Search Results (139)

While geostationary satellites can provide continuous near-real-time observations, their low spatial resolution makes it difficult to detect small wildfires. Conversely, polar-orbiting satellites are capable of observing small wildfires at high spatial resolution, but can operate only within restricted observation periods. To improve wildfire spread prediction accuracy using polar-orbiting satellite observations, this paper proposes a novel methodology to accurately reproduce wildfire perimeters observed at specific time points by these satellites. The approach employs a wildfire spread simulator combined with a rate of spread (ROS) adjustment factor. The proposed algorithm derives ROS adjustment factors for each fuel model based on differential evolution, achieving up to a 0.4 increase in the Sørensen index when reproducing wildfire perimeter data at given observation times. Incorporating these factors into simulator-based predictions allows comprehensive consideration of external factors affecting wildfire propagation, which have not been sufficiently accounted for in previous methods. Moreover, considering the frequent occurrence of small wildfires in Korea, this study establishes a mapping between major species of trees in Korea and corresponding Fire Behavior Fuel Models (FBFMs). This serves as an example of appropriately matching major species of trees to FBFMs for wildfire spread prediction in countries where FBFMs have not been previously applied. The methodology’s effectiveness is demonstrated using wildfire perimeter data from polar-orbiting satellite observations and ignition points of recent wildfires in Korea. The proposed algorithm is expected to significantly enhance wildfire response by swiftly providing critical information for accurate wildfire spread prediction. This will facilitate prompt and precise countermeasures for small wildfires independent of external conditions such as weather. Full article

In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behavior models, such as Fire Dynamics Simulator (FDS), can represent heterogeneous fuels and simulate fire behavior processes with greater detail than conventional models. However, they require accurate information about species composition and 3D distribution of fuel mass and bulk density at the voxel level. Working in a Mediterranean ecosystem study area we developed a methodology based on the use of geometric and spectral features from UAS-based digital aerial photogrammetric point clouds for (i) species segmentation and classification using machine learning algorithms, (ii) generation of biomass prediction models at individual plant level, and (iii) creation of 3D fuel scenarios and modeling wildfire behavior. Field measurements were conducted on 22 circular plots with a radius of 5 m. Data from the field measurements, combined with species-specific allometric equations, were used for the evaluation of classification and prediction models. Fire behavior variables such as rate of spread, heat release rate, and mass loss rate were monitored and assessed as outputs from 20 different scenarios using FDS. The overall species classification accuracy was 80.3%, and the biomass regression R² values obtained by cross-validation were 0.77 for Pinus halepensis and 0.83 for Anthyllis cytisoides. These results are encouraging further improvement based on the integration of sensors onboard UAS, and the characterization of fuels for fire behavior modeling. These high-resolution fuel representations can be coupled with standard risk assessment tools, enabling fire managers to prioritize treatment areas and plan for resource deployment. Full article

The increasing complexity of urban structures has significantly elevated the risk and severity of façade fires in high-rise buildings. Unlike traditional models assuming continuous fuel beds, real-world fire scenarios often involve discrete combustible materials arranged in discrete fuel matrices. This study presents a systematic investigation into the influence of lateral spacing on vertical flame propagation behavior. Laboratory-scale experiments were conducted using vertically oriented polymethyl methacrylate (PMMA) fuel arrays under nine different spacing configurations. Results reveal that lateral spacing plays a critical role in determining flame spread paths and intensities. Specifically, with a vertical spacing fixed at 8 cm, a lateral spacing of 10 mm resulted in rapid flame growth, reaching a peak flame height of approximately 96.5 cm within 450 s after ignition. In contrast, increasing the lateral spacing to 15 mm significantly slowed flame development, achieving a peak flame height of just under 90 cm at approximately 600 s. This notable transition in flame dynamics is closely associated with the critical thermal boundary layer thickness (~11.5 mm). Additionally, at 10 mm spacing, a chimney-like effect was observed, enhancing upward air entrainment and resulting in intensified combustion. These findings reveal the coupled influence of geometric configuration and heat transfer mechanisms on façade flame propagation. The insights gained provide guidance for cladding system design, suggesting that increasing lateral separation between combustible elements may be an effective strategy to limit flame spread and enhance fire safety performance in buildings. Full article

