Small Fires Research Articles

Global expansion of wildland-urban interface intensifies human exposure to wildfire risk in the 21st century.

Rapidly increasing human-nature interactions exacerbate the risk of exposure to wildfires for human society. The wildland-urban interface (WUI) represents the nexus of human-nature interactions, where the risk of exposure to natural hazards such as wildfire is most pronounced. However, quantifying long-term global WUI change and the corresponding driving factors at fine resolution remain challenging. Here, we mapped and analyzed the global WUI at 30-meter resolution in 2000, 2010, and 2020. Our analysis revealed that the global WUI expanded by 35.6% since 2000, reaching 1.93 million square kilometer in 2020. Notably, 85% of this growth occurred between 2010 and 2020. The increase in WUI was primarily driven by the unprecedented expansion of global urbanization, contributing an additional 589,914 square kilometer of WUI. In addition, the number of small fires occurring in WUI areas has increased substantially since 2010. These findings underscore the rising wildfire risk to human society and highlight the urgency of implementing tailored fire management strategies in WUI areas.

Numerical Simulation of an Isolated N-Heptane Pool Fire

Refineries are industrial complexes of great economic importance which are located close to major cities. A pool fire accident that can occur from an oil leak combined with wind can result in disastrous consequences for such an industry. This study investigates the characteristics of an isolated n-heptane square pool fire of 36 m2 under the influence of a cross wind. The pool fire characteristics are numerically studied using open-source Computational Fluid Dynamics (CFD) software, such as FireFoam (v4.1) and Fire Dynamic Simulator (FDS) (version 6.9.0). The turbulent flow field and the fire characteristics were simulated with the LES Method. The crucial parameters of the pool fire, such as (a) the temperature and velocity fields, (b) the flame length and height, (c) the surface emissive power, and (d) the flame tilt angles, were computed. Comparisons against experimental data for both small and large-area pool fires from the literature were made successfully. The flame tilt angle is shown to correlate very well with the reciprocal of the Richardson number, which was approximated within a multiplication constant to the Froude number. Thus, both the reciprocal Richardson number and Froude number can be used for correlating the flame tilt angle. It is shown that both of these numbers are used to correlate the tilt angle of experimental pool fires with effective diameters from a fraction of a meter to approximately 16 m, and wind speeds up to 7 m/s. The goodness of a linear fit based on the sum of the residual squares is 0.91.

Wildfire CNN: An Enhanced Wildfire Detection Model Leveraging CNN and VIIRS in Indian Context

Introduction Wildfires are an unexpected global hazard that significantly impact environmental change. An accurate and affordable method of identifying and monitoring on wildfire areas is to use coarse spatial resolution sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Compared to MODIS, wildfire observations from VIIRS sensor data are around three times as extensive. Objective The traditional contextual wildfire detection method using VIIRS data mainly depends on the threshold value for classifying the fire or no fire which provides less performance for detecting wildfire areas and also fails in detecting small fires. In this paper, a wildfire detection method using Wildfiredetect Convolution Neural Network model is proposed for an effective wildfire detection and monitoring system using VIIRS data. Methods The proposed method uses the Convolutional Neural Network model and the study area dataset containing fire and non-fire spots is tested. The performance metrics such as recall rate, precision rate, omission error, commission error, F-measure and accuracy rate are considered for the model evaluation. Results The experimental analysis of the study area shows a 99.69% recall rate, 99.79% precision rate, 0.3% omission error, 0.2% commission error, 99.73% F-measure and 99.7% accuracy values for training data. The proposed method also proves to detect small fires in Alaska forest dataset for the testing data with 100% recall rate, 99.2% precision rate, 0% omission error, 0.7% commission error, 99.69% F-measure and 99.3% accuracy values. The proposed model achieves a 26.17% higher accuracy rate than the improved contextual algorithm. Conclusion The experimental findings demonstrate that the proposed model identifies small fires and works well with VIIRS data for wildfire detection and monitoring systems.

