Editor’s Choice Articles

Indigenous Peoples have been stewarding lands with fire for ecosystem improvement since time immemorial. These stewardship practices are part and parcel of the ways in which Indigenous Peoples have long recorded and protected knowledge through our cultural transmission practices, such as oral histories. In short, our Peoples have always been data gatherers, and as this article presents, we are also fire data gatherers and stewards. Given the growing interest in fire research with Indigenous communities, there is an opportunity for guidance on data collection conducted equitably and responsibly with Indigenous Peoples. This Special Issue of Fire presents fire research approaches and data harvesting practices with Indigenous communities as we “Reimagine the Future of Living and Working with Fire”. Specifically, the article provides future-thinking practices that can achieve equitable, sustainable, and just outcomes with and for stakeholders and rightholders (the preferred term Indigenous Peoples use in partnerships with academics, agencies, and NGOs). This research takes from the following key documents to propose an “Indigenous fire data sovereignty” (IFDS) framework: (1) Articles declared in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) as identified by the author and specified in Indigenous-led and allied Indigenous fire research in Australia, Canada, and the U.S.; (2) recommendations specific to cultural fire policy and calls for research in the 2023 Wildland Fire Mitigation and Management Commission report; (3) research and data barriers and opportunities produced in the 2024 Good Fire II report; and threads from (4) the Indigenous Fire Management conceptual model. This paper brings together recommendations on Indigenous data sovereignty, which are principles developed by Indigenous researchers for the protection, dissemination, and stewardship of data collected from Tribal/Nation/Aboriginal/First Nations Indigenous communities. The proposed IFDS framework also identifies potential challenges to Indigenous fire data sovereignty. By doing so, the framework serves as an apparatus to deploy fire research and data harvesting practices that are culturally informed, responsible, and ethically demonstrated. The article concludes with specific calls to action for academics and researchers, allies, fire managers, policymakers, and Indigenous Peoples to consider in exercising Indigenous fire data sovereignty and applying Indigenous data sovereignty principles to fire research. Full article

The study of forest fire prediction holds significant environmental and scientific importance, particularly in regions like South Carolina (SC) with a high incidence rate of forest fires. Despite the limited existing research on forest fires in this area, the application of machine learning and neural network techniques presents an opportunity to enhance forest fire prevention and control efforts. Utilizing data of forest fire from the SC Forestry Commission for the year 2023, prediction models were developed incorporating various factors such as meteorology, terrain, vegetation, and infrastructure—key drivers of forest fires in SC. Feature importance analysis was employed to construct the final fire prediction model using different machine learning and neural network approaches including Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Correlation coefficients analysis was employed to construct the final fire hazard map using a correlation test. The evaluation of predictive performance based on accuracy scores revealed that the DT model achieved the highest accuracy of 90.58%, surpassing other models. However, based on the kernel density map of the fire data from 2000 to 2023, the correlation test gave the better fire hazard map compared to any other machine learning or neural network approach that utilized feature importance. Nonetheless, all models achieved prediction accuracies exceeding 80%. This finding directed us to the approach based on the correlation coefficients rather than to those just based on feature importance. The overlap between fire locations and carbon hotspots provided the immediate need to mitigate the carbon loss due to fire in those locations. These results serve as a valuable resource for forest fire prediction in SC, demonstrating the efficacy of the correlation test, providing a theoretical foundation and data support for future forestry applications in the region, and showing the outperforming capability of this method compared to other approaches based on feature importance and the importance to prioritize areas to mitigate the climate change impact based upon fire prediction. Full article

With more frequent and intense fires expected under future climate conditions, it is important to understand the mechanisms that control flammability in Australian forests. We followed a systematic review approach to determine which physical traits make eucalypts leaves more or less flammable. Specifically, we reviewed 20 studies that covered 35 eucalypt species across five countries and found that leaf water content, leaf area (LA), and specific leaf area (SLA) are the main drivers of leaf flammability. These traits are easy and straightforward to measure, while more laborious traits (e.g., volatile organic compounds and structural carbohydrates) are seldom measured and reported. Leaf flammability also varies with species, and, while the biochemistry plays a role in how leaves burn, it plays a minor role in fire behaviour at landscape scales. This review highlights the range of different protocols used to measure flammability and leaf water content, warranting caution when comparing traits and results between studies. As a result, we propose a standardised protocol to measure leaf water content and advocate for long-term measurements of leaf traits and flammability. This study not only contributes to the understanding of how and why eucalypt leaves burn but also encourages research into the relative importance of traits in influencing flammability and provides a guide for selecting traits that can be monitored using satellite images to inform fire management policies and strategies. Full article

