Fire Behavior Models Research Articles

Quantifying surface fuels for fire modelling in temperate forests using airborne lidar and Sentinel-2: potential and limitations

Surface fuel information is an essential input for models of fire behaviour and fire effects. However, spatially explicit, continuous information on surface fuel loads and fuelbed depth is scarce because the collection of field data is laborious, while suitable methods for deriving estimates from remote sensing data are still at an early stage of development. Fine-scale surface fuel mapping using both passive and active remote sensing has not yet been carried out in Central European forest types, and it remains unexplored how prediction uncertainties of different fuel components affect modelled fire behaviour. This study combines very detailed airborne lidar and multispectral satellite data to extract metrics describing forest structure and composition in two forested areas in southwestern Germany. These metrics were used to predict field-sampled surface fuel components using random forest regression. Accuracies of continuous fuel load predictions were compared to accuracies that could be achieved if only forest type-specific average fuels were assigned. Results revealed that models based on remotely sensed metrics explain part of the variance in litter and fine dead woody fuels (R2=0.27-0.41), but not in coarser dead woody fuels. Estimates for herb and shrub fuels were fairly accurate (R2=0.55-0.64) but limited for the more fire-relevant fine fraction of shrub fuels (R2=0.39). Fuelbed depth was moderately well predicted based on remote sensing data (R2=0.44). Lidar-derived metrics were particularly useful for predicting understory fuels and fuelbed depth. Litter and fine woody fuel predictions were linked to canopy characteristics captured with both lidar and multispectral data and similarly accurate estimates could be obtained using average values based on forest type. We used the fine-scale surface fuel maps derived from remote sensing to predict potential surface fire behaviour in the study area and analysed the sensitivity of modelled fire behaviour to errors in the predicted loads of different surface fuel components: fire behaviour was most sensitive to errors in litter and especially shrub fuel loads, hence estimates of these components need to be improved. Overall, this study showed that statistical relationships between remotely sensed metrics describing forest composition and structure and surface fuels have some potential for estimating fuel loads in Central European forest types and should be further developed to provide starting points for realistic fire behaviour models.

Modelling and Numerical Simulation of a Compartment Fire: Flow Rate Behaviour at Opening

The modelling of fire behaviour in a compartment is the subject of this work. A small-scale experimental study was carried out in a compartment sized 0.5 m × 0.5 m × 0.5 m with a single door with a width of 0.2 m and a height of 0.4 m. The collected results were converted to full-scale results using Froude modelling. The objective of this study was to see how ventilation affects fuel decomposition rate, gas flow rate at the opening and the power generated inside the compartment. This small-scale experimental study allowed us to determine four values of the fuel’s (diesel) mass loss rate, which correspond to four values of the ventilation factor. Numerical simulation using Fire Dynamics Simulator (FDS) software was used to show the gas’ behaviour at the opening and the variation of the power generated inside the compartment. The vertical profiles of the experimental and numerical temperatures were compared. It was found that the dimensions of the opening have a significant influence on the power generated inside the compartment.

Characterizing the Role of Moisture and Smoke on the 2021 Santa Coloma de Queralt Pyroconvective Event Using WRF‐Fire

AbstractSmoke from wildfires or burning biomass directly affects air quality and weather through modulating cloud microphysics and radiation. A simple wildfire emission coupling of black carbon (BC) and organic carbon (OC) with microphysics was implemented using the Weather Research and Forecasting model's fire module. A set of large‐eddy simulations inspired by unique surface and upper atmospheric observations from the 2021 Santa Coloma de Queralt Fire (Spain) were conducted to investigate the influence of background conditions and interactions between atmospheric and fire processes such as fire smoke, ambient moisture, and latent heat release on the formation and evolution of pyroconvective clouds. While the microphysical impact of BC and OC emissions on the dynamics of fire behavior is minimal on short time scales (<6 hr), their presence increased the cloud water content and decreased the rain rates in our case study. In our case study, atmospheric moisture played an important role in the formation and development of pyroconvective clouds, which in turn enhanced the surface winds (8%) and fire spread rate (25%). The influence of fuel moisture on the pyroconvective cloud formation is smaller when compared with the atmospheric moisture content. A better representation of cloud processes can improve the mesoscale forecasts, which is important for better fire behavior modeling.