On gravity-driven liquid nitrogen jets reach and horizontal spread for extinction of ground fires by aerial means

This paper describes experimental and numerical results on the reach and the spread of gravity-driven jets of liquid nitrogen (LN2) on the ground for applications to fire extinction by aerial means. A series of experiments released LN2 jets from different elevations in ambient air to measure their reach and spread distances upon the impingement. A numerical model was developed to simulate the behavior of such jets. Upon validation, the numerical model was used to further predict the LN2 pool mass and spreading distances under various release configurations. Results showed that the LN2 survivability is greatly affected by the release height of the cryogen, since the LN2 quantity reaching the ground decreases as the release height increases. Moreover, releasing larger initial LN2 quantities and, most importantly using larger nozzle diameters, both the LN2 pool mass and spreading diameter can be extended. Additional experiments were conducted where cryogen jets were released onto small (∼300 cm2) alcohol pool fires; results showed that only limited quantities of the LN2 evaporated in transit to the fire, and small amounts of the cryogen expediently snuffed the fires. A simplified model also suggested that in fire supression/extinction by LN2 the fuel cooling mechanism is of secondary importance compared to the mechanism of separating the fuel from oxygen.

Secrets Buried in the Pits: Ritual Activities in Western Anatolia in the First Half of the Second Millennium bce

Western Anatolian ritual pits provide valuable insights into socio-cultural, economic and symbolic practices during the Early to Middle Bronze Age. Findings in feasting pits, such as carbonized seeds and animal bones, indicate a strong link between ritual and food. Standing stones, altars and carefully arranged artefacts suggest a symbolic and sacred dimension beyond mere ceremonies. The pits from this period contain carbonized seeds and fragments of wood, indicating the presence of small fires during certain rituals. Changing features in ritual pits from the Early to Middle Bronze Age reveal a dynamic relationship between spatial arrangements and religious practices. The study shows that in the first half of the second millennium bce several ritual activities known from different regions reached western Anatolia for the first time. Interregional trade involved not only goods, but also the dissemination of rituals over a wide geographical area. This cultural interaction reveals western Anatolia as a dynamic and influential centre in this historical period. By exploring the ritual practices of second-millennium bce western Anatolia, this paper presents new perspectives on the rituals of the region.

LD-YOLO: A Lightweight Dynamic Forest Fire and Smoke Detection Model with Dysample and Spatial Context Awareness Module

The threat of forest fires to human life and property causes significant damage to human society. Early signs, such as small fires and smoke, are often difficult to detect. As a consequence, early detection of smoke and fires is crucial. Traditional forest fire detection models have shortcomings, including low detection accuracy and efficiency. The YOLOv8 model exhibits robust capabilities in detecting forest fires and smoke. However, it struggles to balance accuracy, model complexity, and detection speed. This paper proposes LD-YOLO, a lightweight dynamic model based on the YOLOv8, to detect forest fires and smoke. Firstly, GhostConv is introduced to generate more smoke feature maps in forest fires through low-cost linear transformations, while maintaining high accuracy and reducing model parameters. Secondly, we propose C2f-Ghost-DynamicConv as an effective tool for increasing feature extraction and representing smoke from forest fires. This method aims to optimize the use of computing resources. Thirdly, we introduce DySample to address the loss of fine-grained detail in initial forest fire images. A point-based sampling method is utilized to enhance the resolution of small-target fire images without imposing an additional computational burden. Fourthly, the Spatial Context Awareness Module (SCAM) is introduced to address insufficient feature representation and background interference. Also, a lightweight self-attention detection head (SADH) is designed to capture global forest fire and smoke features. Lastly, Shape-IoU, which emphasizes the importance of boundaries’ shape and scale, is used to improve smoke detection in forest fires. The experimental results show that LD-YOLO realizes an mAP0.5 of 86.3% on a custom forest fire dataset, which is 4.2% better than the original model, with 36.79% fewer parameters, 48.24% lower FLOPs, and 15.99% higher FPS. Therefore, LD-YOLO indicates forest fires and smoke with high accuracy, fast detection speed, and a low model complexity. This is crucial to the timely detection of forest fires.

