Search Results (3,092)

Early and reliable detection of forest fires is essential for reducing environmental damage and ensuring public safety. Deep learning-based object detection enables automated fire monitoring across heterogeneous sensing platforms, including satellite, Unmanned Aerial Vehicle (UAV), and ground-based imaging systems. However, differences in spatial resolution, viewing geometry, and computational constraints present challenges for developing unified detection models. This study presents a comparative benchmarking analysis of the lightweight YOLOv26-nano model for forest fire detection using the FASDD dataset, comprising satellite, UAV, and ground-based imagery. A unified experimental protocol with five-fold cross-validation is adopted to ensure robustness and cross-platform generalization. Performance is enhanced through data augmentation, contrast-limited adaptive histogram equalization, and stochastic gradient descent optimization. Experimental results demonstrate that YOLOv26-nano achieves reliable detection accuracy and demonstrates promising computational characteristics under simulated resource-constrained edge-computing conditions. The proposed benchmarking framework provides a standardized reference for multi-platform fire detection and highlights the suitability of nano-scale object detection models for scalable wildfire monitoring and early-warning systems. Full article

Multi-temporal ecosystem-type maps for 1945, 1996, and 2022 were developed to examine how long-term ecosystem-type change influences regulating ecosystem services (ESs) across five Natura 2000 sites in Greece. We quantified three regulating ESs: climate regulation, hydrological regulation, and soil erosion regulation, using InVEST, and assessed multifunctionality using the combined Comprehensive Ecosystem Services Index (CESI). ES dynamics were assessed through a multi-metric framework of change indices comprising the Ecosystem Services Change Index (ESCI) and the Ecosystem Service Status Index (ESSI). In addition, we explored ES synergies and trade-offs and identified ES bundles using Self-Organizing Maps. The results showed pronounced spatial and temporal heterogeneity. Sites characterized by gradual woody expansion generally exhibited stable ES structures and modest improvements in regulating service status. In contrast, sites affected by disturbances and anthropogenic pressures (notably wildfire and urban expansion), showed persistent declines and an expansion of low-performing zones. Hydrologically dynamic systems characterized by land–water shifts exhibited persistent trade-offs between hydrological regulation and the other regulating services. Overall, ecosystem-type change analysis, combined with ES metrics quantification and spatial bundling, provided valuable insights for the assessment of the spatio-temporal dynamics of ESs. Study findings can also facilitate the preliminary translation of ES patterns into functional zones, serving as decision-support indicators for spatially targeted and adaptive Natura 2000 management measures and actions. Full article

Cork oak (Quercus suber L.) forests play a crucial role in the Mediterranean region, providing essential ecological, social, and economic services. Increasing pressure from wildfires, pests, diseases, and climate change has led to a progressive decline of these ecosystems, making the development of innovative post-stripping management strategies urgent. This study evaluates the effectiveness of kaolin application on cork oak trees immediately after cork removal in a mixed forest in Sant Celoni (Barcelona, Spain). Short- and long-term physiological responses were assessed through stomatal conductance and chlorophyll fluorescence (OJIP test), while sap flux density (J_s) was continuously monitored over a four-month period (July–October 2023) using IoT-based TreeTalker® Cyber (Nature 4.0 s.r.l., Viterbo, Italy). Proximal vegetation indices (Normalized Difference Vegetation Index, NDVI; and Normalized Difference Red Edge, NDRE) were also evaluated but showed no significant differences among treatments (p > 0.05). Kaolin-treated trees (K) maintained significantly higher photosynthetic performance and stem water transport capacity compared to untreated stripped trees (nK), with effects persisting up to 140 days after stripping. These findings support kaolin application as a viable and low-cost tool for mitigating post-stripping physiological stress in cork oak forest management. Further research across multiple sites and consecutive harvesting cycles is recommended to fully assess its long-term implications for tree vitality and cork productivity. Full article