Canopy and surface fuel estimations using RPAS and ground-based point clouds

Abstract Forest management activities intended to reduce wildfire risk rely on accurate characterizations of the amount and arrangement of canopy and surface fuels. Metrics that describe these fuels are typically estimated with various systems that transform plot-level field data into metrics that can be used within fire behaviour models. Remote sensing data have long been used to estimate these metrics across large spatial scales, but more advanced, high-density point clouds have the potential to estimate these metrics with higher accuracy. This study collected LiDAR and digital aerial photogrammetric (DAP) point clouds from a remotely piloted aerial system (RPAS), as well as mobile laser scanning (MLS) point clouds from a mobile ground-based system, and compared their ability to estimate fuel metrics. This involved the extraction of predictor variables from each point cloud, of which small subsets were used to estimate various fuel metrics. These included six overstory canopy metrics (stand height, canopy cover, tree density, canopy fuel load, canopy bulk density and canopy base height), three diameter at breast height (DBH)–related metrics (stand density index, basal area and quadratic mean diameter) and three surface fuel metrics (total woody debris (TWD), coarse woody debris (CWD) and fine woody debris (FWD)). Overall, canopy metrics were estimated most accurately by the RPAS LiDAR models, although none of the point clouds were able to accurately estimate DBH-related metrics. For the other six canopy metrics, RPAS LiDAR models had an average R2 value of 0.70; DAP – 0.63 and MLS – 0.63. CWD (>7 cm) and TWD loads were estimated most accurately by the MLS models (average R2 values – 0.70), followed by the RPAS LiDAR – 0.38 and DAP – 0.13. None of these models were able to accurately estimate FWD loads (≤7 cm in diameter), with the three types of point clouds having a maximum R2 value of 0.08. Overall, this research shows the relative ability of three types of high-density point clouds to estimate metrics relevant for fire behaviour modeling.

IX International Conference on Forest Fire Research and 17th International Wildland Fire Safety Summit: introduction to special issue (Part 2)

The ninth International Conference on Forest Fire Research, together with the 17th International Wildland Fire Safety Summit was held in November 2022. This joint conference brought together several hundred presentations in the field of wildfire research. This special issue of the International Journal of Wildland Fire includes several papers describing work presented at the conference. Due to the large number and variety of papers submitted, the special issue is being published in several parts. Part 1 of the special issue was published, in January 2023, with a set of eight papers. This Foreword to Part 2 of the special issue introduces 10 new papers on various topics that include: modelling of ignition probability, fire behaviour modelling, wildfire hazard mapping, fire management policies, imagery and mapping, and wildland–urban interface. All papers in the special issue are published Open Access.

Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system

Abstract. Accurate and spatially explicit information on forest fuels becomes essential to designing an integrated fire risk management strategy, as fuel characteristics are critical for fire danger estimation, fire propagation, and emissions modelling, among other aspects. This paper proposes a new European fuel classification system that can be used for different spatial scales and purposes (propagation, behaviour, and emissions). The proposed classification system is hierarchical and encompasses a total of 85 fuel types, grouped into six main fuel categories (forest, shrubland, grassland, cropland, wet and peat/semi-peat land, and urban), plus a nonfuel category. For the forest cover, fuel types include two vertical strata, overstorey and understorey, to account for both surface and canopy fuels. In addition, this paper presents the methods to map fuel types at the European scale, including the first-level of the classification system. Land cover, biogeographic datasets, and bioclimatic modelling were used. The final map, which is publicly available (https://doi.org/10.21950/YABYCN; Aragoneses et al., 2022a), included 20 fuel categories at 1 km spatial resolution. A first assessment of this map was performed using field information obtained from LUCAS (Land Use and Coverage Area frame Survey), complemented with high-resolution data. This validation exercise provided an overall accuracy of 88 % for the main fuel types and 81 % for all mapped fuel types. To facilitate the use of this fuel dataset in fire behaviour modelling, an assignment of fuel parameters to each fuel type was performed by developing a crosswalk to the standard fuel models defined by Scott and Burgan (2005; FBFMs – Fire Behavior Fuel Models), considering European climate diversity. This work has been developed within the framework of the FirEUrisk project, which aims to create a European-integrated strategy for fire danger assessment, reduction, and adaptation.

