Fire Perimeter Research Articles

Dynamic correction of forest fire spread prediction using observation error covariance matrix estimation technique based on FLC-GRU

BackgroundData assimilation (DA) techniques have played a significant role in improving the prediction accuracy of forest fire spread. The dynamic correction technique weights the predicted and observed values to obtain an analytical value that better reflects the position of the fire perimeter. The weighted importance of each contribution is determined by the magnitude of its associated error. However, as a crucial parameter affecting prediction accuracy, the covariance matrix of observation errors is often inaccurate and neglects its own temporal correlation. This is unfriendly to spread prediction results. To address this issue, we proposed a targeted technique for estimating the observation error covariance matrix (R matrix) based on the Fire Line Convolutional Gated Recurrent Unit (FLC-GRU).ResultsWe integrated this method into the DA framework and validated its applicability and accuracy using Observing System Simulation Experiment (OSSE). Through comparisons with traditional methods, the results indicate that using the FLC-GRU estimated R matrix for correction calculations leads to wildfire prediction locations that are closer to the true values.ConclusionsThe proposed approach learns the covariance matrix directly from time-series observed fire line data, without requiring any prior knowledge or assumptions about the error distribution, in contrast to classical posterior tuning methods. The proposed method significantly improves the rationality and accuracy of R matrix estimation, enhances the utility of observational data, and thereby improves the correction accuracy of forest fire spread predictions. Moreover, the study also demonstrates the applicability of the proposed method within the DA framework.

Post-fire vegetation dynamic patterns and drivers in Greater Hinggan Mountains: Insights from long-term remote sensing data analysis

Fire has become a major disturbing factor in boreal forests, and giant forest disturbances play a vital role in regulating the climate under global warming. Therefore, it is essential to investigate the spatiotemporal patterns and main drivers of post-fire vegetation recovery for forest ecological research and post-fire recovery management. However, previous studies have focused on the post-fire forest change within the entire fire perimeter, lacking separate analysis and comparison of the burned zone (BZ) and unburned zone (UNBZ). Here, we propose the utilization of Moderate Resolution Imaging Spectroradiometer land cover type and vegetation index data to monitor vegetation dynamics and explore its drivers after the most serious forest fire in the history of P.R. China in the Greater Hinggan Mountains (GHM). The temporal and spatial patterns of vegetation recovery in the BZ/UNBZ in the GHM were analyzed using the Sen & Mann-Kendall method, Hurst index and coefficient of variation, and their driving mechanisms were explored using GeoDetector and geographically weighted regression. The results showed that there were significant differences in the spatial distribution and fluctuation of vegetation between the BZ and UNBZ, and that the BZ exhibited higher productivity and vigor. Vegetation recovery was influenced by different dominant factors and changed over time, in which land surface temperature and precipitation dominated all the time, whereas topographic relief and elevation had a more significant contribution to vegetation recovery in the BZ and UNBZ, respectively. This study provides a scientific basis for the protection and management of vegetation in disturbed forested areas, particularly after fires.

Active Fire Clustering and Spatiotemporal Dynamic Models for Forest Fire Management

Forest fires are increasingly destructive, contributing to significant ecological damage, carbon emissions, and economic losses. Monitoring these fires promptly and accurately, particularly by delineating fire perimeters, is critical for mitigating their impact. Satellite-based remote sensing, especially using active fire products from VIIRS and MODIS, has proven indispensable for real-time forest fire monitoring. Despite advancements, challenges remain in accurately clustering and delineating fire perimeters in a timely manner, as many existing methods rely on manual processing, resulting in delays. Active fire perimeter (AFP) and Timely Active Fire Progression (TAFP) models were developed which aim to be an automated approach for clustering active fire data points and delineating perimeters. The results demonstrated that the combined dataset achieved the highest matching rate of 85.13% for fire perimeters across all size classes, with a 95.95% clustering accuracy for fires ≥100 ha. However, the accuracy decreased for smaller fires. Overall, 1500 m radii with alpha values of 0.1 were found to be the most effective for fire perimeter delineation, particularly when applied at larger radii. The proposed models can play a critical role in improving operational responses by fire management agencies, helping to mitigate the destructive impact of forest fires more effectively.