Globally, wildfires are increasingly threatening forest ecosystems and human well-being, requiring proactive management strategies. Integrating wildfire mitigation with bioenergy production presents a dual opportunity to reduce fire risk while contributing to clean energy. This study builds upon previous work by incorporating updated annual heat load estimates from 32 off-grid communities in northern Canada to assess the amount of biomass at risk of wildfire that could be mobilized to meet local bioenergy needs. Our results reveal that energy consumption in the remote communities considered was previously significantly underestimated, with an average of 11,710 MWh per year, and a minimum and maximum of 1869 and 43,867 MWh per year, respectively. With the updated dataset, which includes both space heating and electricity energy usage, the average energy demand is approximately 300% higher than earlier estimates. Despite this substantial increase in energy consumption, the amount of biomass needed to meet local energy demand per year ranges from 352 to 8276 odt per year, representing only a small fraction (approximately 1.67% on average) of the total biomass identified as being at risk within a 10 km buffer. This corresponds to fuel treatment areas ranging from 4 to 222 hectares per year (around 51 ha on average), depending on the community. The results presented here, based on updated energy data, provide important insights into the operational feasibility of this approach. To be successful, implementation will require strong community leadership and collaboration with fire management agencies to design consistent and cost-effective fuel treatment strategies that are tailored to each community’s environmental conditions and energy needs. 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

The transition from MIS3 interstadial to the coldest stadial of the last glacial (MIS 2) marked a rapid change in the climate. Findings of multiproxy (sedimentological, MS, geochemical (AAS, XRD), micromorphological, anthracological, phytolith and malacological) studies from a loess/paleosol sequence in northeastern Hungary highlighted the transformation of a reddish-brown fossil soil layer (cambisol) to a podzolic soil with signs of iterative wildfires during the terminal part of MIS3. According to our findings, a Scots pine (Pinus sylvestris) dominated open parkland emerged on the northern slopes during the second phase of MIS3 hosted by a special reddish-brown soil. Then the last phase of MIS3 was marked by the development of spruce (Picea abies) dominated open parkland. Results further suggest that vegetation change passed a critical threshold leading to an unusually rapid expansion of spruce (within ca. 100 yr). This rapid expansion of spruce, changing the geochemistry of the litter to a more acidic state likely caused the initiation of podzolization and the transformation of the original soil. The opening of MIS2 marked not only intensive dust accumulation but also a steady decline of arboreal elements as well, leading to the emergence of a cold tundra on top of the podosol with charcoal remains. Full article

In wildfire environments, high temperatures generated by wildfires may cause thermal aging, deformation, and even burning damage to the silicone rubber sheds of composite insulators, thereby deteriorating their surface hydrophobicity and insulation characteristics. Meanwhile, ash and carbonaceous particles produced by vegetation combustion tend to accumulate on insulator surfaces, forming conductive contamination layers that reduce surface resistance, intensify leakage current activity, and increase the risk of flashover. To investigate these effects, FXBW₄-35/70 composite insulators were selected as the research object. A simulated burning test platform was established to evaluate variations in the mechanical properties of insulator sheds under wildfire conditions. In addition, the feasibility of using simulated ash was assessed. AC flashover tests were conducted on contaminated insulators to quantify the influence of ash deposition on flashover performance. Beyond confirming the thermal aging behavior of silicone rubber under wildfire exposure, this study establishes a quantitative relationship between wildfire ash deposition, equivalent contamination severity, and flashover performance. A correction model for post-fire pollution withstand voltage is further proposed, providing a practical basis for condition assessment and maintenance of transmission line insulators after wildfire events. Full article