Future Direction of Fuels Management in Sagebrush Rangelands

Sagebrush ecosystems in the United States have been declining since EuroAmerican settlement, largely due to agricultural and urban development, invasive species, and altered fire regimes, resulting in loss of biodiversity and wildlife habitat. To combat continued conversion to undesirable ecological states and loss of habitat to invasive species fueled by frequent fire, a variety of fuel treatments, including networks of fuel breaks, are being implemented or proposed in sagebrush ecosystems, particularly in and around the Great Basin. In this forum paper we briefly review current knowledge of common fuel treatment approaches, their intended benefits, potential risks, and limitations. We additionally discuss challenges for fuel treatment strategies in the context of changes in climate, invasive species, wildlife habitat, and human population, and we explore how advances in geospatial technologies, monitoring, and fire behavior modeling, as well as accounting for social context, can improve the efficacy of fuels management in sagebrush ecosystems. Finally, given continued potential for ecosystem transformation, we describe approaches to future fuels management, by considering the applicability of the Resist-Accept-Direct framework. The intent of this paper is to provide scientists and land managers with key information and a forward-thinking framework for fuels science and adaptive management that can respond to both expected and unexpected changes in sagebrush rangelands.

Integrating plant physiology into simulation of fire behavior and effects.

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

Vegetation Fuel Mapping at Regional Scale Using Sentinel-1, Sentinel-2, and DEM Derivatives—The Case of the Region of East Macedonia and Thrace, Greece

The sustainability of Mediterranean ecosystems, even if previously shaped by fire, is threatened by the diverse changes observed in the wildfire regime, in addition to the threat to human security and infrastructure losses. During the two previous years, destructive, extreme wildfire events have taken place in southern Europe, raising once again the demand for effective fire management based on updated and reliable information. Fuel-type mapping is a critical input needed for fire behavior modeling and fire management. This work aims to employ and evaluate multi-source earth observation data for accurate fuel type mapping in a regional context in north-eastern Greece. Three random forest classification models were developed based on Sentinel-2 spectral indices, topographic variables, and Sentinel-1 backscattering information. The explicit contribution of each dataset for fuel type mapping was explored using variable importance measures. The synergistic use of passive and active Sentinel data, along with topographic variables, slightly increased the fuel type classification accuracy (OA = 92.76%) compared to the Sentinel-2 spectral (OA = 81.39%) and spectral-topographic (OA = 91.92%) models. The proposed data fusion approach is, therefore, an alternative that should be considered for fuel type classification in a regional context, especially over diverse and heterogeneous landscapes.

Topology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds

Terrestrial laser scanning (TLS) is a ground-based approach to rapidly acquire 3D point clouds via Light Detection and Ranging (LiDAR) technologies. Quantifying tree-scale structure from TLS point clouds requires segmentation, yet there is a lack of automated methods available to the forest ecology community. In this work, we consider the problem of segmenting a forest TLS point cloud into individual tree point clouds. Different approaches have been investigated to identify and segment individual trees in a forest point cloud. Typically these methods require intensive parameter tuning and time-consuming user interactions, which has inhibited the application of TLS to large area research. Our goal is to define a new automated segmentation method that lifts these limitations. Our Topology-based Tree Segmentation (TTS) algorithm uses a new topological technique rooted in discrete Morse theory to segment input point clouds into single trees. TTS algorithm identifies distinctive tree structures (i.e., tree bottoms and tops) without user interactions. Tree tops and bottoms are then used to reconstruct single trees using the notion of relevant topological features. This mathematically well-established notion helps distinguish between noise and relevant tree features. To demonstrate the generality of our approach, we present an evaluation using multiple datasets, including different forest types and point densities. We also compare our TTS approach with open-source tree segmentation methods. The experiments show that we achieve a higher segmentation accuracy when performing point-by-point validation. Without expensive user interactions, TTS algorithm is promising for greater usage of TLS point clouds in the forest ecology community, such as fire risk and behavior modeling, estimating tree-level biodiversity structural traits, and above-ground biomass monitoring.

Effect of flame zone depth on the correlation of flame length with fireline intensity

Background Previously established correlations of flame length L with fireline intensity IB are based on theory and data which showed that flame zone depth D of a line fire could be neglected if L was much greater than D. Aims We evaluated this correlation for wildland fires where D is typically a non-negligible proportion of L (i.e. roughly L/D < ~2). Methods Experiments were conducted to measure flame length L from line-source fires using a gas burner where IB and D were controlled independently (0.014 ≤ L/D ≤ 13.6). Key results The resulting correlation showed D significantly reduced L for a given IB over the entire range of observations and was in accord with independent data from spreading fires. Flame length is reduced because the horizontal extent of deep flame zones entrains more air for combustion than assumed by theory involving only the vertical flame profile. Conclusions Analysis suggested that the noted variability among published correlations of L with IB may be partly explained by varying L/D ratios typical of wildland fires. Implications Fire behaviour modelling that relies on correlations of L with IB for scaling of heat transfer processes would likely benefit by including the effects of D.

