Search Results (59)

Topographic ridges are widely used in wildfire interpretation, suppression planning, and potential control-line design, but the claim that final fire boundaries preferentially follow ridge crests is rarely tested against local terrain availability. Remote-sensing-based burn mapping and DEM-derived terrain metrics now make this question testable at cohort scale, although most Korean wildfire studies have focused on ignition, occurrence probability, or fire-risk prediction rather than final-perimeter geometry. We therefore tested whether 118 final wildfire perimeters in the Republic of Korea (2018–2025) were non-randomly associated with ridge lines derived independently from a 30 m SRTM DEM. Sentinel-2 pre- and post-fire imagery and official fire metadata were used to generate burn masks and perimeters, which were sampled every 20 m and compared with ridge networks using a proximity endpoint (R30) and a joint distance-orientation endpoint (Aθ) under a local translate-and-rotate null model. Most fires were both more ridge-proximal and more strongly ridge-aligned than their local null perimeters, and the directional signal was the stronger of the two (mean enrichment 2.3, versus 1.5 for proximity alone). A valley-inclusive comparator showed no comparable pattern, indicating an association specific to ridges rather than to terrain lines in general. The directional signal was robust to ridge continuity, spatial scale, null design, and the exclusion of road-adjacent ridges. Because the analysis uses final mapped perimeters rather than time-resolved fire fronts, it documents a ridge-specific geometric association rather than proof that ridges stopped individual fires. These results provide an observational benchmark for terrain representation in fire-spread models. 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

A substantial proportion of wildfires in Mediterranean regions continue to be recorded without information about the cause or source of ignition, limiting our ability to understand ignition drivers and design effective prevention strategies. In this study, we develop a spatially harmonised wildfire database for mainland Spain by integrating ignition records from the Spanish General Fire Statistics (EGIF) with fire perimeters generated from satellite images. We then apply a Random Forest classifier to infer ignition causes for events lacking cause attribution. To interpret model behaviour, we use Shapley Additive Explanation (SHAP) values at both global and local scales. Results indicate that human-caused ignitions are dominant, with intentional and negligence-related fires accounting for 52.13% of all known events, although they are associated with contrasting climatic and land-use settings. Negligence-related fires tend to occur under hot, dry and windy conditions, often in agricultural interfaces, whereas intentional fires are more frequent under cooler and wetter conditions and in areas with higher population density and land-use change. Lightning-caused fires represent a small fraction of total ignitions (3%) but exhibit a distinct climatic signature, occurring primarily in sparsely populated areas, under intermediate moisture conditions, and often leading to larger burned areas. Despite strong overall model performance (F1-score = 0.82), minority classes (e.g., lightning and fire rekindling, 0.17%) remain challenging to classify, reflecting both data imbalance and uncertainty in causal attribution. Overall, the combined use of machine learning and explainable AI provides a coherent spatial characterisation of wildfire ignition drivers across mainland Spain, highlights systematic differences among ignition causes, and identifies key limitations in existing fire cause records. This framework represents a practical step towards improving fire cause information by integrating remote sensing products with field-based fire reports, thereby supporting more targeted and evidence-based fire risk management. Full article

Recently, numerous countries have experienced devastating wildfires, leading to significant destruction and loss of life. These catastrophic events highlight the shortcomings in current building regulations and testing methods. There is a pressing need for a more profound understanding of the characteristics and behaviour of large outdoor fires to address these inadequacies effectively. Wildfires can spread to structures located at the wildland–urban interface, leading to further fire propagation from one building to another. In this study, the Fire Dynamics Simulator (FDS) model was validated using experimental data from the National Institute of Standards and Technology (NIST). The experiment consisted of a target wall and a small wooden shed containing six wooden cribs as fuel, with a separation distance of 3 m. Both FDS and the experiment proved that 3 m is the safe separation distance. Different shed materials, such as steel, were used, which reduced the total heat release rate by 40% and the flame height by 20%. The effects of wind speed and direction were investigated using two wooden sheds in FDS to observe fire spread between them. The safe separation distance was 3 m for both wind speeds (2 and 5 m/s) in all directions, where the critical temperature was not reached to cause self-ignition of the second shed, except in the north direction (inward) at a speed of 5 m/s. When the separation distance increased to 3.5 m, the average heat flux at the other shed reduced to 3.18 kW/m², which did not cause self-ignition. Therefore, the safe separation distance between two structures for a wind speed of 5 m/s should be 3.5 m to mitigate the spread of fire based on the shed dimensions and the fire source load. Full article