Wildland fire dispatchers play a key role in wildland fire management and response organization; however, to date, wildland fire studies have largely focused on the physical hazards and, to a lesser extent, mental health hazards of wildland firefighting operational personnel, and dispatcher studies have primarily focused on 911 and police dispatchers. Studies of other dispatchers have provided some limited insight into potential strains impacting this workforce, including work-related fatigue, burnout, and traumatic exposure. However, the specific job hazards that are faced by wildland fire dispatchers are poorly understood. In 2023, we conducted a cross-sectional survey of 510 wildland fire dispatchers with questions about their occupational health, general health, and well-being. We used validated screening instruments to measure the rates of anxiety, depression, PTSD, and suicidal thoughts and ideation. Here, we also present the results of mental health and trauma exposure questions that were asked as part of a larger survey. We found that demographic factors were significant indicators of anxiety, depression, and binge/restrictive eating. Our data indicate that rates of anxiety, depression, PTSD, and suicidal thoughts and ideation are significantly higher for both the wildland fire dispatching workforce and other emergency responder populations than those of the general United States population. Full article

Many plant species are well-adapted to historical fire regimes. An increase in the severity, frequency, and extent of wildfires could compromise the regenerative capacity of species, resulting in permanent shifts in plant diversity. We surveyed extant vegetation and soil seed banks across two forest types with contrasting historical fire regimes—Shrubby Dry Forest (fire return interval: 10–20 years) and Sub-Alpine Woodland (50–100 years). Over the past 20 years, both forests have been subject to repeated, high-severity wildfires at intervals significantly shorter than their historical return intervals. We examined the soil seed bank response to fire-cued germination, and whether the plant diversity in soil seed banks and extant vegetation demonstrated similar responses to short-interval, high-severity wildfires. The soil seed bank demonstrated a positive response to heat in combination with smoke, and for the Sub-Alpine Woodland, this was limited to sites more frequently burnt by fire. With an increase in fire frequency, there was a decline in species richness and Shannon’s Diversity and a shift in species composition in both extant vegetation and the soil seed bank. The fire frequency effects on the relative richness of trait associations were restricted to the Shrubby Dry Forest, and included an increase in short-lived obligate seeders, wind-dispersed species, and ant-dispersed shrubs in burnt relative to long unburnt sites in both extant vegetation and the soil seed bank. Graminoids were the most abundant component of the soil seed banks of Sub-Alpine Woodlands, and this increased with more frequent fire, with a similar trend (p = 0.06) in extant vegetation. Clear shifts in plant diversity in both soil seed banks and extant vegetation in forest types with contrasting historical fire regimes suggest that emerging fire regimes are pushing ecosystems beyond their historical range of variability, including potentially more flammable states and a decline in the buffering capacity of soil seed banks. Full article

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099. Full article

Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of geographical and environmental variables. However, unlike the ideal conditions simulated in the laboratory, data gaps often occur in real wildfire scenarios, posing challenges to the accuracy and robustness of predictions. It is necessary to explore the extent to which different missing items affect prediction accuracy, thereby providing rational suggestions for emergency decision-making. In this paper, we tested how different conditions of missing data affect the prediction accuracy of existing wildfire spread models and quantified the corresponding errors. The final experimental results suggest that it is necessary to judge the potential impact of data gaps based on the geographical conditions of the study area appropriately, as there is no significant pattern of behavior yet identified. This study aims to simulate the impact of data scarcity on the accuracy of wildfire spread prediction models in real scenarios, thereby enabling researchers to better understand the priority of different environmental variables for the model and identify the acceptable degree of missing data and the indispensable data attributes. It offers new insights for developing spread prediction models applicable to real-world scenarios and rational assessment of the effectiveness of model outcomes. Full article

Fire regimes have shaped extant vegetation communities, and subsequently fuel arrays, in fire-prone landscapes. Understanding how resilient fuel arrays are to fire regime attributes will be key for future fire management actions, given global fire regime shifts. We use a network of 63-field sites across the Sydney Basin Bioregion (Australia) to quantify how fire interval (short: last three fires <10 years apart, long: last two fires >10 years apart) and severity (low: understorey canopy scorched, high: understorey and overstorey canopy scorched), impacted fuel attribute values 2.5 years after Australia’s 2019–2020 Black Summer fires. Tree bark fuel hazard, herbaceous (near-surface fuels; grasses, sedges <50 cm height) fuel hazard, and ground litter (surface fuels) fuel cover and load were higher in areas burned by low- rather than high-severity fire. Conversely, midstorey (elevated fuels: shrubs, trees 50 cm–200 m in height) fuel cover and hazard were higher in areas burned by high- rather than low-severity fire. Elevated fuel cover, vertical connectivity, height and fuel hazard were also higher at long rather than short fire intervals. Our results provide strong evidence that fire regimes rearrange fuel arrays in the years following fire, which suggests that future fire regime shifts may alter fuel states, with important implications for fuel and fire management. Full article