Ash-free net heat content (AF-NHC) represents the combustible heat content of plant biomass and is an important parameter in fire behavior and fire effects modeling. Despite its widespread use, little information exists regarding seasonal and regional variation in AF-NHC among common woody fuels of the southeastern US. This study quantified seasonal and regional variation in AF-NHC among five common woody species in eastern Texas: yaupon (Ilex vomitoria), greenbrier (Smilax spp.), eastern red cedar (Juniperus virginiana), Chinese privet (Ligustrum sinense), and escarpment live oak (Quercus fusiformis). Foliage samples were collected during the dormant and growing seasons across the Pineywoods, Post Oak Savannah, and Blackland Prairie ecoregions and were analyzed using oxygen bomb calorimetry. Linear mixed-effects models evaluated species, season, and species × season effects while accounting for regional variation. AF-NHC ranged from 17.35 to 19.92 MJ kg⁻¹ and differed significantly among species and seasons, with distinct species-specific seasonal trajectories (p < 0.05). Regional variation accounted for approximately 41% of total model variance, indicating that environmental conditions influence fuel thermal properties. AF-NHC was greatest in yaupon and red cedar, intermediate in privet and greenbrier, and lowest in live oak. Although AF-NHC likely exerts less influence on fire behavior than fuel consumption and the rate of spread, species-specific differences in combustible heat content may contribute to variation in potential heat release and fuel combustibility. These findings provide baseline AF-NHC values for common eastern Texas woody fuels and improve the understanding of spatial and temporal variation in fuel thermal properties relevant to fire effects and wildfire hazard assessment. Full article

Wildfires are increasingly complex emergencies driven by climate variability, the expansion of wildland–urban interfaces, and the interaction between fire events and hazardous environments. These factors pose significant challenges for emergency management, particularly in the presence of cascading effects and multi-hazard interactions. This review examines the potential contribution of Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) frameworks to wildfire emergency management, focusing on preparedness and operational response. A narrative analysis of interdisciplinary literature was conducted to identify conceptual and operational overlaps between fire science and CBRNE-based approaches, with particular attention to command structures, hazard assessment, and response coordination. The analysis indicates that wildfire management systems often remain fragmented, with variability in procedures, training, and the integration of monitoring technologies. Evidence from CBRNE operational models suggests that structured command systems, field-based analytical capabilities, and interoperable procedures support improved situational awareness and decision-making. The review highlights how selected CBRNE principles, including structured command systems, zoning strategies, hazard characterization, and interoperability mechanisms, may address persistent gaps in complex wildfire emergency management, providing a basis for improved coordination, operational effectiveness, and system resilience. Full article

Wildfires increasingly contribute to urban particulate matter (PM) exposure, particularly fine particles (PM_2.5), through atmospheric transport processes influenced by meteorological conditions and terrain complexity. This study investigated wildfire impacts on PM₁₀ and PM_2.5 concentrations in Split, Solin, and Kaštela (Croatia) using a terrain-aware wildfire transport framework combined with statistical and machine learning (ML) approaches. Daily PM observations (2016–2024) from three air quality monitoring stations were integrated with meteorological data from six stations, wildfire polygons, and a digital elevation model (DEM). A wildfire influence index accounting for fire size, transport distance, wind conditions, and terrain-modified airflow was evaluated using Ordinary Least Squares (OLSs) regression, Random Forest (RF) modelling, and SHAP (SHapley Additive exPlanations) analysis. Results showed stronger wildfire-related effects for PM_2.5 than for PM₁₀, while meteorological variables remained the dominant predictors of PM variability. RF models improved predictive performance relative to OLS, achieving $R^2$ = 0.474 for PM_2.5 and $R^2$ = 0.416 for PM₁₀. SHAP analysis identified precipitation, temperature, and lagged wildfire transport variables as important predictors. A total of 84 wildfire events were classified as effective wildfires, with most measurable impacts occurring within approximately 30–70 km of monitoring stations, indicating that wildfire impacts on urban air quality in Mediterranean coastal environments are strongly mediated by atmospheric transport and meteorological conditions. The proposed framework demonstrates the potential of explainable and geospatially informed ML for environmental monitoring and wildfire-related urban air quality risk assessment. Full article

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R² = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems. Full article

