Fire Perimeter Research Articles

Country-level fire perimeter datasets (2001–2021)

Fire activity is changing across many areas of the globe. Understanding how social and ecological systems respond to fire is an important topic for the coming century. But many countries do not have accessible fire history data. There are several satellite-based products available as gridded data, but these can be difficult to access and use, and require significant computational resources and time to convert into a usable product for a specific area of interest. We developed an open source software package called Fire Event Delineation for python (FIREDpy) which automatically downloads and processes all of the source files for an area of interest from the MODIS burned area product, and runs a spatiotemporal flooding algorithm that converts those hundreds of grids into a single fire perimeter shapefile. Here we present a collection of fire event perimeter datasets for every country on the globe that we created using the FIREDpy software. We will continue to improve the efficiency and flexibility of the underlying algorithm, and intend to update these datasets annually.

Priorities and Effectiveness in Wildfire Management: Evidence from Fire Spread in the Western United States

Costs of fighting wildfires have increased substantially over the past several decades. Yet surprisingly little is known about the effectiveness of wildfire suppression or how wildfire incident managers prioritize resources threatened within a wildfire incident. We investigate the determinants of wildfire suppression effort using a novel empirical strategy comparing over 1,400 historical fire perimeters to the spatial distribution of assets at risk. We find that fires are more likely to stop spreading as they approach homes, particularly when homes are of greater value. This effect persists after controlling for physical factors (fuels, landscape, and weather) using a state-of-the-art wildfire simulation tool. As well, the probability that spread will be halted is affected by characteristics of homes 1–2 kilometers beyond a fire’s edge. Overall, we find that suppression efforts can substantively affect wildfire outcomes but that some groups may benefit more from wildfire management than others.

Evaluating a New Relative Phenological Correction and the Effect of Sentinel-Based Earth Engine Compositing Approaches to Map Fire Severity and Burned Area

The remote sensing of fire severity and burned area is fundamental in the evaluation of fire impacts. The current study aimed to: (i) compare Sentinel-2 (S2) spectral indices to predict field-observed fire severity in Durango, Mexico; (ii) evaluate the effect of the compositing period (1 or 3 months), techniques (average or minimum), and phenological correction (constant offset, c, against a novel relative phenological correction, rc) on fire severity mapping, and (iii) determine fire perimeter accuracy. The Relative Burn Ratio (RBR), using S2 bands 8a and 12, provided the best correspondence with field-based fire severity (FBS). One-month rc minimum composites showed the highest correspondence with FBS (R2 = 0.83). The decrease in R2 using 3 months rather than 1 month was ≥0.05 (0.05–0.15) for c composites and <0.05 (0.02–0.03) for rc composites. Furthermore, using rc increased the R2 by 0.05–0.09 and 0.10–0.15 for the 3-month RBR and dNBR compared to the corresponding c composites. Rc composites also showed increases of up to 0.16–0.22 and 0.08–0.11 in kappa values and overall accuracy, respectively, in mapping fire perimeters against c composites. These results suggest a promising potential of the novel relative phenological correction to be systematically applied with automated algorithms to improve the accuracy and robustness of fire severity and perimeter evaluations.

Tracking Wildfires With Weather Radars

AbstractThere is a need for nowcasting tools to provide timely and accurate updates on the location and rate of spread (ROS) of large wildfires, especially those impacting communities in the wildland urban interface. In this study, we demonstrate how fixed‐site weather radars can be used to fill this gap. Specifically, we develop and test a radar‐based fire‐perimeter tracking tool that leverages the tendency for local maxima in the radar reflectivity to be collocated with active fire perimeters. Reflectivity maxima are located using search radials from points inside a fire polygon, and perimeters are updated at intervals of ∼10 min. The algorithm is tested using publicly available Next Generation Weather Radar radar data for two large and destructive wildfires, the Camp and Bear Fires, both occurring in northern California, USA. The radar‐based fire perimeters are compared with available, albeit limited, satellite and airborne infrared observations, showing good agreement with conventional fire‐tracking tools. The radar data also provide insights into fire ROS, revealing the importance of long‐range spotting in generating ROS that exceeds conventional estimates. One limitation of this study is that high‐resolution fire perimeter validation data are sparsely available, precluding detailed error quantification for the radar estimates drawn from samples spanning a range of environmental conditions and radar configurations. Nevertheless, the radar tracking approach provides the basis for improved situational awareness during high‐impact fires.

California wildfire spread derived using VIIRS satellite observations and an object-based tracking system

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.