Background: Prenatal exposure to air pollutants may increase the risk of adverse respiratory outcomes, particularly in offspring of asthmatic mothers. Evidence on wildfire-related PM_2.5 exposure during pregnancy remains limited. This study investigated associations between early gestational wildfire-related PM_2.5 exposure, infant lung function, and respiratory outcomes at 6 years. Methods: Gestational wildfire-related PM_2.5 exposure patterns were characterised using group-based trajectory modelling and linked to infant lung function outcomes. Infant respiratory measurements were obtained at six weeks of age during behaviourally defined quiet sleep using tidal-breathing flow–volume loops (TBFVL). Airway mechanics at six years were assessed by impulse oscillometry (IOS) following international guideline standards. Trajectory modelling of PM_2.5 during gestation was conducted in SAS (PROC TRAJ); all additional statistical analyses were performed in Stata IC 16.1. Results: Increased mean tidal inspiratory flow (MTIF, beta coefficient [β]: 10.51 mL/s, 95% CI: 3.66 to 17.36, p = 0.003) and peak tidal inspiratory flow (PTIF, β: 12.49 mL/s, 95% CI: 2.48 to 22.51, p = 0.014) were observed in infants born to mothers with higher wildfire-related PM_2.5 exposure during early gestation (n = 420; n = 411 not exposed, n = 9 exposed). β-coefficients from infant mixed models were then used as proxy indicators and applied in linear regression models and associated with higher reactance at 5 Hz frequency (n = 73) at 6 years of age (PTIF: β: 9.88 mL/s, 95% CI: 0.10 to 19.67, p = 0.048 and MTIF: β: 13.43 mL/s, 95% CI: 1.43 to 25.44, p = 0.029). PTIF was further associated with asthma diagnoses at 6 years (aOR: 1.36, 95% CI: 1.07 to 1.73, p = 0.012; n = 259; n = 116 asthma). Conclusion: Early gestational exposure to wildfire-related PM_2.5 may be linked with altered respiratory patterns in infancy and differences in airway reactance during childhood. Findings also suggest a relationship with asthma risk, although mechanisms remain uncertain. Full article

This study presents the Moisture Dynamic Model (MDM), a new semi-physical formulation designed to estimate Fuel Moisture Content (FMC) using only air temperature and relative humidity. The core innovation of this work lies in the introduction of an Arrhenius-type kinetic term into a fuel moisture prediction framework, allowing temperature-driven desorption processes to be explicitly represented within a lightweight operational model. Its predictive capability was assessed through experimental campaigns on Cistus monspeliensis shrublands in Corsica and validated using FireStar3D simulations. A second major contribution is the coupling of the MDM with the physical wildfire simulator FireStar3D to quantify how FMC prediction errors propagate into fire spread predictions. The MDM accurately reproduced the seasonal variability of FMC, achieving strong correlation with experimental data during dry summer periods. When coupled with FireStar3D, discrepancies in the predicted rate of spread remained below 4% under high-risk meteorological conditions. While the model performed robustly during summer, its accuracy decreased during spring, when rainfall events and microclimatic variability introduced greater uncertainty. This work represents a proof of concept demonstrating the potential of a simple physically interpretable FMC model for operational fire behaviour prediction. Full article