Most current surface fire simulators rely upon Rothermel’s model, which considers the local properties of fuel, topography, and meteorology to estimate the rate of spread, and utilises the concept of elliptical growth to predict the evolution of the fire perimeter throughout time. However, the effects of convective processes near the fireline, which modify fire spread conditions along the fire perimeter, are not considered in this model. An innovative fire prediction simulator based on the concept of fireline element displacement, which is composed of translation, rotation, and extension, rather than a point-by-point displacement, is proposed in this article. Based on the laws of convective heat fluxes across and along the fireline and on laboratory experiments, models to estimate the angular rotation velocity and the extension of the fireline during its displacement are proposed. These models are applied to a set of laboratory experiments of point ignition fires on slopes of 30° and 40° and, given the fact that the rate of spread of the head, back, and flank fire are known, the evolution of the fire perimeter can be predicted. The fire spread model can be applied to other situations of varying boundary conditions provided that the parameters required by the model are known. Full article

This article focuses on using machine learning to predict the distance at which a chemical storage tank fire reaches a specified thermal radiation intensity. DNV’s Process Hazard Analysis Software Tool (PHAST) is used to simulate different scenarios of tank leakage and to establish a database of tank accidents. Backpropagation (BP) neural networks, random forest models, and the optimized random forest model K-R are used for model training and consequence prediction. The regression performance of the models is evaluated using the mean squared error (MSE) and $R^2$. The results indicate that the K-R regression prediction model outperforms the other two machine learning algorithms, accurately predicting the distance at which the thermal radiation intensity is reached after a tank fire. Compared with the simulation results, the model demonstrates higher accuracy in predicting the distance of tank fire consequences, proving the effectiveness of machine learning algorithms in predicting the range of consequences of tank storage area fire events. Full article

Wildfire causes environmental, economic, and human problems or losses. This study reviewed wildfires induced by lightning strikes. This review focuses on the investigations of lightning mechanisms in the laboratory. Also, the paper aims to discuss some of the modeling studies on lightning-induced wildfires at different geographical locations using satellite-recorded lightning data and different statistical analyses. This review established that irrespective of the different models used to predict lightning wildfires, there is still a lack of understanding of the lightning-strike ignition mechanism; few experiments have been modeled to establish the dynamics of lightning-strike ignition. Therefore, further research needs to be carried out in this area to understand lightning ignition. It was ascertained from the various statistical modeling that lightning-induced wildfires are exacerbated by the abundant availability of fuel with a lower moisture content and high lightning efficiency. Moreover, because of changes in the climate and weather conditions, i.e., harsh weather and climate conditions due to anthropogenic activities, lightning-induced ignition wildfires have increased over the years, and they are expected to increase in the future if the climate and weather conditions continue to aggravate. Although various modeling studies have identified that lightning-induced wildfires have increased recently, no preventive measures have been conclusively proposed to reduce lightning-caused wildfires. Hence, this aspect of research has to be given critical attention. This review presents information that gives a profound understanding of lightning-induced wildfires, especially factors that influence lightning wildfires, and the state-of-the-art research that has been completed to understand lightning-induced wildfires. Full article

In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features and its precision in detection, this study initially introduces ConvNeXtV2 and Conv2Former to the You Only Look Once version 7 (YOLOv7) algorithm, separately, and then compares the results with the original YOLOv7 algorithm through experiments. After comprehensive comparison, the proposed ConvNeXtV2-YOLOv7 based on ConvNeXtV2 exhibits a superior performance in detecting forest fires. Additionally, in order to further focus the network on the crucial information in the task of detecting forest fires and minimize irrelevant background interference, the efficient layer aggregation network (ELAN) structure in the backbone network is enhanced by adding four attention mechanisms: the normalization-based attention module (NAM), simple attention mechanism (SimAM), global attention mechanism (GAM), and convolutional block attention module (CBAM). The experimental results, which demonstrate the suitability of ELAN combined with the CBAM module for forest fire detection, lead to the proposal of a new method for forest fire detection called CNTCB-YOLOv7. The CNTCB-YOLOv7 algorithm outperforms the YOLOv7 algorithm, with an increase in accuracy of 2.39%, recall rate of 0.73%, and average precision (AP) of 1.14%. Full article