CFD modelling of WUI fire behaviour in historical fire cases according to different fuel management scenarios

Background In most wildland–urban interface (WUI) fires, damage to buildings results from poor surrounding vegetation management. No simulation had been conducted yet on historical WUI fires with Computational Fluid Dynamics modelling. Aims It was interesting to check the feasibility of this modelling in simulating past fire cases for different scenarios of vegetation management and fire propagation. Methods We studied three cases of WUI dwellings surrounded by gardens (subject to French regulations on fuel reduction) adjacent to forest affected by a past fire. The 3D fire propagation was assessed using the Fire Dynamic Simulator model (FDS) and taking into account accurate fire environment (fine vegetation distribution, terrain, etc.). Key results Results showed that, in the current model state, brush-clearing mitigated fire intensity and propagation and damage to ornamental vegetation. However, it sometimes highlighted that this measure could be strengthened when the effects of topography and wind were combined. Conclusions FDS modelling at the WUI scale using accurate vegetation distribution proved to be functionally satisfactory, exhibiting realistic fire behaviour. Implications Once validated, this modelling will ultimately help to assess when fuel reduction is efficient in fire mitigation and to pinpoint possible limitations.

Conifer encroachment increases foliar moisture content in a northwestern California oak woodland

Background Foliar moisture content influences crown fire ignition and behaviour. Some spatial variation in foliar moisture is attributable to differences in stand conditions but evidence describing this role is lacking or contradicting. Aims To examine the role of stand conditions on tree physiology and foliar moisture content in Oregon white oak (Quercus garryana) and Douglas-fir (Pseudotsuga menziesii). Methods We monitored foliar moisture content in both species, and tree physiology (stomatal conductance and leaf water potential) in oak, across three stand conditions, including intact stands (unencroached), stands invaded by Douglas-fir (encroached), and thinned stands with Douglas-fir removed. Key results Encroached stands had higher foliar moisture content than intact or thinned stands. Higher stand density was associated with higher foliar moisture content in both species and foliage ages. Encroached stands also had higher midday leaf water potential compared with intact or thinned stands. Conclusions These findings provide strong evidence that stand conditions and thinning treatments can influence foliar moisture content in Oregon white oak ecosystems, with likely implications for other ecosystems. Implications Better understanding of the role of stand conditions on foliar moisture content may contribute to improved spatial and temporal prediction of foliar moisture content and modelling of potential crown fire behaviour.

Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations

Background Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression. Aims We developed a new fire mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS and 2012–2020 VIIRS data) to develop a high-resolution wildfire growth dataset, including growth areas, perimeters, and cross-referenced fire information from agency reports. Methods Satellite fire detections were buffered using a historical pixel-to-fire size relationship, then grouped spatiotemporally into individual fire events. Sub-daily and daily growth areas and perimeters were calculated for each fire event. After assembly, fire event characteristics including location, size, and date, were merged with agency records to create a cross-referenced dataset. Key results Our satellite-based total fire size shows excellent agreement with agency records for MODIS (R2 = 0.95) and VIIRS (R2 = 0.97) in California. VIIRS-based estimates show improvement over MODIS for fires with areas less than 4047 ha (10 000 acres). To our knowledge, this is the finest resolution quality-assured fire growth dataset available. Conclusions and Implications The novel spatiotemporal resolution and methodological consistency of our dataset can enable advances in fire behaviour and fire weather research and model development efforts, smoke modelling, and near real-time fire monitoring.

Crown fire initiation of a thunderstorm

Understanding bushfire–atmosphere interactions is essential for accurate prediction of fire behaviour, and for the safe and effective strategic management of fires to mitigate risk to people and property. Bushfires with feedbacks to thunderstorms represent the most extreme form of fire–atmosphere interaction, with potential to initiate tornadoes, lightning and hazardous winds causing dangerous fire behaviour and new ignitions many kilometres from the fire front. However, there is very little evidence that links quantitative fire behaviour with observed thunderstorm dynamics. Here we combine stochastic modelling of fire behaviour with satellite and mobile weather radar data of a bushfire thunderstorm in Queensland, Australia. The results show the coupling between fire behaviour and thunderstorm development in a conditionally unstable atmosphere. The process by which the coupling occurs raises questions as to the cause and effect relationship of the bushfire-initiated thunderstorms and associated fire behaviour. Recommendations for future research are made, highlighting the need for understanding links between modelled and observed fire behaviour dynamics and atmospheric thermodynamics.