Live fuel moisture content (LFMC) significantly influences fire activity and behavior over different spatial and temporal scales. The ability to estimate LFMC is important to improve our capability to predict when and where large wildfires may occur. Currently, there is a gap in providing reliable near-real-time LFMC estimates which can contribute to better operational decision-making. The objective of this work was to develop near-real-time LFMC estimates for operational purposes in Portugal. We modelled LFMC using Random Forests for Portugal using a large set of potential predictor variables. We validated the model and analyzed the relationships between estimated LFMC and both fire size and behavior. The model predicted LFMC with an R² of 0.78 and an RMSE of 12.82%, and combined six variables: drought code, day-of-year and satellite vegetation indices. The model predicted well the temporal LFMC variability across most of the sampling sites. A clear relationship between LFMC and fire size was observed: 98% of the wildfires larger than 500 ha occurred with LFMC lower than 100%. Further analysis showed that 90% of these wildfires occurred for dead and live fuel moisture content lower than 10% and 100%, respectively. Fast-spreading wildfires were coincident with lower LFMC conditions: 92% of fires with rate of spread larger than 1000 m/h occurred with LFMC lower than 100%. The availability of spatial and temporal LFMC information for Portugal will be relevant for better fire management decision-making and will allow a better understanding of the drivers of large wildfires. Full article

Accurate wildfire spread modeling critically depends on the representation of wind dynamics, which vary with terrain, land cover characteristics, and height above ground. Many fire spread models are often coupled with coarse atmospheric grids that cannot explicitly resolve the vertical variation of wind near flame heights. Rothermel’s fire spread model, a widely used parameterization, relies on midflame wind speed to calculate the fire rate of spread. In coupled fire atmosphere models such as the Community Fire Behavior Model (CFBM), users are required to specify the midflame height before running a fire spread simulation. This study evaluates the use of logarithmic interpolation wind adjustment factors (WAF) for improving midflame wind speed estimates, which are critical for the Rothermel model. We compare the fixed wind height approach that is currently used in CFBM with WAF-derived winds for unsheltered and sheltered surface fire spread. For the first time in this context, these simulations are validated against satellite and ground-based observations of fire perimeters. The results show that WAF implementation improves fire perimeter predictions for both grass and canopy fires while reducing the overestimation of fire spread. Moreover, this approach solely depends on the fuel bed depth and estimation of canopy density, enhancing operational efficiency by eliminating the need for users to specify a wind height for simulations. Full article

This research investigates the dynamic behavior of flames generated from methyl laurate droplets using advanced deep learning techniques. By analyzing high-resolution image sequences, we aim to extract valuable insights into the flame’s evolution, including its ignition, growth, and extinction phases. YOLOv9, a state-of-the-art object detection model, is employed to automatically segment and track key flame features such as flame shape, size, and intensity. Our results demonstrate a high accuracy of 0.97 and 0.92 mAP for automatic object segmentation of the flame and droplet. Through quantitative analysis of these features, we seek to gain a deeper understanding of the underlying physical processes governing droplet combustion. The results of this study can contribute to the development of more accurate and efficient combustion models, as well as improved fire safety strategies. This study investigates the combustion dynamics of methyl laurate droplets at atmospheric pressure, providing foundational insights into its behavior as a biodiesel fuel. Future research under high-pressure conditions is recommended to better understand its performance in practical engine applications. Full article

The global shift towards clean energy has been driven by the need to address global warming, which is exacerbated by economic expansion and rising energy demands. Traditional fossil fuels, particularly coal, emit more pollutants than other fuels. Recent studies have shown significant efforts in using biomass as a replacement or co-firing it with coal. This is because biomass, being a solid fuel, has a combustion mechanism similar to that of coal. This study investigates the co-firing behavior of pulverized coal and biomass in a semi-combustion furnace with a 500 kW heat input, comprising a pre-chamber and a main combustion chamber. Using computational fluid dynamics (CFD) simulations with ANSYS Fluent 2020 R1, the study employs species transport models to predict combustion reactions and discrete phase models (DPM) to track fuel particle movement. These models are validated against experimental data to ensure accurate predictions of mixed fuel combustion. The research examines various biomass-to-coal ratios (0%, 25%, 50%, 75%, and 100%) to understand their impact on combustion temperature and emissions. Results show that increasing the biomass ratio reduces combustion temperature due to biomass’s lower heating value, higher moisture content, and larger particle size, leading to less efficient combustion and higher CO emissions. However, this temperature reduction also correlates with lower NO_x emissions. Additionally, biomass’s lower nitrogen and sulfur content contributes to further reductions in NO_x and SO₂ emissions. Despite biomass having higher volatile matter content, which results in quicker combustion, coal demonstrates a higher carbon burnout rate, indicating more efficient carbon combustion. The study concludes that while pure coal combustion efficiency is higher at 87.7%, pure biomass achieves only 77.3% efficiency. Nonetheless, increasing biomass proportions positively impacts emissions, reducing harmful NO_x and SO₂ levels. Full article

Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform strategic approaches to mitigate or prevent large-scale wildfires and respond to such incidents. Field surveys for fuel load assessment are labor-intensive, time-consuming, and as such, cannot be repeated frequently across large territories. On the contrary, remote-sensing sensors quantify fuel load in near-real time and at not only local but also regional or global scales. We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. Also, upcoming research initiatives aim to advance current methodologies by combining photogrammetry and uncrewed aerial vehicles (UAVs) to accurately map fuel loads at sub-meter scales. However, challenges persist in securing data for algorithm calibration and validation and in achieving the desired accuracies for surface fuels. Full article

The characterization and mapping of fuel types is one of the most important factors to consider in the development of accurate fire behavior models. This study introduces a new methodology for generating a fuel map that can be easily updated on an annual basis. The method involves identifying associations between the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover MCD12Q1 classes and the fuel-type classes categorized by the Canadian Fire Behavior Prediction System (FBP). For this purpose, MCD12Q1 Land Cover Type 1 data (MODIS LCM) were collected for the Canadian region. Concurrently, the Canadian fuel-type map implemented in the Fire Behavior Prediction System (FBP FTM) served as the reference dataset. Both MODIS LCM and FBP FTM were reclassified into a new Canadian FTM (NC-FTM) based on seven fuel-type classes. The method involves three key steps: (1) adapting MODIS LCM and FBP FTM for the classification of the Canadian region, (2) removing ambiguity, and (3) characterizing and assessing the accuracy of the new fuel-type classification using a confusion matrix classification algorithm. The achieved accuracy for the new classification exceeds 85%, highlighting the effectiveness of the approach. The use of MODIS LCM offers a cost-effective method for the annual characterization and mapping of fuel types, providing a practical improvement to the FBP model for Canada. Furthermore, with the proposed methodology, a fuel-type map can be generated for other specific areas of interest in the boreal region. Full article

This study investigates the role of prescribed grazing as a sustainable fire prevention strategy in Mediterranean ecosystems, with a focus on Sardinia, an area highly susceptible to wildfires. Using FlamMap simulation software, we modeled fire behavior across various grazing and environmental conditions to assess the impact of grazing on fire severity indicators such as flame length, rate of spread, and fireline intensity. Results demonstrate that grazing can reduce fire severity by decreasing combustible biomass, achieving reductions of 25.9% in fire extent in wet years, 60.9% in median years, and 45.8% in dry years. Grazed areas exhibited significantly lower fire intensity, particularly under high canopy cover. These findings support the integration of grazing into fire management policies, highlighting its efficacy as a nature-based solution. However, the study’s scope is limited to small biomass fuels (1-h fuels); future research should extend to larger fuel classes to enhance the generalizability of prescribed grazing as a fire mitigation tool. Full article

Forest fuels are the core element of fire management; each fuel component plays an important role in fire behavior. Therefore, accurate determination of their characteristics and spatial distribution is crucial. This paper introduces a novel method for mapping the spatial distribution of litter and duff fuel loads using data collected by unmanned aerial vehicles. The approach leverages a very high-resolution multispectral data analysis within a machine learning framework to achieve precise and detailed results. A set of vegetation indices and texture metrics derived from the multispectral data, optimized by a “Variable Selection Using Random Forests” (VSURF) algorithm, were used to train random forest (RF) models, enabling the modeling of high-resolution maps of litter and duff fuel loads. A field campaign to measure fuel loads was conducted in the mixed forest of the natural protected area of “Sierra de Quila”, Jalisco, Mexico, to measure fuel loads and obtain field reference data for calibration and validation purposes. The results revealed moderate determination coefficients between observed and predicted fuel loads with R² = 0.32, RMSE = 0.53 Mg/ha for litter and R² = 0.38, RMSE = 13.14 Mg/ha for duff fuel loads, both with significant p-values of 0.018 and 0.015 for litter and duff fuel loads, respectively. Moreover, the relative root mean squared errors were 33.75% for litter and 27.71% for duff fuel loads, with a relative bias of less than 5% for litter and less than 20% for duff fuel loads. The spatial distribution of the litter and duff fuel loads was coherent with the structure of the vegetation, despite the high complexity of the study area. Our modeling approach allows us to estimate the continuous high-resolution spatial distribution of litter and duff fuel loads, aligned with their ecological context, which dictates their dynamics and spatial variability. The method achieved acceptable accuracy in monitoring litter and duff fuel loads, providing researchers and forest managers with timely data to expedite decision-making in fire and forest fuel management. Full article