In forested ecosystems all over the world, usually, fire is the main disturbance, and due to global climate change, its effects are worsening in many areas. Although fire impacts have been studied for many years, integrative analyses of their effects on various ecosystem services (ES) at different scales are uncommon. This study tries to assess the ecological role of fire in a changing environment, focusing on a Mediterranean country. Data were collected by the use of an online questionnaire in Greece, where the summer fires in the last decades have had significant impacts on the environment and the economy and, in many cases, there were many human and animal victims from them. The sample size of the survey was 384 workers in the primary production sector from all over the country. The study showed that fire has several effects on animal husbandry, the quality of soil nutrients and fertility, the overall vegetation cover, and on general biodiversity. It seems that the degree to which fire has an effect on ecosystem components depends on the intensity, frequency, and length of the fires. Additionally, the frequency, intensity, and length of fire affect the impacts of fire on herbaceous plant, woody vegetation, soil physical qualities, and on the different animals’ habitats. Full article

In 2023, Greece faced its worst wildfire season, with nine major fires causing unprecedented environmental damage of 1470.31 km². This article uses Copernicus Land Monitoring Service and Sentinel-2 data, employing advanced remote sensing and GIS techniques to analyze spatial dynamics, map burn severity, assess fire extent, and highlight pre-fire tree density and land cover. The study focuses on the catastrophic fire in the Evros region and the damage to the National Forest Park of Dadia–Lefkimmi–Soufli. It also analyzes significant fires in Rhodes, Attica, Thessaly, Evia, Corfu, and Magnesia, emphasizing the compounded challenges posed by terrain, climate, and human factors in those areas. Additionally, the climate data for each affected area were compared with the weather conditions prevailing at the time of the fires. Copernicus Land Cover and Tree Density data are integrated to aid future management, assessment, and restoration. The analysis of maps and fire statistics underscores a notable pattern: areas with higher pre-fire tree density experienced correspondingly higher burn severity. This research underscores the crucial role of such data in assessing wildfire impact. In addition, compared with Copernicus Emergency Management Service, the burned area maps validate the accuracy and reliability of the utilized satellite data. The total burned area was assessed with a high accuracy rate of 96.28%. Full article

One of the most common forest disturbances, fire, has a significant influence on the people, societies, economies, and environment of countries all over the world. This study explores the different environmental and socioeconomic effects of forest fires to establish priorities for countries in battling and mitigating the harmful effects of forest fires based on data collected from 382 professionals working in Greece’s forestry and agriculture sectors. Secondary data, especially from Statista, were further utilized to enhance the analytical comparisons and conclusions of this study. Wildfires in Greece destroy agricultural land and greatly impact the rural economy and community. This study showed that forest fires have led to several economic costs, mainly affecting the incomes of different investors in the forest sector in Greece. It was revealed that the overall cost of a fire is determined by the direct and indirect expenditures as well as the price of fire control and preventative methods. Direct expenses are broken down into two categories: direct damage that occurs immediately and direct losses that are caused immediately after a fire. Governments should take the initiative to create and expand bilateral and/or multilateral cooperation and coordination, as well as exchange necessary financial resources, technology, and training, to reduce the effects of forest fires in a fragile international man-made and natural environment. Full article

The horizontal storm structure surrounding 92,512 lightning-ignited wildfires is examined in the mid to eastern sections of the United States from 2003 to 2015 using Vaisala’s National Lightning Detection Network (NLDN), NCEP’s Stage IV gauge-corrected radar precipitation mosaic, and the US Forest Service’s Fire Occurrence Database. Though lightning flash density peaks strongly around fire ignitions on the instantaneous 1 km scale, on the hourly 10 km scale, both the lightning and precipitation peaks are typically offset from fire ignitions. Lightning density is higher, and precipitation is lower around ignition points compared to non-ignition points. The average spatial distribution of total lightning flashes around fire ignitions is symmetrical, while that of precipitation and positive flashes is not. Though regression is consistent with the claim that positive flashes have a stronger association with ignition than negative flashes, the statistical significance is ambiguous and is contradicted by an unchanging positive flash fraction in the vicinity of wildfires. Full article

This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk. Full article

Ethnographic observations suggest that Indigenous peoples employed a distinct regime of frequent, low-intensity fires in the Australian landscape in the past. However, the timing of this behaviour and its ecological impact remain uncertain. Here, we present detailed analysis of charcoal, including a novel measure of fire severity using Fourier transform infrared (FTIR) spectroscopy, at a site in eastern Australia that spans the last two glacial/interglacial transitions between 135–104 ka and 18–0.5 ka BP (broadly equivalent to Marine Isotope Stage (MIS) 6-5 and 2-1, respectively). The accumulation of charcoal and vegetation composition was similar across both periods, correlating closely with Antarctic ice core records, and suggesting that climate is the main driver of fire regimes. Fire severity was lower over the past 18,000 years compared to the penultimate glacial/interglacial period and suggests increasing anthropogenic influence over the landscape during this time. Together with local archaeological records, our data therefore imply that Indigenous peoples have been undertaking cultural burning since the beginning of the Holocene, and potentially the end of the Last Glacial Maximum. We highlight the fact that this signal is not easily discernible in the other proxies examined, including widely used charcoal techniques, and propose that any anthropogenic signal will be subtle in the palaeo-environmental record. While early Indigenous people’s reasons for landscape burning were different from those today, our findings nonetheless suggest that the current land management directions are based on a substantive history and could result in a reduction in extreme fire events. Full article