Water from mountain regions is a crucial natural resource because of its major economic, social, and environmental significance. Wildfires may disrupt the normal functioning of the hydrological cycle, limiting water resources for nearby areas and degrading water quality in mountainous regions as contaminants enter water systems from the burning of vegetation and soil. In August 2022, the Serra da Estrela mountain, situated in the Mediterranean biogeographical region, was affected by a large wildfire that consumed 270 km² of the Serra da Estrela Natural Park, often resulting in severe vegetation burn, although the soil burn severity was low to moderate in most of the area. The research objective is to assess the impact of this wildfire on the hydrogeochemistry of groundwater and surface water in the Manteigas-Covão da Ametade sector of Serra da Estrela in the context of a wildfire with limited soil burn severity. Groundwater and surface water samples were collected from October 2022 to September 2023 and were analyzed for pH, Total Organic Carbon, electrical conductivity, major ions, potentially toxic elements, iron (Fe), and Polycyclic Aromatic Hydrocarbons. A stormy event in mid-September 2022, occurring before the first sampling campaign, removed most of the ash layer and likely caused transient hydrogeochemical changes in streams. However, the analytical results from the sampled waters revealed that the post-wildfire hydrogeochemical effects are not evident. In fact, the hydrogeochemical changes observed in groundwater and surface water appear to be primarily influenced by the regular hydrological behaviour of aquifers and streams. The low to moderate soil burn severity, the high soil hydrophobicity, and the temporal distribution of precipitation explain why the hydrogeochemistry was primarily influenced by groundwater flow paths, the types and weathering of local lithologies, soil types, dilution effects following wet periods, and seasonal changes in the tributaries feeding into streams, rather than by post-wildfire effects. These outcomes provide valuable insights for water resource management and for developing strategies to mitigate wildfire impacts in mountainous environments. Full article

Wildfires are among the most frequent and destructive natural hazards in Europe, particularly in Portugal. They have severe impacts on forests, ecosystems, human health, and infrastructure, leading to substantial socio-economic losses due to firefighting efforts and post-fire recovery costs. Moreover, wildfires cause numerous casualties each year, highlighting the need for a deeper understanding of fire behaviour to support effective firefighting strategies and ensure the safety of both responders and communities. This study examines the influence of wind flow velocity variation on fire behaviour, both in the presence and absence of an edge wall in the fuel bed, aiming to replicate the characteristics of real wildfire fronts at a laboratory scale. Experimental tests were conducted at the Forest Fire Research Laboratory (LEIF) of the University of Coimbra using a shrub mixture, composed of Ulex europaeus, Baccharis trimera, and Caralluma adscendens, representing one of the most common fine fuels in Portuguese forested landscapes. This research provides novel insights by experimentally analyzing the combined effect of wind velocity variation and fuel bed edge presence on fire behaviour, paving the way for future comparisons with numerical simulations and real wildfire fronts. As expected, increasing wind velocity and the presence of fuel bed edges resulted in higher values of rate of spread, fireline intensity, and fire intensity. Full article

Surrogate modelling is increasingly used in engineering to improve computational efficiency in complex simulations. However, traditional data-driven surrogate models often face limitations in generalizability, physical consistency, and extrapolation—issues that are especially critical in safety-sensitive fields such as fire safety engineering (FSE). To address these concerns, physics-informed surrogate modelling (PISM) integrates physical laws into machine learning models, enhancing their accuracy, robustness, and interpretability. This systematic review synthesises existing applications of PISM in FSE, classifies the strategies used to embed physical knowledge, and outlines key research challenges. A comprehensive search was conducted across Google Scholar, ResearchGate, ScienceDirect, and arXiv up to May 2025, supported by backward and forward snowballing. Studies were screened against predefined criteria, and relevant data were analysed through narrative synthesis. A total of 100 studies were included, covering five core FSE domains: fire dynamics, wildfire behaviour, structural fire engineering, material response, and heat transfer. Four main strategies for embedding physics into machine learning were identified: feature engineering techniques (FETs), loss-constrained techniques (LCTs), architecture-constrained techniques (ACTs), and offline-constrained techniques (OCTs). While LCT and ACT offer strict enforcement of physical laws, hybrid approaches combining multiple strategies often produce better results. A stepwise framework is proposed to guide the development of PISM in FSE, aiming to balance computational efficiency with physical realism. Common challenges include handling nonlinear behaviour, improving data efficiency, quantifying uncertainty, and supporting multi-physics integration. Still, PISM shows strong potential to improve the reliability and transparency of machine learning in fire safety applications. Full article