Background: Some widely used wildland fire behavior models, like the Fire Area Simulator (FARSITE), propagate fire fronts by computing the front-normal velocity (spread rate) as a function of local inputs and the front-normal direction. Such models are sometimes observed to cause the collapse of crown fires into sharp wedge shapes that eliminate heading fire behavior. Aims: We set out to document this phenomenon and, more generally, understand the relationships between fire shapes and spread rate functions. Methods: The phenomenon is studied both mathematically and through simulation experiments. Non-smooth fire fronts are theorized mathematically by an Eikonal partial differential equation ($H(x,\tau ,D\tau )=1$), where the unknown $\tau \left(x\right)$ is the time-of-arrival function and the Hamiltonian $H(x,t,p)$ is positively homogeneous and possibly non-convex in p; convex analysis is used to study viscosity solutions in constant conditions. Results: We show that a fire spread model preserves the smoothness of fire fronts if and only if it is equivalent to using the Huygens principle. Nontrivially, this is equivalent to a convexity criterion on the inverse spread rate profile, which is then the polar dual of the Huygens wavelet; this corresponds to Hamiltonian–Lagrangian duality. The relevance of smoothness-destroying models to crown fire is debated. Exact analytical formulas are derived for fire growth in constant conditions. Conclusions: Our understanding of fire spread models is improved by solving the spread equations in more general ways than previously known. In particular, the collapse of heading crown fires into sharp shapes is now explained. Smoothness-destroying spread models cannot be simulated by algorithms based on travel time like cellular automata; their general well-definedness remains an open question. Fire modelers can use these findings to guide their search for improved crown fire models, and more generally to verify the accuracy of numerical implementations. Full article

Wildfire can alter soil organic carbon (SOC) pools and microbial pathways of carbon stabilization, but depth-dependent links between microbial necromass and stable carbon pools remain unclear. We investigated a wildfire-severity gradient in a subtropical coniferous forest in Guizhou, China, including four severity classes (unburned, light, moderate, and severe) and two soil layers (0–20 and 20–40 cm). We measured easily oxidizable organic carbon (EOC), recalcitrant organic carbon (ROC), SOC, amino sugars, microbial necromass carbon (MNC), extracellular enzyme activities, and carbohydrate-active enzyme (CAZy) functional indices. MNC peaked under moderate wildfire in both layers, increasing by 73.8% and 85.1% in the topsoil and subsoil, respectively, relative to unburned plots. After accounting for soil physicochemical properties and wildfire severity, MNC was more strongly associated with ROC and SOC in the topsoil than in the subsoil. Extracellular enzyme activities were positively associated with amino sugars and necromass pools, whereas CAZy composite indices showed weaker relationships that did not persist after false discovery rate correction. Exploratory path analysis suggested that the EOC–NAG–MNC–ROC–SOC chain was more pronounced in the topsoil, while the subsoil showed weaker chained associations and stronger direct EOC–MNC and EOC–ROC links. Overall, microbial necromass was associated with depth-dependent post-fire carbon stabilization. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Mapping wildfire patterns in Wildland–Urban Interface (WUI) areas is a fundamental tool for fire management and prevention, particularly in regions where urban expansion occurs in close proximity to natural vegetation. This mapping approach makes it possible to identify critical zones and to support more effective interventions adapted to the specific conditions of each territory. This work analyzed wildfires in the state of Tocantins, Brazil, using detailed geospatial data and advanced analysis techniques and statistics to characterize the dynamics of burned areas. Data used for the project were retrieved from MapBiomas and the Geoprocessing Laboratory of the Public Ministry of Tocantins (LABGEO), applying logistic regression models to explore the relationship between the distance of WUIs and the frequency of wildfires. The methodology covered the spatial distribution of fires and the different dynamics observed by type and size of burned area, allowing for a more detailed analysis. The results indicated significant variations in the proportion of burned areas inside and outside the WUIs, suggesting that proximity to these interfaces plays a critical role in the occurrence pattern of fires. Notably, Palmas, the state capital, stood out as one of the municipalities with the highest concentration of impacts in WUI areas, highlighting the relevance of these zones in environmental risk management. The study emphasizes the importance of adopting regional approaches that consider local specificities in the management and prevention of wildfires. The integration of geospatial data with robust statistical methodologies can guide more effective management strategies, assisting in the planning of public policies adapted to the socio-environmental dynamics of Tocantins. Full article