Protecting “wilderness” and removing human involvement in “nature” was a core pillar of the modern conservation movement through the 20th century. Conservation approaches and legislation informed by this narrative fail to recognise that Aboriginal people have long valued, used, and shaped most landscapes on Earth. Aboriginal people curated open and fire-safe Country for millennia with fire in what are now forested and fire-prone regions. Settler land holders recognised the importance of this and mimicked these practices. The Land Conservation Act of 1970 in Victoria, Australia, prohibited burning by settler land holders in an effort to protect natural landscapes. We present a 120-year record of vegetation and fire regime change from Gunaikurnai Country, southeast Australia. Our data demonstrate that catastrophic bushfires first impacted the local area immediately following the prohibition of settler burning in 1970, which allowed a rapid increase in flammable eucalypts that resulted in the onset of catastrophic bushfires. Our data corroborate local narratives on the root causes of the current bushfire crisis. Perpetuation of the wilderness myth in conservation may worsen this crisis, and it is time to listen to and learn from Indigenous and local people, and to empower these communities to drive research and management agendas. Full article

Fire robots are an effective way to save lives from fire, but their limited detection accuracy has greatly hampered their practical applications in complicated fire conditions. This study therefore proposes an advanced thermal imaging flame detection model of YOLOv4-F based on YOLOv4-tiny. We replaced the Leaky ReLU activation function with the Mish activation function in the YOLOV4-tiny feature extraction network. A Spatial Pyramid Pooling (SPP) was also added to increase the receiving range of the feature extraction network. To improve the feature fusion efficiency between multi-scale feature layers, a Path Aggregation Network (PANet) was adopted to replace the YOLOv4-tiny Feature Pyramid Network (FPN) with full use of feature information; a high-quality dataset containing 14,757 thermal imaging flame images was built according to the PASCAL VOC 2007 dataset standard. The results show that, when compared to the YOLOv4-tiny, YOLOv5-s, and YOLOv7-tiny models, the average detection accuracy of the proposed YOLOv4-F model is 5.75% higher, the average mAP of the five IOU cases rises by 7.02%, and the average detection confidence of three scaled flames shows a 18.09% gain. The proposed YOLOV4-F meets the requirements of fire robots on real-time responses and accurate flame detection, offering an important tool to improve the performance of the current fire robots. Full article

Fire location and burning area are essential parameters for estimating fire emissions. However, ground-based fire data (such as fire perimeters from incident reports) are often not available with the timeliness required for real-time forecasting. Fire detection products derived from satellite instruments such as the GOES-16 Advanced Baseline Imager or MODIS, on the other hand, are available in near real-time. Using a ground fire dataset of 2699 fires during 2017–2019, we fit a series of linear models that use multiple satellite fire detection products (HMS aggregate fire product, GOES-16, MODIS, and VIIRS) to assess the ability of satellite data to detect and estimate total burned area. It was found that on average models fit with fire detections from GOES-16 products performed better than those developed from other satellites in the study (modelled R² = 0.84 and predictive R² = 0.88). However, no single satellite product was found to best estimate incident burned area, highlighting the need for an ensemble approach. With our proposed modelling ensemble, we demonstrate its ability to estimate burned area and suggest its further use in daily fire tracking and emissions-modeling frameworks. Full article

Local knowledge and communities’ active role in disaster risk areas are recognized in the literature as key conditions to better understand risks, enhance adaptive capacities and foster local resilience. A participatory action research project in forest fire-prone areas in Monchique, Portugal, is aligned with the literature and adopts participatory mapping as a method that can bring evidence to the importance of local knowledge and communities’ agency. In the BRIDGE Project, different types of knowledge are integrated, triggering local/collective agency and fostering a forest fire community-based disaster risk reduction (CBDRR) approach. An innovation laboratory (InnoLab) provides the space for dialogue and knowledge sharing for different actors that manage forest territories. In the InnoLab, participatory mapping is used as a method to engage landowners where risk factors and local vulnerabilities were identified. Their active engagement enabled a collective perception in the assessment of vulnerability and led to the identification of strategic measures for risk reduction. This paper shares the process and outcomes of this participatory mapping, highlighting the benefits of a community approach and the importance of local knowledge and practices as recognized in the literature. It also reveals how the active role of local stakeholders can help drive a CBDRR process. Full article