An artificial intelligence framework for predicting fire spread sustainability in semiarid shrublands

Background Fire behaviour simulation and prediction play a key role in supporting wildfire management and suppression activities. Aims Using machine-learning methods, the aim of this study was to predict the onset of fire propagation (go vs no-go) and type of fire behaviour (surface vs crown fire) in southern Australian semiarid shrublands. Methods Several machine-learning (ML) approaches were tested, including Support Vector Machine, Multinomial Naive Bayes and Multilayered Neural Networks, as was the use of augmented datasets developed with Generative Adversarial Networks (GAN) in classification of fire type. Key results Support Vector Machine was determined as the optimum machine learning classifier based on model overall accuracy against an independent evaluation dataset. This classifier correctly predicted fire spread sustainability and active crown fire propagation in 70 and 79% of the cases, respectively. The application of synthetically generated datasets in the Support Vector Machine model fitting process resulted in an improvement of model accuracy by 20% for the fire sustainability classification and 4% for the crown fire occurrence. Conclusions The selected ML modelling approach was shown to produce better results than logistic regression models when tested on independent datasets. Implications Artificial intelligence frameworks have a role in the development of predictive models of fire behaviour.

Simulated behaviour of wildland fire spreading through idealised heterogeneous fuels

Homogeneous vegetation is widely used in wildland fire behaviour models, although real vegetation is heterogeneous in nature and composed of different kinds of fuels and non-combustible parts. Many features of fires can arise from this heterogeneity. For land management and firefighting, creating heterogeneous fuel areas may be useful to reduce fire intensity and rate of spread (ROS), and alter fire geometry. Recently, an empirical model for fire spread in spinifex grasslands was developed and validated against experimental measurements. In this study, physics-based grassland fire behaviour simulations were conducted with varying percentages of fuel cover and alternating square and rectangular patches of burnable and non-burnable material. The environmental conditions and thermophysical properties of the grassland were kept constant throughout the simulation to separate the effects of fuel heterogeneities from other parameters. For three sets of nominal wind velocities, 3, 5.6 and 10 m s−1, we identified ‘go’ and ‘no go’ fires. Reasonable agreement between the non-dimensionalised simulated ROS and observed ROS in spinifex was found. There is a significant reduction of fire intensity, ROS, flame length, fire width and fire line length due to the heterogeneous effect of vegetation.

A method to produce a flexible and customized fuel models dataset

Simulation of vegetation fires very often resorts to fire-behavior models that need fuel models as input. The lack of fuel models is a common problem for researchers and fire managers because its quality depends on the quality/availability of data. In this study we present a method that combines expert- and research-based knowledge with several sources of data (e.g. satellite and fieldwork) to produce customized fuel models maps. Fuel model classes are assigned to land cover types to produce a basemap, which is then updated using empirical and user-defined rules. This method produces a map of surface fuel models as detailed as possible. It is reproducible, and its flexibility relies on juxtaposing independent spatial datasets, depending on their quality or availability. This method is developed in a ModelBuilder/ArcGis toolbox named FUMOD that integrates ten sub-models. FUMOD has been used to map the Portuguese annual fuel models grids since 2019, supporting regional fire risk assessments and suppression decisions. Datasets, models and supplementary files are available in a repository (https://github.com/anasa30/PT_FuelModels).•FUMOD is a flexible toolbox with ten sub-models included that maps updated Portuguese fuel models.

CFD modelling of WUI fire behaviour in historical fire cases according to different fuel management scenarios

CFD modelling of WUI fire behaviour in historical fire cases according to different fuel management scenarios

Future Direction of Fuels Management in Sagebrush Rangelands

Sagebrush ecosystems in the United States have been declining since EuroAmerican settlement, largely due to agricultural and urban development, invasive species, and altered fire regimes, resulting in loss of biodiversity and wildlife habitat. To combat continued conversion to undesirable ecological states and loss of habitat to invasive species fueled by frequent fire, a variety of fuel treatments, including networks of fuel breaks, are being implemented or proposed in sagebrush ecosystems, particularly in and around the Great Basin. In this forum paper we briefly review current knowledge of common fuel treatment approaches, their intended benefits, potential risks, and limitations. We additionally discuss challenges for fuel treatment strategies in the context of changes in climate, invasive species, wildlife habitat, and human population, and we explore how advances in geospatial technologies, monitoring, and fire behavior modeling, as well as accounting for social context, can improve the efficacy of fuels management in sagebrush ecosystems. Finally, given continued potential for ecosystem transformation, we describe approaches to future fuels management by considering the applicability of the Resist-Accept-Direct (RAD) framework. The intent of the paper is to provide scientists and land managers with key information and a forward-thinking framework for fuels science and adaptive management that can respond to both expected and unexpected changes in sagebrush rangelands.