Mass fire behavior created by extensive tree mortality and high tree density not predicted by operational fire behavior models in the southern Sierra Nevada

Large, severe wildfires continue to burn in frequent-fire adapted forests but the mechanisms that contribute to them and their predictability are important questions. Using a combination of ground based and remotely sensed data we analyzed the behavior and patterns of the 2020 Creek Fire where drought and bark beetles had previously created substantial levels of tree mortality in the southern Sierra Nevada. We found that dead biomass and live tree densities were the most important variables predicting fire severity; high severity fire encompassed 41% of the area and the largest high severity patch (19,592 ha) comprised 13% of total area burned. Areas with the highest amounts of dead biomass and live tree densities were also positively related to high severity fire patch size indicating that larger, more homogenous conditions of this forest characteristic resulted in adverse, landscape-scale fire effects. The first two days of the Creek Fire were abnormally hot and dry but weather during the days of the greatest fire growth was largely within the normal range of variation for that time of year with one day with lower windspeeds. From September 5 to 8th the fire burned almost 50% of its entire area and fire intensity patterns inferred from remotely sensed brightness-temperature data were typical except on September 6th when heat increased towards the interior of the fire. Not only was the greatest heat concentrated away from the fire perimeter, but a significant amount of heat was still being generated within the fire perimeter from the previous day. This is a classic pattern for a mass fire and the high amount of dead biomass created from the drought and bark beetles along with high live tree densities were critical factors in developing mass fire behavior. Operational fire behavior models were not able to predict this behavior largely because they do not include post-frontal combustion and fire-atmosphere interactions. An important question regarding this mass fire is if the tree mortality event that preceded it could have been avoided or reduced or was it within the natural range of variation for these forests? We found that the mortality episode was outside of historical analogs and was exacerbated by past management decisions. The Creek Fire shows us how vulnerable of our current frequent-fire forest conditions are to suffering high tree mortality and offering fuel conditions capable of generating mass fires from which future forest recovery is questionable because of type conversion and probable reoccurring high severity fire.

Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting

The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time are computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.

Climate-induced fire regime amplification in Alberta, Canada

Acting as a top-down control on fire activity, climate strongly affects wildfire in North American ecosystems through fuel moisture and ignitions. Departures from historical fire regimes due to climate change have significant implications for the structure and composition of boreal forests, as well as fire management and operations. In this research, we characterize the relationship between trends in climate and fire regime characteristics, for a study area predominantly in Alberta, Canada. We examined trends of fire and climate in northwestern boreal forests using time series analysis of downscaled historical annual climate, fire history (1970–2019), and fire severity (the impacts of wildfire on plants and organic biomass; 1985–2018). We represented fire severity using the relativized burn ratio (RBR) calculated from multispectral Landsat imagery. The climate of the study area has significantly warmed and dried over the past 50 years. Over the same period the annual number of large wildfires, area burned, and fire sizes in the study area significantly increased. Furthermore, the likelihood, area, and number of extreme short-interval reburns (≤15 years between fires; 1985–2019) also significantly increased. During the study period, the portion of forested unburned islands within fire perimeters significantly declined, and fire severity (RBR) increased in open conifer and mixedwood forests. These fire regime changes are significantly correlated with annual climate variability, and a path analysis supports the hypothesis that annual climate patterns have led to fire regime shifts. The increasing fire activity in this region has implications for forest ecology and habitat availability, as the disruption of the fire regime is likely to alter forest recovery. Managers may face increasing challenges to fire suppression if the observed trends of increasing hotter and drier annual climate in the study area persist, driving extreme fire activity.

EVALUATION OF FIELD-BASED BURN INDICES FOR ASSESSING FOREST FIRE SEVERITY IN LUHANSK REGION, UKRAINE

Evaluation of forest fire severity is a basis of post-fire forest management. Remote sensing-based methods enable reliable delineation of fire perimeters, however, assessments of the degree of forest damage need to be verified and adjusted through field sampling. The forest damage assessment conducted in this study is useful for practitioners to understand and justify the design of clear cuts for restoration purposes. Thus, the aim of the study is to verify the different approaches to field assessment of forest fire severity. In this paper, the authors present a site-specific assessment of large wildfires in Luhansk oblast, Ukraine occurred in 2020 using field-based burn severity indices. The Composite Burn Index (CBI) and the Geometrically Structured Composite Burn Index (GeoCBI) were used to estimate the extent of forest damage. The Burned Area Emergency Response (BAER) methodology was also tested to assess the extent of soil damage. The authors used PlanetScope images to delineate perimeters of burned areas. These perimeters were overlaid over a forest inventory database to extract forest attributes and site characteristics for all forested and unforested areas affected by fires. Within the fire perimeters, the burned area was stratified into six strata to independently account for forest damage in diverse types of land cover. In total 73 test plots were proportionally distributed among different classes of land cover to assess fire severity using CBI, GeoCBI, and BAER approaches. It was found that the fire’s footprints covered 39,782 hectares. Among that area, 21.2% were forested lands. About 78% of burned forests were pine plantations. The highest fire intensity levels were estimated within pure pine plantations that were grown in very dry sites, while the lowest ones were associated with hardwoods forests in moisture site conditions. The average estimates of fire severity using the field-based indices varied within strata (CBI>GeoCBI) which could be an issue for assessing burn severity using remote sensing-based approaches. The authors also concluded that the BAER methodology contributed less to assessing the fire intensity because soil burn severity is not directly related to vegetation damage. This work creates a foundation for further assessment of fire severity using satellite imagery. As a result of this study, a spatial data set of sample plots was proposed that can facilitate calibrating approaches used to map fire severity in the region