Coupled Fire–Atmosphere Simulations of the Split, Croatia, Wildfire

Abstract The Weather Research and Forecasting Spread FIRE (WRF-SFIRE) model was used to simulate the evolution of the fire perimeter for the Split wildfire in Croatia, evaluated in our previous detailed reconstruction of the event. A four-domain configuration was applied, with the innermost domain having a resolution of 500 m with a planetary boundary layer parameterization. The coupled fire grid was further downscaled to 33.3-m resolution, using the best available estimates of topography and fuel load data. WRF-SFIRE simulated slightly smaller fire areas in the four observed fire perimeter stages of the Split wildfire event, especially the northwestward spread in the last stage along the Adriatic coast. Additional experiments with multiple ignitions in the WRF-SFIRE model, representing the spotting mechanism, successfully simulated the northwestward spread in the event. The study also included sensitivity experiments that turned off the heat and moisture flux coupling between WRF and the fire grid. It was found that the simulated fire area is larger than the model run that included heat and moisture feedback from the fire grid to the atmosphere, which is contrary to some previous studies. The large wind speed and convergence along the fire line in our feedback-on simulation, which constrain the fire when there are feedback processes, were likely the cause of the smaller fire area. Last, areas of pine that burnt in the Split fire event favored crown fire spread, which is not represented in the current modeling framework of WRF-SFIRE.

Wildfire Threshold Detection and Progression Monitoring Using an Improved Radar Vegetation Index in California

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance.

Mapping the wildland-urban interface at municipal level for wildfire exposure analysis in mainland Portugal

The Wildland-Urban Interface (WUI), where vegetation and built-up structures intermingle, encompasses a variety of territorial elements that interact spatially, being variable both in space and time. Mapping the WUI at finer scales is paramount to assess wildfire exposure and define tailored mitigation strategies. Our aim was to develop a semi-automated method to map the WUI at municipal level, leveraging recent advances in data and technology. We tested the procedure in four municipalities of mainland Portugal with different fire history, biophysical conditions, and sociodemographic contexts. We considered WUI as either intermix or interface. Our approach integrates both building location data and high-resolution vegetation maps, to calculate the density of buildings and forest cover proportion within different circular moving window sizes. Within each radius, we evaluated the total area and spatial distribution of the WUI types, as well as the number of buildings within WUI and within the fire perimeters recorded between the years 2000 and 2022 and analysed the differences between municipalities. We then compared the mapped WUI with previous WUI mappings for mainland Portugal, to identify common spots and potential spatial divergences. We found that the area mapped as WUI within all four municipalities ranged from about 400 km2 to 1135 km2 depending on the radius size. A distinct distribution for each type of WUI was observed as the radius size increased: the intermix WUI showed a tendency to increase, and the interface WUI increased only between the radius of 100 and 200 m, decreasing gradually in subsequent radii. Between 39.4% and 45.5% of the nearly 200,000 buildings in the study areas were within WUI, depending on radius size and a total of 5436 buildings were within the historic fire perimeter. Although the comparison with other maps showed fair agreement, due to differences in data and methodology, common areas mapped as WUI were found, which suggests that these areas should receive greater attention from decision-makers regarding fire management strategies, since their classification as WUI remains consistent across different methodologies.

Fire refugia patch dynamics differ between prescribed fires and wildfires in longleaf pine savanna

Increasing wildfire activity has spurred an increasing push for the application of prescribed fire to reduce wildfire risk while simultaneously acting as a surrogate for fire’s historical role as a fundamental ecosystem process. However, prescribed fires are often ignited using uniform ignitions to maintain operational control and there are concerns that they may not be able to replicate the landscape heterogeneity, particularly patterns in unburned patches, generated by historical fires. Fire refugia, unburned areas within fire perimeters, play an integral role in determining post-fire recovery and community structure. We assess patterns in fire refugia across 443 large (>200 ha) wildfire and prescribed fire perimeters using remotely sensed fire severity data in longleaf pine (Pinus palustris) savannas of the Florida Panhandle. Contrary to concerns, large prescribed fires had a significantly greater proportion of unburned area than wildfires, driven by larger refugia patch sizes. Drier conditions promoted smaller and more numerous fire refugia patches. Our study demonstrates differences in wildfires versus prescribed fire outcomes on landscape structure, with implications for future longleaf pine savanna management.