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones. Full article

The flashover mechanism in an aircraft cargo compartment under low pressure was investigated in this study. A series of fire experiments were conducted in a scale model of a one-quarter volume FAA standard aircraft cargo compartment at 96 kPa and 60 kPa. The ignition of single-walled corrugated cardboard was chosen as the criterion of the flashover. The influence of different fire sizes and fuel types on the flashover was studied by comparing the average temperature of the smoke layer, the radiation heat flux at the floor level, and the heat release rate of the fire source. The critical condition and behavior of the flashover were analyzed. The results show that under low pressure, the flashover occurs at a higher temperature and radiation heat flux. Increasing the fire source size brings the flashover forward. At 60 kPa and 96 kPa, the cardboard ignites under a flashover when the average temperature of the smoke layer reaches 551 °C and 450 °C, and the average radiant heat flux at the floor level reaches 19.6 kW/m² and 14 kW/m², respectively. In addition, the minimum fire size for a flashover is directly proportional to the heat of evaporation and inversely proportional to the heat of combustion. Full article

Fire models predict fire behavior and effects. However, there is a need to know how confident users can be in forecasts. This work developed a probabilistic methodology based on ensemble simulations that incorporated uncertainty in weather, fuel loading, and model physics parameters. It provided information on the most likely forecast scenario, confidence levels, and potential outliers. It also introduced novel ways to communicate uncertainty in calculation and graphical representation and applied this to diverse wildfires using ensemble simulations of the CAWFE coupled weather–fire model ranging from 12 to 26 members. The ensembles captured many features but spread was narrower than expected, especially with varying weather and fuel inputs, suggesting errors may not be easily mitigated by improving input data. Varying physics parameters created a wider spread, including identifying an outlier, underscoring modeling knowledge gaps. Uncertainty was communicated using burn probability, spread rate, and heat flux, a fire intensity metric related to burn severity. Despite limited ensemble spread, maps of mean and standard deviation exposed event times and locations where fire behavior was more uncertain, requiring more management or observations. Interpretability was enhanced by replacing traditional hot–cold color palettes with ones that accommodate the vision-impaired and adhere to web accessibility standards. Full article

Flame spread over discrete fuels is a typical phenomenon in fire scenes. Experimental and theoretical research on flame spread over discrete thermally thin fuels separated by air gaps with different inclination angles was conducted in the present study. Experiments with six inclination angles ranging from 0° to 85° and various fuel coverage rates from 0.421 to 1 were designed. The flame spread behavior, the characteristic flame size, and the flame spread rate were analyzed. The results show that the flow pattern, stability, and flame size exhibit different characteristics with different inclination angles and gap sizes. As the inclination angle increases, particularly with smaller gaps, turbulent and oscillating flames are observed, while larger gap sizes promote flame stability. The mechanism of flame propagation across the gap depends on the interplay between the flame jump effect and heat transfer, which evolves with gap size. Average flame height, average flame width, and flame spread rate initially increase and then decline with the increase in fuel coverage, peaking at fuel coverage rates between 0.93 and 0.571 for different inclination angles. A theoretical model is proposed to predict the flame spread rate and the variation in the flame spread rate with inclination angle and fuel coverage. Furthermore, the map determined by inclination angle and fuel coverage is partitioned into distinct regions, comprising the accelerated flame spread region, the flame spread weakening region, and the failed flame spread region. These findings provide valuable insights into flame spread dynamics over discrete thermally thin fuels under diverse conditions. Full article