Estimating fuel load for wildfire risk assessment at regional scales using earth observation data: A case study in Southwestern Australia

The risk of wildfires is increasing globally and models are critical to reducing this risk. Such models require information on fuel load, a crucial factor of fire behaviour, which is generally determined using a combination of fuel age and fuel accumulation models. Traditionally, estimating fuel load relies on manually compiled fire history data (MCFH). In this paper, we introduce an approach to estimate fuel load using readily available earth observation (EO) data, MODIS MCD64A1. The approach is applied to a wildfire-prone region in Southwestern Australia from 2001 to 2021. Results suggest that MODIS produces more accurate and reliable estimates of fuel load compared with MCFH. It is effective in maintaining spatially and temporally complete records of fires, as it reports 11,019 more hectares of burned areas associated with wildfires over the study period. MODIS performs better in capturing wildfires than prescribed burns, as the spatial overlapping ratio is higher for wildfires (0.63) than prescribed burns (0.42). The high agreement between the two datasets for fuel load estimation (weighted kappa of 0.91) results from grassland covering the majority of the landscape. However, the agreement is reduced for other vegetation types — 0.24 for pine, 0.36 for mallee heath, 0.39 for shrubland, and 0.58 for forest. MODIS has lower effectiveness in detecting small and under-canopy fires such as prescribed burns, suggesting the value in combining EO and manually compiled data to obtain improved estimates of fuel load. Due to the scope of objectives, the integration of EO and MCFH has not been fully explored in this study, which will be included in our future research. This study highlights the potential of earth observation data in assessing wildfire risk as the data are easily accessible and reliable, as well as efficient and cost-effective, and they provide the opportunity to develop mitigation strategies at regional scales.

Small scale pool fires: The case of toluene

Industrial applications adopting toluene as a solvent have been largely extended in recent years, including solutions within the framework of the energy transition and energy storage technologies. The potential use of this flammable compound in a different set of operative conditions and compositions requires a comprehensive and complete knowledge of its fire behaviour and combustion kinetic. To this scope, an innovative experimental procedure applicable to liquid reactive systems was developed for this scope and implemented at different boundary conditions. More specifically, the specimen was exposed to air and heat fluxes between 7 and 50 kW/m2, at a constant sample surface of 0.01 m2, an initial sample thickness of 0.01 m, and a distance between the sample and the horizontally oriented conical-shaped heater of 0.025 m. Measurements were compared with data from the current literature, when available, demonstrating the robustness and validity of the adopted procedure. Although an increase in the external flux leads to growing mass burning rates (i.e., from 0.47 g/s to 0.85 g/s), negligible effects on the ignitability were observed. Conversely, a peak in the heat release rate at 35 kW/m2 was measured. The observed reduction at higher external heat fluxes was attributed to less effective combustion, demonstrating that the maximum expected heat flux cannot be considered as an aprioristic worst-case scenario for the evaluation of pool fires. The collected data were, then, further utilized to obtain insights on the formation of the main products, including soot tendency. Based on the collected data a simplified kinetic model suitable for the computational fluid dynamics was proposed to reproduce the chemistry of the system.