Sensitivity of Fire Indicators on Forest Inventory Plots Is Affected by Fire Severity and Time since Burning

Forest inventory data are useful for determining forest stand structure, growth, and change. Among the information collected on forest inventory plots by the USDA Forest Service Forest Inventory and Analysis Program, attributes characterizing various types of disturbance provide researchers a means of selecting plots specifically affected by disturbances, such as fire. We determine the performance of three of these attributes as indicators of recent fires on forest inventory plots of the United States by comparing them to independent records of wildland fire occurrence. The indicators are plot-level observations of fire effects on (1) general site appearance, (2) tree mortality, and (3) damage to live trees. Independent spatial layers of wildland fire perimeters provide an approach to test indicator performance and identify characteristics of fires that may affect detection. The sensitivities of indicators are generally higher in the West relative to the East. Detection rates exceed 90 percent for the Pacific Coast forests but seldom reach 80 percent in the East. Among the individual indicators, site appearance has higher identification rates than tree indicators for fires in the Pacific Coast, Great Plains, North, and South regions. Tree mortality is the most important single indicator for identifying Rocky Mountain fires. Tree damage is more important than tree mortality in the South; otherwise, the tree damage indicator is of relatively lower importance, particularly where high-severity fires are common, and tree survival is low. The rate of detection by the indicators is affected by the severity of the fire or the recency of the fire. The joint effect of severity and recency influence all three indicators for the Pacific Coast and Rocky Mountain fires, as well as the site appearance indicator in the South. Only a small proportion of fires are clearly missed by all three of the indicators.

Post-Fire Vegetation (Non-)Recovery across the Edges of a Wildfire: An Unexplored Theme

Wildfires have a significant influence on ecosystems globally, shaping vegetation, biodiversity, landscapes, soil properties, and other ecosystem processes. Despite extensive research on different aspects of wildfires, the edges of burned areas remain understudied, even though they involve complex dynamics. In this study, we analyzed the post-fire vegetation recovery across the edges of a large wildfire in a Mediterranean area. The investigations were focused on patches of woodlands that, in a previous study, showed a normalized burn ratio (NBR) decline one year after the fire. Field vegetation surveys were carried out in areas characterized by different NBR recovery rates and in areas outside the burned area as controls. Five hypotheses were tested, identifying delayed tree mortality as a key factor linked to NBR decline, particularly in low-severity fire zones in proximity to the fire edges. Delayed mortality, observed predominantly near the edges, may also affect unburned or less severely burned patches within the main fire perimeter, highlighting the need for ongoing monitoring. As these areas play a crucial role in the post-fire succession and vegetation dynamics, understanding the second-order effects of a fire is imperative for effective ecosystem management. This study underscores the importance of the long-term assessment of fire impacts, emphasizing the necessity of field surveys alongside remote sensing. Continued observation is essential to elucidate the enduring impacts of wildfires and to facilitate informed restoration strategies.

Forty-Year Fire History Reconstruction from Landsat Data in Mediterranean Ecosystems of Algeria following International Standards

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks a complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics, and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions as well as temporal and geographical coverage. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated, and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30 m spatial resolution in the typical Mediterranean ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10 m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10%; commission and omission errors < 8.20%; Dice coefficients > 91.90%; and relative biases < 3.44%. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most continuous and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution to further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.

Comparison of Perimeter Delineation Methods for Remote Sensing Fire Spot Data in Near/Ultra-Real-Time Applications

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies.

Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts

Abstract Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements toward improving fire spread forecasts from numerical models through data assimilation. This work develops a physics-informed approach for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere–wildfire models from a measured wildfire state. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein generative adversarial network (cWGAN), trained with WRF–SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high-resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sørensen’s coefficient of 0.81 for the fire perimeters and an average ignition time difference of 32 min suggest that the method is highly accurate. Significance Statement To initialize coupled atmosphere–wildfire simulations in a physically consistent way based on satellite measurements of active fire locations, it is critical to ensure the state of the fire and atmosphere aligns at the start of the forecast. If known, the history of a wildfire may be used to develop an atmospheric state matching the wildfire state determined from satellite data in a process known as spinup. In this paper, we present a novel method for inferring the early stage history of a wildfire based on satellite active fire measurements. Here, inference of the fire history is performed in a probabilistic sense and physics is further incorporated through the use of training data derived from a coupled atmosphere–wildfire model.

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.