As human impacts from wildfires mount, there is a pressing need to understand why structures are lost in destructive fires. Despite growing research on factors contributing to structure loss, fewer studies have focused on why some fires are destructive and others are not. We characterized overall differences between fires that resulted in structure loss (“destructive fires”) and those that did not (“non-destructive wildfires”) across three California regions. Then, we performed statistical analyses on large fires only (≥100 ha) to distinguish the primary differences between large destructive large fires and large non-destructive fires. Overall, destructive fires were at least an order of magnitude larger than non-destructive fires, with the largest area burned varying by season in different regions. Fire severity was also significantly higher in destructive than non-destructive fires. The statistical analysis showed that, in the San Francisco Bay Area and the northern Sierra Nevada foothills, proximity to the Wildland Urban Interface (WUI) was by far the most important factor differentiating destructive and non-destructive wildfires, followed by different combinations of short-term weather, seasonal climate, topography, and vegetation productivity. In Southern California, wind velocity on the day of the fire ignition was the top factor, which is consistent with previous assumptions that wind-driven fires tend to be most destructive and most of the destruction occurs within the first 24 h. Additionally, Southern California’s high population density increases the odds that a human-caused wildfire may occur during a severe fire-weather event. The geographical differences among regions and the variation of factors explaining the differences between large destructive and large non-destructive fires reflects the complexity inherent in decision-making for reducing wildfire risk. Land use planning to reduce future exposure of housing development to fire and increased focus on wildfire ignition prevention emerge as two approaches with substantial potential. Full article

In recent decades, wildfires in many areas of the United States (U.S.) have become larger and more frequent with increasing anthropogenic pressure, including interactions between climate, land-use change, and human ignitions. We aimed to characterize the spatiotemporal patterns of contemporary fire characteristics across the contiguous United States (CONUS). We derived fire variables based on frequency, fire radiative power (FRP), event size, burned area, and season length from satellite-derived fire products and a government records database on a 50 km grid (1984–2020). We used k-means clustering to create a hierarchical classification scheme of areas with relatively homogeneous fire characteristics, or modern ‘pyromes,’ and report on the model with eight major pyromes. Human ignition pressure provides a key explanation for the East-West patterns of fire characteristics. Human-dominated pyromes (85% mean anthropogenic ignitions), with moderate fire size, area burned, and intensity, covered 59% of CONUS, primarily in the East and East Central. Physically dominated pyromes (47% mean anthropogenic ignitions) characterized by relatively large (average 439 mean annual ha per 50 km pixel) and intense (average 75 mean annual megawatts/pixel) fires occurred in 14% of CONUS, primarily in the West and West Central. The percent of anthropogenic ignitions increased over time in all pyromes (0.5–1.7% annually). Higher fire frequency was related to smaller events and lower FRP, and these relationships were moderated by vegetation, climate, and ignition type. Notably, a spatial mismatch between our derived modern pyromes and both ecoregions and historical fire regimes suggests other major drivers for modern U.S. fire patterns than vegetation-based classification systems. This effort to delineate modern U.S. pyromes based on fire observations provides a national-scale framework of contemporary fire regions and may help elucidate patterns of change in an uncertain future. Full article

Tropical peatlands store vast volumes of carbon belowground. Human land uses have led to their degradation, reducing their carbon storage services. Clearing and drainage make peatlands susceptible to surface and belowground fires. Satellites do not readily detect smouldering peat fires, which release globally significant quantities of aerosols and climate-influencing gases. Despite national and international desire to improve management of these fires, few published results exist for in situ tropical peat fire behaviour and associated carbon emissions. We present new field methodology for calculating rates of fire spread within degraded peat (average spread rates, vertical 0.8 cm h⁻¹, horizontal 2.7 cm h⁻¹) and associated peat volume losses (102 m³ ha⁻¹ in August, 754 m³ ha⁻¹ in September) measured at six peat fire sites in Kalimantan, Indonesia, in 2015. Utilizing locally collected bulk density and emission factors, total August and September gas emissions of 27.2 t ha⁻¹ (8.1 tC ha⁻¹) and 200.7 t ha⁻¹ (60.2 tC ha⁻¹) were estimated. We provide much needed, but currently lacking, IPCC Tier 3-level data to improve GHG estimates from tropical peat fires. We demonstrate how calculations of total emission estimates can vary greatly in magnitude (+798% to −26%) depending on environmental conditions, season, peat burn depth methodology, bulk density and emission factors data sources, and assumed versus observed combustion factors. This illustrates the importance of in situ measurements and the need for more refined methods to improve accuracies of GHG estimates from tropical peat fires. Full article