분전반, 배전반 등에 사용하는 질산칼륨 기반의 소공간 소화장치의 성능에 관한 기초 연구 - 소화메커니즘 및 A급 소화성능을 중심으로

As there was no objective date on the fire extinguishing mechanism and performance of potassium nitrate-based small space fire extinguisher that can automatically extinguish fires in small spaces (less than 0.36 ㎡) such as distribution boards and distribution boards, we attemped to conduct basic research on this. We sought to confirm the importance of fires in switchboards and other devices through fire statistical analysis of electrical factors, and analyzed fire extinguishing mechanisms that react with heat based on potassium nitrate. We also sought to determine the interconnectivity of related variables through Class A fire extinguishing tests. Through analysis of fire statistics, the importance of electrical fires was confirmed and the cause of fire in the switchboard was investigated. By investigating its composition, it was found that it was a safe material that could be used for fire extinguishing, and that in the event of a fire, the thermal decomposition of KNO3 produced condensed aerosol particles, K2CO3, which could extinguish the fire. Through the Class A fire extinguishing test of the switchboard, it was confirmed that the fire could be reliably extinguished. The following conclusions were drawn through this study. 1. Analysis of fire statistics confirmed that electrical factors were the biggest cause of fire and that fires caused among this short circuit and insulation deterioration were the main causes. 2. The fire risk of switchboards, distribution boards, etc. was confirmed, and it was found that it was necessary to install simple fire extinguishers for small spaces that can quickly respond to and extinguish fires. 3. The fire extinguishing mechanism of potassium nitrate was confirmed to extinguish the fire by generating condensed aerosol particles, K2CO3, through thermal decomposition of KNO3 when a fire occurs. 4. Through the Class A fire extinguishing performance test, it was confirmed that there was a relationship between fire extinguishing delay time, weight, and volume, and the relationship could be expressed as , and it was found that a value of 1 or more is required for fire extinguishing.

Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques.

In this study, a novel method combining contour analysis with deep CNN is applied for fire detection. The method was made for fire detection using two main algorithms: one which detects the color properties of the fires, and another which analyzes the shape through contour detection. To overcome the disadvantages of previous methods, we generate a new labeled dataset, which consists of small fire instances and complex scenarios. We elaborated the dataset by selecting regions of interest (ROI) for enhanced fictional small fires and complex environment traits extracted through color characteristics and contour analysis, to better train our model regarding those more intricate features. Results of the experiment showed that our improved CNN model outperformed other networks. The accuracy, precision, recall and F1 score were 99.4%, 99.3%, 99.4% and 99.5%, respectively. The performance of our new approach is enhanced in all metrics compared to the previous CNN model with an accuracy of 99.4%. In addition, our approach beats many other state-of-the-art methods as well: Dilated CNNs (98.1% accuracy), Faster R-CNN (97.8% accuracy) and ResNet (94.3%). This result suggests that the approach can be beneficial for a variety of safety and security applications ranging from home, business to industrial and outdoor settings.

Efficient forest fire detection based on an improved YOLO model

A forest fire is a natural disaster characterized by rapid spread, difficulty in extinguishing, and widespread destruction, which requires an efficient response. Existing detection methods fail to balance global and local fire features, resulting in the false detection of small or hidden fires. In this paper, we propose a novel detection technique based on an improved YOLO v5 model to enhance the visual representation of forest fires and retain more information about global interactions. We add a plug-and-play global attention mechanism to improve the efficiency of neck and backbone feature extraction of the YOLO v5 model. Then, a re-parameterized convolutional module is designed, and a decoupled detection head is used to accelerate the convergence speed. Finally, a weighted bi-directional feature pyramid network (BiFPN) is introduced to merge feature information for local information processing. In the evaluation, we use the complete intersection over union (CIoU) loss function to optimize the multi-task loss for different kinds of forest fires. Experiments show that the precision, recall, and mean average precision are increased by 4.2%, 3.8%, and 4.6%, respectively, compared with the classic YOLO v5 model. In particular, the mAP@0.5:0.95 is 2.2% higher than the other detection methods, while meeting the requirements of real-time detection.

Small Fires, Big Impact: Evaluating Fire Emission Estimates in Southern Africa Using New Satellite Imagery of Burned Area and Carbon Monoxide

AbstractVarious fire emission estimates for southern Africa during 2019, derived with multiple burned area data sets with resolutions ranging from 500 to 20 m, are evaluated using satellite carbon monoxide (CO) observations. Southern African emissions derived from burned area generated by 20 m Sentinel‐2 satellite imagery are up to 120% higher than other estimates because small fires are better detected with a higher‐resolution satellite instrument. A comprehensive comparison between simulated and observed atmospheric CO indicates that the Sentinel‐2 burned area data significantly improves emission estimates, with up to 15% reduction in CO concentration biases in comparison to emissions based on coarser resolution burned area data. We also found that the temporal lag between emissions and atmospheric CO concentrations during the peak fire month was related to atmospheric transport. These findings emphasize the importance of utilizing higher‐resolution satellite instruments in accurately estimating emissions and understanding the impact of small fires on global climate.