Future fire events are likely to be worse than climate projections indicate – these are some of the reasons why

Background Climate projections signal longer fire seasons and an increase in the number of dangerous fire weather days for much of the world including Australia. Aims Here we argue that heatwaves, dynamic fire–atmosphere interactions and increased fuel availability caused by drought will amplify potential fire behaviour well beyond projections based on calculations of afternoon forest fire danger derived from climate models. Methods We review meteorological dynamics contributing to enhanced fire behaviour during heatwaves, drawing on examples of dynamical processes driving fire behaviour during the Australian Black Summer bushfires of 2019–20. Results Key dynamical processes identified include: nocturnal low-level jets, deep, unstable planetary boundary layers and fire–atmosphere coupling. Conclusions The future scenario we contend is long windows of multi-day fire events where overnight suppression is less effective and fire perimeters will expand continuously and aggressively over multiple days and nights. Implications Greater overnight fire activity and multi-day events present strategic and tactical challenges for fire management agencies including having to expand resourcing for overnight work, manage personnel fatigue and revise training to identify conditions conducive to unusually active fire behaviour overnight. Effective messaging will be critical to minimise accidental fire ignition during heatwaves and to alert the community to the changing fire environment

A modest increase in fire weather overcomes resistance to fire spread in recently burned boreal forests.

Recently burned boreal forests have lower aboveground fuel loads, generating a negative feedback to subsequent wildfires. Despite this feedback, short-interval reburns (≤20 years between fires) are possible under extreme weather conditions. Reburns have consequences for ecosystem recovery, leading to enduring vegetation change. In this study, we characterize the strength of the fire-fuel feedback in recently burned Canadian boreal forests and the weather conditions that overwhelm resistance to fire spread in recently burned areas. We used a dataset of daily fire spread for thousands of large boreal fires, interpolated from remotely sensed thermal anomalies to which we associated local weather from ERA5-Land for each day of a fire's duration. We classified days with >3 ha of fire growth as spread days and defined burned pixels overlapping a fire perimeter ≤20 years old as short-interval reburns. Results of a logistic regression showed that the odds of fire spread in recently burned areas were ~50% lower than in long-interval fires; however, all Canadian boreal ecozones experienced short-interval reburning (1981-2021), with over 100,000 ha reburning annually. As fire weather conditions intensify, the resistance to fire spread declines, allowing fire to spread in recently burned areas. The weather associated with short-interval fire spread days was more extreme than the conditions during long-interval spread, but overall differences were modest (e.g. relative humidity 2.6% lower). The frequency of fire weather conducive to short-interval fire spread has significantly increased in the western boreal forest due to climate warming and drying (1981-2021). Our results suggest an ongoing degradation of fire-fuel feedbacks, which is likely to continue with climatic warming and drying.

Exploring Drivers of Wildfires in Spain

Wildfires play a dual role in ecosystems by providing ecological benefits while posing catastrophic events; they also inflict non-catastrophic damage and yield long-term effects on biodiversity, soil quality, and air quality, among other factors, including public health. This study analysed the key determinants of wildland fires in Spain using openly available spatial data from 2008 to 2021, including fire perimeters, bioclimatic variables, topography, and socioeconomic datasets, at a resolution of 1 km2. Our methodology combined principal component analysis (PCA), linear regression analysis, and one-way analysis of variance (ANOVA). Our findings show that scrub/herbaceous vegetation (average 63 ± 1.45% SE) and forests (average 19 ± 0.76% SE) have been highly susceptible to wildfires. The population density exhibited a robust positive correlation with wildfire frequency (R2 = 0.88, p < 0.0001). Although the study provides insights into some fire-related climatic drivers over Spain, it includes only temperature and precipitation-based variables and does not explicitly consider fuel dynamics. Therefore, a more advanced methodology should be applied in the future to understand the local specifics of regional wildfire dynamics. Our study identified that scrub/herbaceous areas and forests near densely populated regions should be prioritised for wildfire management in Spain, particularly under changing climate conditions.

Evidence of increasing wildfire damage with decreasing property price in Southern California fires.