This paper provides understanding of the fire performance of exposed cross-laminated-timber (CLT) in large enclosures. An office-type configuration has been represented by a 3.75 by 7.6 by 2.4 m high enclosure constructed of non-combustible blockwork walls, with a large opening on one long face. Three experiments are described in which propane-fuelled burners created a line fire that impinged on different ceiling types. The first experiment had a non-combustible ceiling lining in which the burners were set to provide flames that extended approximately halfway along the underside of the ceiling. Two further experiments used exposed 160 mm thick (40-20-40-20-40 mm) loaded CLT panels with a standard polyurethane adhesive between lamella in one experiment and a modified polyurethane adhesive in the other. Measurements included radiative heat flux to the ceiling and the floor, temperatures within the depth of the CLT and the mass loss of the panels. Results show the initial peak rate of heat release with the exposed CLT was up to three times greater when compared with the non-combustible lining. As char formed, this stabilised at approximately one and a half times that of the non-combustible lining. Premature char fall-off (due to bond-line failure) was observed close to the burners in the CLT using standard polyurethane adhesive. However, both exposed CLT ceiling experiments underwent auto-extinction of flaming combustion once the burners were switched off. Full article

A severe outbreak of wildfire across the US Pacific Coast during August 2020 led to persistent fire activity through the end of summer. In late September, Fire Weather Outlooks predicted higher than usual fire activity into the winter in parts of California, with concomitant elevated fire danger in the Southeastern US. To help inform the regional and national allocation of firefighting personnel and equipment, we developed visualizations of resource use during recent late season, high-demand analogs. Our visualizations provided an overview of the crew, engine, dozer, aerial resource, and incident management team usage by geographic area. While these visualizations afforded information that managers needed to support their decisions regarding resource allocation, they also revealed a potentially significant gap between resource demand and late-season availability that is only likely to increase over time due to lengthening fire seasons. This gap highlights the need for the increased assessment of suppression resource acquisition and allocation systems that, to date, have been poorly studied. Full article

This Perspective highlights the lingering consequences of nuclear disasters by examining the risks posed by wildfires that rerelease radioactive fallout originally deposited into the environment by accidents at nuclear power plants or testing of nuclear weapons. Such wildfires produce uncontainable, airborne, and hazardous smoke, which potentially carries radioactive material, thus becoming the specter of the original disaster. As wildfires occur more frequently with climate change and land use changes, nuclear wildfires present a pressing yet little discussed problem among wildfire management and fire scholars. The problem requires urgent attention due to the risks it poses to the health and wellbeing of wildland firefighters, land stewards, and smoke-impacted communities. This Perspective explains the problem, outlines future research directions, suggests potential solutions, and underlines the broader benefits of mitigating the risks. Full article

The catastrophic 2019/2020 Black Summer bushfires were the worst fire season in the recorded history of Southeast Australia. These bushfires were one of several recent global conflagrations across landscapes that are homelands of Indigenous peoples, homelands that were invaded and colonised by European nations over recent centuries. The subsequent suppression and cessation of Indigenous landscape management has had profound social and environmental impacts. The Black Summer bushfires have brought Indigenous cultural burning practices to the forefront as a potential management tool for mitigating climate-driven catastrophic bushfires in Australia. Here, we highlight new research that clearly demonstrates that Indigenous fire management in Southeast Australia produced radically different landscapes and fire regimes than what is presently considered “natural”. We highlight some barriers to the return of Indigenous fire management to Southeast Australian landscapes. We argue that to adequately address the potential for Indigenous fire management to inform policy and practice in managing Southeast Australian forest landscapes, scientific approaches must be decolonized and shift from post-hoc engagement with Indigenous people and perspectives to one of collaboration between Indigenous communities and scientists. Full article

Prescribed burning by Indigenous people was once ubiquitous throughout California. Settler colonialism brought immense investments in fire suppression by the United States Forest Service and the California Department of Forestry and Fire Prevention (CAL FIRE) to protect timber and structures, effectively limiting prescribed burning in California. Despite this, fire-dependent American Indian communities such as the Karuk and Yurok peoples, stalwartly advocate for expanding prescribed burning as a part of their efforts to revitalize their culture and sovereignty. To examine the political ecology of prescribed burning in Northern California, we coupled participant observation of prescribed burning in Karuk and Yurok territories (2015–2019) with 75 surveys and 18 interviews with Indigenous and non-Indigenous fire managers to identify political structures and material conditions that facilitate and constrain prescribed fire expansion. Managers report that interagency partnerships have provided supplemental funding and personnel to enable burning, and that decentralized prescribed burn associations facilitate prescribed fire. However, land dispossession and centralized state regulations undermine Indigenous and local fire governance. Excessive investment in suppression and the underfunding of prescribed fire produces a scarcity of personnel to implement and plan burns. Where Tribes and local communities have established burning infrastructure, authorities should consider the devolution of decision-making and land repatriation to accelerate prescribed fire expansion. Full article