FSH-DETR: An Efficient End-to-End Fire Smoke and Human Detection Based on a Deformable DEtection TRansformer (DETR).

Fire is a significant security threat that can lead to casualties, property damage, and environmental damage. Despite the availability of object-detection algorithms, challenges persist in detecting fires, smoke, and humans. These challenges include poor performance in detecting small fires and smoke, as well as a high computational cost, which limits deployments. In this paper, we propose an end-to-end object detector for fire, smoke, and human detection based on Deformable DETR (DEtection TRansformer) called FSH-DETR. To effectively process multi-scale fire and smoke features, we propose a novel Mixed Encoder, which integrates SSFI (Separate Single-scale Feature Interaction Module) and CCFM (CNN-based Cross-scale Feature Fusion Module) for multi-scale fire, smoke, and human feature fusion. Furthermore, we enhance the convergence speed of FSH-DETR by incorporating a bounding box loss function called PIoUv2 (Powerful Intersection of Union), which improves the precision of fire, smoke, and human detection. Extensive experiments on the public dataset demonstrate that the proposed method surpasses state-of-the-art methods in terms of the mAP (mean Average Precision), with mAP and mAP50 reaching 66.7% and 84.2%, respectively.

Revisiting the 2023 wildfire season in Canada

The area burned by wildfires in Canada in 2023 is unprecedented in historical records. To help ensure the safety of communities and support the mobilization of firefighting resources, rapid detection of areas affected by wildfires is required. Satellite data are ideally suited to provide near real-time wildfire information over large areas. At the same time, clouds, smoke, and haze can obscure the collection of observations from sensors typically used for mapping purposes. Established methods using coarse spatial resolution satellites (e.g., MODIS, VIIRS) rely upon the combination of daily revisit to enable the rapid and reliable detection of large active fires, in full or in part, and the application of modeling (including spatial buffering) to infer additional, yet still obscured, areas. While timely, these initial maps of wildfire-impacted areas do not capture small fires (those smaller than 200 ha) and, importantly, are not intended to differentiate unburned areas within fire perimeters. To address these limitations, we used data from Sentinel-2A and -2B, and Landsat-8 and -9, which form a virtual constellation of four satellites to revisit and map burned area in Canada's forested ecosystems for the 2023 fire season. Availing upon the high temporal data density and using the Tracking Intra- and Inter-year Change algorithm (TIIC), an aggregate seasonal mapping of wildfires resulted in a total area affected by wildfires in 2023 of 12.74 Mha. Within this total area, 9.51 Mha of treed land cover was impacted. Shrubs and wetlands comprised most of the remaining non-treed area that was burned. Using a 2022 map of aboveground treed biomass (AGB), approximately 0.649 Pg of AGB was impacted by 2023 wildfires, representing an 11-fold increase in AGB impacts relative to a long-term annual average of treed AGB loss. Differences between the estimate of total burned area reported herein and the total burned area indicated by the Natural Resources Canada (NRCan) Fire M3 hotspot fire perimeters (18.64 Mha) were analyzed. Overall, estimates of burned area differed by 5.9 Mha, including over 1.13 Mha of water identified as burned within the NRCan perimeters. Differences in land cover and AGB impacts between the two products were also investigated and quantified. TIIC enables the near-continuous capture of areas impacted by fire through the fire season, allowing for within-year refinement of total burned area, rapid interrogation of land cover types impacted, and estimation of associated biomass consequences.