The 2019–2020 Black Summer bushfire in Australia is a good example of the frequent and severe bushfires (wildfires) observed around the world in recent years. Fire-enhanced winds and fire–wind interactions during those bushfire events have caused increased wind velocities in the vicinity of a bushfire front. This can lead to a premature failure of the building envelope, making it vulnerable to ember attack and direct flame contact. In Australia, crest-fixed cold-formed steel (CFS) claddings are commonly used for buildings in bushfire-prone areas because of their non-combustibility. Therefore, this study investigated the pull-through failure behaviour of corrugated CFS claddings under wind uplift/suction loading at elevated temperatures, simulating fire-enhanced winds during a bushfire by means of experimental and numerical studies. Experimental results showed a negligible influence of the thermal expansion of the cladding system on the pull-through failure behaviour, while a significant decrease in pull-through capacity was observed with increasing temperatures. Suitable finite element models were developed, validated and used in a detailed numerical parametric study. Based on the findings from these studies, a design equation was proposed for the pull-through capacity of the crest-fixed corrugated claddings at elevated temperatures. Full article

Estimating live fuel moisture content (LFMC) is critical for assessing vegetation flammability and predicting potential fire behaviour, thus providing relevant information for wildfire prevention and management. Previous research has demonstrated that empirical modelling based on spectral data derived from remote sensing is useful for retrieving LFMC. However, these types of models are often very site-specific and generally considered difficult to extrapolate. In the present study, we analysed the performance of empirical models based on Sentinel-2 spectral data for estimating LFMC in fire-prone shrubland dominated by Cistus ladanifer. We used LFMC data collected in the field between June 2021 and September 2022 in 27 plots in the region of Andalusia (southern Spain). The specific objectives of the study included (i) to test previous existing models fitted for the same shrubland species in a different study area in the region of Madrid (central Spain); (ii) to calibrate empirical models with the field data from the region of Andalusia, comparing the model performance with that of existing models; and (iii) to test the capacity of the best empirical models to predict decreases in LFMC to critical threshold values in historical wildfire events. The results showed that the empirical models derived from Sentinel-2 data provided accurate LFMC monitoring, with a mean absolute error (MAE) of 15% in the estimation of LFMC variability throughout the year and with the MAE decreasing to 10% for the critical lower LFMC values (<100%). They also showed that previous models could be easily recalibrated for extrapolation to different geographical areas, yielding similar errors to the specific empirical models fitted in the study area in an independent validation. Finally, the results showed that decreases in LFMC in historical wildfire events were accurately predicted by the empirical models, with LFMC <80% in this fire-prone shrubland species. Full article

Flammable litter such as Pinus koraiensis needle accumulation increases the risk of wildfire. In the event of a high-intensity fire, forest resources can be severely damaged. To reduce the occurrence of forest fires, it is important to reduce loads and modify structures. This study conducted 270 indoor degradation experiments to determine physicochemical properties of Pinus koraiensis during the combustion degradation process. Combustion degradation treatment variables were constructed with different durations, Trichoderma fungi, and doses. The results show that the physicochemical properties of flammable litter changed significantly after degradation, with a maximum degradation rate of 11. The degradation rate was affected by time and microbial agents, but there was no significant difference between different doses. Principal component analysis was used to determine overall combustibility, and it was found that a dose of 4 mL of Trichoderma harzianum had the best effect on degradation for 42 days, reducing combustibility by 203%. It was found that the 6ml composite mould had the best inhibitory effect on fire spread rate, reaching the lowest value. After 42 days, the flame intensity of 4 mL Trichoderma harzianum reached its lowest value of 57.17 kw/m, which represents a decrease of 54% compared to the initial value. Similarly, the flame’s length reached its lowest value of 4.91 cm, which represents a decrease of 31% compared to the same period last year. The aim of this study is to establish the relationship between time, microbial agents, dosage, flammable physical and chemical properties, overall flammability, and potential fire behaviour. The values of the goodness-of-fit index and the comparative fit index are both >0.98, and the values of the standardised root mean square residual and the approximate root mean square error are both <0.05. This study has a positive effect on accelerating the decomposition of combustibles, reducing the content of flammable components, reducing flammability and potential fire behaviour, and reducing the risk of forest fires. It is of great significance for strengthening natural resource management and forest ecological conservation. Full article

Coastal mallee shrubland wildfires present challenges for accurately predicting fire spread sustainability and rate of spread. In this study, we assess the fuel drivers contributing to coastal mallee shrubland fires. A review of shrubland fire behaviour models and fuel metrics was conducted to determine the current practice of assessing shrubland fuels. This was followed by workshops designed to elicit which fuel structural metrics are key drivers of fire behaviour in coastal mallee shrublands. We found that height is the most commonly used fuel metric in shrubland fire models due to the ease of collection in situ or as a surrogate for more complex fuel structures. Expert workshop results suggest that cover and connectivity metrics are key to modelling fire behaviour in coastal mallee shrublands. While height and cover are frequently used in fire models, we conclude that connectivity metrics would offer additional insights into fuel drivers in mallee shrublands. Future research into coastal mallee fire behaviour should include the measurements of fuel height, cover, and horizontal and vertical connectivity. Full article