Across the Western United States, human development into the wildland urban interface (WUI) is contributing to increasing wildfire damage. Given that natural disasters often cause greater harm within socio-economically vulnerable groups, research is needed to explore the potential for disproportionate impacts associated with wildfire. Using Zillow Transaction and Assessment Database (ZTRAX), hereafter "Zillow", real estate data, we explored whether lower-priced structures were more likely to be damaged during the most destructive, recent wildfires in Southern California. Within fire perimeters occurring from 2000-2019, we matched property price data to burned and unburned structures. To be included in the final dataset, fire perimeters had to surround at least 25 burned and 25 unburned structures and have been sold at most seven years before the fire; five fires fit these criteria. We found evidence to support our hypothesis that lower-priced properties were more likely to be damaged, however, the likelihood of damage and the influence of property value significantly varied across individual fire perimeters. When considering fires individually, properties within two 2003 fires-the Cedar and Grand Prix-Old Fires-had statistically significantly decreasing burn damage with increasing property value. Occurring in 2007 and later, the other three fires (Witch-Poomacha, Thomas, and Woolsey) showed no significant relationship between price and damage. Consistent with other studies, topographic position, slope, elevation, and vegetation were also significantly associated with the likelihood of a structure being damaged during the wildfire. Driving time to the nearest fire station and previously identified fire hazard were also significant. Our results suggest that further studies on the extent and reason for disproportionate impacts of wildfire are needed. In the meantime, decision makers should consider allocating wildfire risk mitigation resources-such as fire-fighting and wildfire structural preparedness resources-to more socioeconomically vulnerable neighborhoods.

Low-severity fires in the boreal region: reproductive implications for black spruce stands in between stand-replacing fire events.

Stand-replacing crown fires are the most prevalent type of fire regime in boreal forests in North America. However, a substantial proportion of low-severity fires are found within fire perimeters. Here we aimed to investigate the effects of low-severity fires on the reproductive potential and seedling recruitment in boreal forests stands in between stand-replacing fire events. We recorded site and tree characteristics from 149 trees within twelve sites dominated by mature black spruce [Picea mariana (Mill.) B.S.P.] trees in the Northwest Territories, Canada. The presence of fire-scarred trees supported classification of sites as unburned or affected by low-severity fires in recent history. We used non-parametric tests to evaluate differences in site conditions between unburned and low-severity sites, and mixed effect models to evaluate differences in tree age, size, and reproductive traits among unburned trees and trees from low-severity sites. Results showed significantly higher density of dead black spruce trees in low-severity sites, and marginally significant higher presence of permafrost. Trees from low-severity fire sites were significantly older, exhibited significantly lower tree growth, and showed a tendency towards a higher probability of cone presence and percentage of open cones compared to trees from unburned sites. Surviving fire-scarred trees affected by more recent low-severity fires showed a tendency towards higher probability of cone presence and cone production. Density of black spruce seedlings significantly increased with recent low-severity fires. Trees in low-severity sites appeared to have escaped mortality from up to three fires, as indicated by fire scar records and their older ages. Shallow permafrost at low-severity sites may cause lower flammability, allowing areas to act as fire refugia. Low-severity surface fires temporarily enhanced the reproductive capacity of surviving trees and the density of seedlings, likely as a stress response to the fire event.

FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

The formulation and planning of integrated fire management strategies must be strengthened by decision support systems about fire-induced ecological impacts and ecosystem recovery processes, particularly in the context of extreme wildfire events that challenge land management initiatives. Wildfire data collection and analysis through remote sensing earth observations is of utmost importance for this purpose. However, the needs of land managers are not always met because the exploitation of the full potential of remote sensing techniques requires a high level of technical expertise. In addition, data acquisition and storage, database management, networking, and computing requirements may present technical difficulties. Here, we present FIREMAP software, which leverages the potential of Google Earth Engine (GEE) cloud-based platform, an intuitive graphical user interface (GUI), and the European Forest Fire Information System (EFFIS) wildfire database for wildfire analyses through remote sensing techniques and data collections. FIREMAP software allows automatic computing of (i) machine learning-based burned area (BA) detection algorithms to facilitate the mapping of (historical) fire perimeters, (ii) fire severity spectral indices, and (iii) post-fire recovery trajectories through the inversion of physically-based radiative transfer models. We introduce (i) the FIREMAP platform architecture and the GUI, (ii) the implementation of well-established algorithms for wildfire science and management in GEE, (iii) the validation of the algorithm implementation in fifteen case-study wildfires across the western Mediterranean Basin, and (iv) the near-future and long-term planned expansion of FIREMAP features.

A Fireline Displacement Model to Predict Fire Spread

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.