Causes of Ammonium Nitrate Explosions and Handling Mechanisms – Case Study of the 2020 Beirut Lebanon Explosion

Ammonium nitrate (AN) is a chemical that is widely used in industry, for example as fertilizer in agriculture, explosives in military and civilian applications (for example in mining) or as a basic ingredient in solid propellants. However, as a dangerous chemical, storing ammonium nitrate can cause problems if not properly. The characteristics of AN have been studied extensively that pure AN is stable at room temperature but can explode when mixed with impurities in a closed space or in the presence of a heat/flame source nearby. This was proven in the explosion disaster at the Port of Beirut, Lebanon in 2020. This article was written using a systematic literature review (SLR) approach, where the data used came from articles published on Google Scholar, DOAJ, Emerald, Springer, and Science Direct with relevant keywords according to the topic. This article aims to evaluate and find out the causes of the ammonium nitrate explosion disaster in Beirut, as well as provide an analysis of suggestions for handling mechanisms so that a similar incident does not happen again. Evaluation of the cause of the explosion at the Port of Beirut, with fire/heat burning down part of the warehouse. An that was stored in a port warehouse mixed with other goods caused a large explosion which was started by a small fire in the warehouse. The explosion caused the death of 220 people, injured 6,500 people, formed a crater 140 meters deep and an earthquake measuring 3.3 on the Richter scale. This explosion is classified as the third most destructive urban explosion of all time, after the atomic bombs on Hiroshima and Nagasaki at the end of World War II. As a precaution, proper handling and storage of AN is required in accordance with international regulations. To reduce the potential danger of explosions without causing significant risks, this is done through an appropriate disposal mechanism.

Autonomous firefighting using a quadruped robot

This paper presents the development of a proof-of-concept autonomous firefighting robot based on the Spot quadruped robot. Both the payload and software package for the autonomous detection, localization, and extinguishing of a small fire are discussed. A thermal camera is mounted to the front of Spot and a standard ABC fire extinguisher is mounted to the back. The thermal camera is used to autonomously detect fires and the fire extinguisher is used to put out a detected fire. The system was successfully tested at a local fire station in a controlled setting with actual fires. The results of this project have direct application in the area of autonomous first response in complex industrial environments and fire watch applications.

LUFFD-YOLO: A Lightweight Model for UAV Remote Sensing Forest Fire Detection Based on Attention Mechanism and Multi-Level Feature Fusion

The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-and-play enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images.

Climate change may shift metapopulations towards unstable source-sink dynamics in a fire-killed, serotinous shrub.

Climate change, with warming and drying weather conditions, is reducing the growth, seed production, and survival of fire-adapted plants in fire-prone regions such as Mediterranean-type ecosystems. These effects of climate change on local plant demographics have recently been shown to reduce the persistence time of local populations of the fire-killed shrub Banksia hookeriana dramatically. In principle, extinctions of local populations may be partly compensated by recolonization events through long-distance dispersal mechanisms of seeds, such as post-fire wind and bird-mediated dispersal, facilitating persistence in spatially structured metapopulations. However, to what degree and under which assumptions metapopulation dynamics might compensate for the drastically increased local extinction risk remains to be explored. Given the long timespans involved and the complexity of interwoven local and regional processes, mechanistic, process-based models are one of the most suitable approaches to systematically explore the potential role of metapopulation dynamics and its underlying ecological assumptions for fire-prone ecosystems. Here we extend a recent mechanistic, process-based, spatially implicit population model for the well-studied fire-killed and serotinous shrub species B. hookeriana to a spatially explicit metapopulation model. We systematically tested the effects of different ecological processes and assumptions on metapopulation dynamics under past (1988-2002) and current (2003-2017) climatic conditions, including (i) effects of different spatio-temporal fires, (ii) effects of (likely) reduced intraspecific plant competition under current conditions and (iii) effects of variation in plant performance among and within patches. In general, metapopulation dynamics had the potential to increase the overall regional persistence of B. hookeriana. However, increased population persistence only occurred under specific optimistic assumptions. In both climate scenarios, the highest persistence occurred with larger fires and intermediate to long inter-fire intervals. The assumption of lower intraspecific plant competition caused by lower densities under current conditions alone was not sufficient to increase persistence significantly. To achieve long-term persistence (defined as >400 years) it was necessary to additionally consider empirically observed variation in plant performance among and within patches, that is, improved habitat quality in some large habitat patches (≥7) that could function as source patches and a higher survival rate and seed production for a subset of plants, specifically the top 25% of flower producers based on current climate conditions monitoring data. Our model results demonstrate that the impacts of ongoing climate change on plant demographics are so severe that even under optimistic assumptions, the existing metapopulation dynamics shift to an unstable source-sink dynamic state. Based on our findings, we recommend increased research efforts to understand the consequences of intraspecific trait variation on plant demographics, emphasizing the variation of individual traits both among and within populations. From a conservation perspective, we encourage fire and land managers to revise their prescribed fire plans, which are typically short interval, small fires, as they conflict with the ecologically appropriate spatio-temporal fire regime for B. hookeriana, and likely as well for many other fire-killed species.