Background: In 2023, wildfires led to widespread destruction of property and displacement of residents in Alberta and Nova Scotia, Canada. Previous research suggests that wildfires increase the psychological burden of impacted communities, necessitating population-level interventions. Cognitive Behavioural Therapy (CBT)-based text message interventions, Text4HopeAB and Text4HopeNS, were launched in Alberta and Nova Scotia, respectively, during the 2023 wildfire season to support the mental health of impacted individuals. Objectives: The study examines the effectiveness of Text4HopeNS and Text4HopeAB in alleviating psychological symptoms and improving wellbeing among subscribers. Methods: The study involved longitudinal and naturalistic controlled trial designs. The longitudinal study comprised subscribers who completed program surveys at baseline and six weeks post-enrolment, while the naturalistic controlled study compared psychological symptoms in subscribers who had received daily supportive text messages for six weeks (intervention group) and new subscribers who had enrolled in the program during the same period but had not yet received any text messages (control group). The severity of low resilience, poor mental wellbeing, likely Major Depressive Disorder (MDD), likely Generalized Anxiety Disorder (GAD), likely Post-Traumatic Stress Disorder (PTSD), and suicidal ideation were measured on the Brief Resilience Scale (BRS), the World Health Organization-5 Wellbeing Index (WHO-5), Patient Health Questionnaire 9 (PHQ-9), Generalized Anxiety Disorder 7 (GAD-7) scale, PTSD Checklist–Civilian Version (PCL-C), and the ninth question on the PHQ-9, respectively. The paired and independent sample t-tests were employed in data analysis. Results: The results from the longitudinal study indicated a significant reduction in the mean scores on the PHQ-9 (−12.3%), GAD-7 (−14.8%), and the PCL-C (−5.8%), and an increase in the mean score on the WHO-5, but not on the BRS, from baseline to six weeks. In the naturalistic controlled study, the intervention group had a significantly lower mean score on the PHQ-9 (−30.1%), GAD-7 (−29.4%), PCL-C (−17.5%), and the ninth question on the PHQ-9 (−60.0%) which measures the intensity of suicidal ideation, and an increase in the mean score on the WHO-5 (+24.7%), but not on the BRS, from baseline to six weeks compared to the control group. Conclusions: The results of this study suggests that the Text4Hope program is an effective intervention for mitigating psychological symptoms in subscribers during wildfires. This CBT-based text messaging program can be adapted to provide effective support for individuals’ mental health, especially in the context of traumatic events and adverse experiences such as those induced by climate change. Policymakers and mental health professionals should consider these findings when shaping strategies for future disaster response efforts, emphasizing the value of scalable and culturally sensitive mental health interventions. Full article

Spatial and temporal estimates of burned areas are often used to model greenhouse gas and air pollutant emissions from fire events that occur in a region of interest and over specified time frames. However, fire behaviour, fuel consumption, fire severity, and ecological effects vary over both time and space when a fire grows across varying fuels and topography under different environmental conditions. We developed a method for estimating the progression of individual wildfires (i.e., day-of-burn) employing ordinary kriging of a combination of different satellite-based active fire detection data sources. We compared kriging results obtained using active fire detection products from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and combined MODIS and VIIRS data to study how inferences about a wildfire’s evolution vary among data sources. A quasi-validation procedure using combined MODIS and VIIRS active fire detection products that we applied to an independent data set of 37 wildfires that occurred in the boreal forest region of the province of Ontario, Canada, resulted in nearly half of each fire’s burned area being accurately estimated to within one day of when it actually burned. Our results demonstrate the strengths and limitations of this geospatial interpolation approach to mapping the progression of individual wildfires in the boreal forest region of Canada. Our study findings highlight the need for future validations to account for the presence of spatial autocorrelation, a pervasive issue in ecology that is often neglected in day-of-burn analyses. Full article