Tens of thousands of structures and hundreds of human lives have been lost in recent fire events throughout California. Given the potential for these types of wildfires to continue, the need to understand why and how structures are being destroyed has taken on a new level of urgency. We compiled and analyzed an extensive dataset of building inspectors’ reports documenting homeowner mitigation practices for more than 40,000 wildfire-exposed structures from 2013–2018. Comparing homes that survived fires to homes that were destroyed, we investigated the role of defensible space distance, defensive actions, and building structural characteristics, statewide and parsed into three broad regions. Overall, structural characteristics explained more of a difference between survived and destroyed structures than defensible space distance. The most consistently important structural characteristics—having enclosed eaves, vent screens, and multi-pane windows—were those that potentially prevented wind-born ember penetration into structures, although multi-pane windows are also known to protect against radiant heat. In the North-Interior part of the state, active firefighting was the most important reason for structure survival. Overall, the deviance explained for any given variable was relatively low, suggesting that other factors need to be accounted for to understand the full spectrum of structure loss contributors. Furthermore, while destroyed homes were preferentially included in the study, many “fire-safe” structures, having > 30 m defensible space or fire-resistant building materials, were destroyed. Thus, while mitigation may play an important role in structure survival, additional strategies should be considered to reduce future structure loss. Full article

Despite major advances in numerical weather prediction, few resources exist to forecast wildland fire danger conditions to support operational fire management decisions and community early-warning systems. Here we present the development and evaluation of a spatial fire danger index that can be used to assess historical events, forecast extreme fire danger, and communicate those conditions to both firefighters and the public. It uses two United States National Fire Danger Rating System indices that are related to fire intensity and spread potential. These indices are normalized, combined, and categorized based on a 39-yr climatology (1979–2017) to produce a single, categorical metric called the Severe Fire Danger Index (SFDI) that has five classes; Low, Moderate, High, Very High, and Severe. We evaluate the SFDI against the number of newly reported wildfires and total area burned from agency fire reports (1992–2017) as well as daily remotely sensed numbers of active fire pixels and total daily fire radiative power for large fires (2003–2016) from the Moderate-Resolution Imaging Spectroradiometer (MODIS) across the conterminous United States. We show that the SFDI adequately captures geographic and seasonal variations of fire activity and intensity, where 58% of the eventual area burned reported by agency fire records, 75.2% of all MODIS active large fire pixels, and 81.2% of all fire radiative power occurred when the SFDI was either Very High or Severe (above the 90th percentile). We further show that SFDI is a strong predictor of firefighter fatalities, where 97 of 129 (75.2%) burnover deaths from 1979 to 2017 occurred when SFDI was either Very High or Severe. Finally, we present an operational system that uses short-term, numerical weather predictions to produce daily SFDI forecasts and show that 76.2% of all satellite active fire detections during the first 48 h following the ignition of nine high-profile case study fires in 2017 and 2018 occurred under Very High or Severe SFDI conditions. The case studies indicate that the extreme weather events that caused tremendous damage and loss of life could be mapped ahead of time, which would allow both wildland fire managers and vulnerable communities additional time to prepare for potentially dangerous conditions. Ultimately, this simple metric can provide critical decision support information to wildland firefighters and fire-prone communities and could form the basis of an early-warning system that can improve situational awareness and potentially save lives. Full article

Prescribed fire is one of the most widely advocated management practices for reducing wildfire hazard and has a long and rich tradition rooted in indigenous and local ecological knowledge. The scientific literature has repeatedly reported that prescribed fire is often the most effective means of achieving such goals by reducing fuels and wildfire hazard and restoring ecological function to fire-adapted ecosystems in the United States (US) following a century of fire exclusion. This has translated into calls from scientists and policy experts for more prescribed fire, particularly in the Western US, where fire activity has escalated in recent decades. The annual extent of prescribed burning in the Western US remained stable or decreased from 1998 to 2018, while 70% of all prescribed fire was completed primarily by non-federal entities in the Southeastern US. The Bureau of Indian Affairs (BIA) was the only federal agency to substantially increase prescribed fire use, potentially associated with increased tribal self-governance. This suggests that the best available science is not being adopted into management practices, thereby further compounding the fire deficit in the Western US and the potential for more wildfire disasters. Full article