Few large or many small fires: Using spatial scaling of severe fire to quantify effects of fire‐size distribution shifts

AbstractAs wildfire activity increases and fire‐size distributions potentially shift in many forested regions worldwide, anticipating the spatial patterns of burn severity expected with future fire activity is critical for ecological understanding and informing management and policy. Because spatial patterns of burn severity are influenced by a complex mixture of drivers, they remain difficult to predict for any given burned landscape. At broader extents, however, spatial scaling relationships relating high‐severity patch size and shape to overall fire size, when combined with scenarios regarding regional area burned and fire‐size distributions, offer a means to anticipate the spatial configuration of burn severity in future fires. Here, leveraging a satellite burn‐severity dataset for 1615 fire events occurring across the northwest United States between 1985 and 2020, we present an approach for simulating expected patch‐level burn‐severity patterns at the scale of a region or fire regime of interest. We demonstrate this approach in a historically climate‐limited fire regime within the Pacific Northwest, USA, where relatively infrequent but large and severe fires shape biomass‐rich forests, and where fire potential is projected to increase as summer fire seasons become warmer and drier. We quantify how, for a given total burned area, the range of cumulative burn‐severity patterns is expected to vary with the size distributions of fire events. Our results illustrate how shifts in fire‐size distributions toward larger fire events will lead to increasingly large high‐severity burn patches with interior areas that are increasingly far from unburned seed sources following fire. In contrast, the same total area burned in more numerous but smaller fire events will result in qualitatively different cumulative patterns of burn severity, characterized by smaller high‐severity patches and closer proximity to postfire seed sources across burned landscapes. These results have important implications in forested regions, informing management actions ranging from prefire planning (e.g., fire response preparedness) to real‐time decision‐making (e.g., fire suppression vs. managed wildfire use) and postfire responses (e.g., replanting to restore tree cover and/or promoting early‐seral habitat). The approach we present is generalizable and can be applied across regions and fire regimes to anticipate potential future fire effects.

Remote sensing applications for prescribed burn research

Prescribed burning is a key management strategy within fire-adapted systems, and improved monitoring approaches are needed to evaluate its effectiveness in achieving social-ecological outcomes. Remote sensing provides opportunities to analyse the impacts of prescribed burning, yet a comprehensive understanding of the applications of remote sensing for prescribed burn research is lacking. We conduct a literature review of 120 peer-reviewed publications to synthesise the research aims, methodologies, limitations and future directions of remote sensing for the analysis of prescribed fire. Studies evaluating management outcomes found prescribed burning effective for wildfire risk reduction, yet few analysed co-benefits or trade-offs with other management goals. Most studies use passive, spaceborne, low spatial resolution sensors, characterised in the literature as consistent and accessible data sources but limited in detecting small, low-severity and short-duration fires characteristic of prescribed burns. In contrast, active remote sensing approaches including LiDAR are less frequently employed, but show promise for highly accurate, spatially explicit 3D vegetation and fuel load mapping. Remote sensing advances toward higher spatial resolution, more frequent revisit, denser spectral sampling and more data across the electromagnetic spectrum are critical to advancing prescribed fire research, addressing current methodological gaps, and improving fuels and fire management capacity.