2-derived dNBR and RBR indices were used to map burn severity, while CORINE Land Cover and Tree Cover Density datasets provided complementary context for interpreting how severity varied across different vegetation types and canopy-density conditions. A one-way ANOVA was used to summarize differences in burned area among severity classes. The results show that low and moderate-low severity levels dominated most fire perimeters, whereas high-severity patches were spatially limited and typically coincided with densely forested areas. Validation against Copernicus Emergency Management Service data yielded an overall agreement of approximately 94%, indicating that the applied multispectral workflow produced severity extents broadly consistent with independent operational products. By applying a consistent methodology across multiple fire events, this study demonstrates the value of combining spectral indices with land-cover information for interpreting severity patterns and supporting post-fire management. The findings highlight the usefulness of freely accessible remote sensing data for timely fire assessment in Mediterranean environments and provide a basis for future multi-regional and multi-year comparisons. Full article

Twin wildfires burned over 9500 ha in Seferihisar, İzmir, western Türkiye, on 29—30 June 2025 under extreme fire weather conditions. This study reconstructs the spatiotemporal progression of the fires and examines the drivers of contrasting behaviors and burn severity. Multi-source datasets—Sentinel-2 imagery, VIIRS/MODIS thermal detections, MTG images and thermal detections, aerial photos, and ground data—were integrated to delineate progression polygons and compute rate of spread (ROS), fuel consumption (FC), and fire-line intensity (FI). Kuyucak fire showed rapid early growth, burning 3554 ha in 2.5 h (mean ROS of 5.0 km h⁻¹; mean FI of 37,789 kW m⁻¹), driven by strong northeasterly winds of 40–50 km h⁻¹, steep terrain, dense Pinus brutia fuels, and very low dead fine-fuel moisture (<6%). Kavakdere fire advanced more slowly (mean ROS of 1.6 km h⁻¹) across open grassland and cropland, yielding lower FC and FI. Synoptic analysis revealed a strong pressure-gradient-induced northeasterly wind regime linked to a mid-tropospheric geopotential height dipole between Central Europe and the Eastern Mediterranean, while WRF simulations indicated a dry boundary layer and enhanced low-level winds during peak spread. Sentinel-2 dNBR burn severity mapping showed substantial spatial variability tied to fuel and topography contrasts. Findings demonstrate how twin ignitions under similar weather conditions can produce divergent outcomes, underscoring the need for terrain- and fuel-aware strategies during extreme Mediterranean fire outbreaks. Full article

Traditional burn severity assessment methods have predominantly leveraged optical remote sensing data, yet such methods often overlook critical vegetation structural information inherent to post-fire ecosystems. Synthetic Aperture Radar (SAR) data offer structural information but are hindered by non-uniform scattering in fire-affected areas, limiting the utility of conventional decomposition techniques. Here, we introduced a metric that quantifies scattering non-uniformity by jointly considering canopy burn and ground condition non-uniformity. From this metric, we derived quantitative polarimetric features that enhance SAR-based severity estimation and demonstrated the potential to assess burn severity, with an R of 0.77 and a RMSE of 0.58. Initially, six decomposition features were extracted with the covariance matrix and then 14 feature groups were formed through metric and combination. Subsequently, sensitivity analyses were conducted for the first nine feature groups with the Composite Burn Index (CBI) values. Following this, the 14 feature groups were employed as inputs and the CBI values as outputs for random forest learning at a 7:3 training ratio to assess burn severity and generate burn severity maps. This study used the Jinyun Mountain fire in Chongqing as the primary case and eight fires in the United States as supplemental data to discuss the general applicability of the quantitative polarimetric features in assessing burn severity. Notably, the developed methodology showcased superior results within all wildfires, offering a new outlook for future burn severity assessments utilizing vegetation structure information. Full article

(1) Background: Zinc is an essential micronutrient involved in immune regulation, epithelial barrier integrity, and the host response to bacterial infections. However, the clinical benefits of zinc supplementation across different age groups remain uncertain, with heterogeneous findings and variable dosing strategies reported in the literature. (2) Objectives: To map and summarize randomized controlled trials (RCTs) evaluating zinc supplementation (either as treatment or prophylaxis) for bacterial infection outcomes in neonates, children, and adults, and to identify gaps requiring further research, including the use of zinc-based nanoparticles. (3) Eligibility Criteria: We included English-language RCTs that evaluated zinc supplementation and reported clinical outcomes related to bacterial infections. Observational studies, trials without infection-related outcomes, and studies not involving human participants were excluded. (4) Sources of Evidence: A MEDLINE (PubMed) search was conducted from 2000 to 1 November 2025 using predefined keywords related to zinc supplementation, neonates, children, adults, and bacterial infections. Reference lists of eligible articles were screened to identify additional studies. (5) Charting Methods: Data were charted for each included study, including population characteristics, zinc dosing and regimen, type of supplementation (therapeutic or prophylactic), main infection-related outcomes, and key findings. Data charting was performed independently and verified within the research team. (6) Results: A total of 51 RCTs were included: 10 in neonates, 32 in children, and 9 in adults. In neonates, therapeutic zinc supplementation as an adjunct to antibiotics showed heterogeneous results, with some studies reporting reductions in morbidity, inflammatory markers or mortality, while others found no significant differences in clinical outcomes. In children, zinc supplementation consistently reduced the duration and severity of diarrheal episodes and, in several trials, improved the resolution of respiratory infections. In adults, the evidence was limited but suggested potential benefits in selected populations, such as burn patients or those with zinc deficiency or immunologic dysfunction. Variability in zinc dosage, treatment duration, and outcome definitions limits direct comparison across studies. (7) Conclusions: Zinc supplementation appears to provide benefits in neonates and children, whereas evidence in adults remains mixed and inconclusive. Standardized, well-powered RCTs are needed to define optimal dosing strategies, identify populations most likely to benefit, and clarify the mechanisms underlying zinc’s anti-infective effects. Future research should consider the use of zinc oxide nanoparticles (ZnO-NPs) demonstrated broad-spectrum antimicrobial activity and potential synergy with antibiotics, although clinical data remain still limited. Full article

In January 2025, multiple wildfires erupted across the Los Angeles region, fueled by prolonged dry conditions and intense Santa Ana winds. Southern California has faced increasingly frequent and severe wildfires in recent years, driven by prolonged drought, high temperatures, and the expanding wildland–urban interface. These fires have caused major loss of life, extensive property damage, mass evacuations, and severe air-quality decline in this densely populated, high-risk region. This study integrates passive and active satellite observations to characterize the spatiotemporal and vertical distribution of wildfire emissions and assesses their impact on air quality. TROPOMI (Sentinel-5P) and the recently launched TEMPO geostationary instrument provide hourly high temporal-resolution mapping of trace gases, including nitrogen dioxide (NO₂), carbon monoxide (CO), formaldehyde (HCHO), and aerosols. Vertical column densities of NO₂ and HCHO reached 40 and 25 Pmolec/cm², respectively, representing more than a 250% increase compared to background climatological levels in fire-affected zones. TEMPO’s unique high-frequency observations captured strong diurnal variability and secondary photochemical production, offering unprecedented insights into plume evolution on sub-daily scales. ATLID (EarthCARE) lidar profiling identified smoke layers concentrated between 1 and 3 km altitude, with optical properties characteristic of fresh biomass burning and depolarization ratios indicating mixed particle morphology. Vertical profiling capability was critical for distinguishing transported smoke from boundary-layer pollution and assessing radiative impacts. These findings highlight the value of combined passive–active satellite measurements in capturing wildfire plumes and the need for integrated monitoring as wildfire risk grows under climate change. Full article