Modelling Fire Behavior to Assess Community Exposure in Europe: Combining Open Data and Geospatial Analysis

Predicting where the next large-scale wildfire event will occur can help fire management agencies better prepare for taking preventive actions and improving suppression efficiency. Wildfire simulations can be useful in estimating the spread and behavior of potential future fires by several available algorithms. The uncertainty of ignition location and weather data influencing fire propagation requires a stochastic approach integrated with fire simulations. In addition, scarcity of required spatial data in different fire-prone European regions limits the creation of fire simulation outputs. In this study we provide a framework for processing and creating spatial layers and descriptive data from open-access international and national databases for use in Monte Carlo fire simulations with the Minimum Travel Time fire spread algorithm, targeted to assess cross-boundary wildfire propagation and community exposure for a large-scale case study area (Macedonia, Greece). We simulated over 300,000 fires, each independently modelled with constant weather conditions from a randomly chosen simulation scenario derived from historical weather data. Simulations generated fire perimeters and raster estimates of annual burn probability and conditional flame length. Results were used to estimate community exposure by intersecting simulated fire perimeters with community polygons. We found potential ignitions can grow large enough to reach communities across 27% of the study area and identified the top-50 most exposed communities and the sources of their exposure. The proposed framework can guide efforts in European regions to prioritize fuel management activities in order to reduce wildfire risk.

Green islands in a sea of fire: the role of fire refugia in the forests of Alberta

Alberta wildfires vary greatly in severity, resulting in a mosaic of burnt, partially burnt, and unburnt forest. These unburnt patches (refugia) within the fire perimeter are critical for the survival of organisms during the fire and the regeneration process. We examined the literature to identify how the fire regimes and landscape features found in Alberta affect the creation and persistence of refugia, the role of refugia for the flora and fauna of Alberta, how climate change is likely to affect refugia, how humans may alter the creation and effectiveness of refugia, and management implications moving forward. Refugia can vary in scale from small areas of unburnt soil or boulders (centimetres to a few metres), to large stands of unburnt trees (many hectares) with different taxa using these refugia across all the spatial scales. Species reliant on habitat connectivity or old growth forest also benefit from refugia as they can use them as stepping-stones between intact habitats or as a lifeboat to recolonize from. The factors influencing what areas remain unburnt are complex and poorly understood but are likely tied to topography, aspect, proximity to waterbodies, weather changes (precipitation and wind direction), time of day during burning, and vegetation type. Areas with the right combination of topography, aspect, and proximity to water have cooler microclimates and higher moisture than the surrounding areas and may remain unburnt throughout multiple fire events, making them persistent refugia. Other areas may remain unburnt by a chance result of weather changes or having the fire pass through at night, making them random refugia. Many of the features that make persistent refugia unlikely to burn (cooler microclimate and higher moisture) will also buffer the effects of climate change. As a result, it is essential we manage the landscape in such a way as to protect areas that act as persistent refugia from industrial activities. In addition, we must restore fire in the landscape to maintain the mosaic of forest caused by mixed-severity fire, especially in the face of climate change, which is projected to increase the severity and frequency of wildfires in Alberta.

Resilience of reptiles to megafires.

Extreme climate events, together with anthropogenic land-use changes, have led to the rise of megafires (i.e., fires at the top of the frequency size distribution) in many world regions. Megafires imply that the center of the burnt area is far from the unburnt; therefore, recolonization may be critical for species with low dispersal abilities such as reptiles. We aimed to evaluate the effect of megafires on a reptile community, exploring to what extent reptile responses are spatially shaped by the distance to the unburnt area. We examined the short-term spatiotemporal response of a Mediterranean reptile community after two megafires (>20,000 ha) that occurred in summer 2012 in eastern Spain. Reptiles were sampled over 4 years after the fire in burnt plots located at different distances from the fire perimeter (edge, middle, and center), and in adjacent unburnt plots. Reptile responses were modeled with fire history, as well as climate and remotely sensed environmental variables. In total, we recorded 522 reptiles from 12 species (11 species in the burnt plots and nine in the unburnt plots). Reptile abundance decreased in burnt compared with unburnt plots. The community composition and species richness did not vary either spatially (unburnt and burnt plots) or temporally (during the 4 years). The persistence of reptiles in the burnt area supported their resilience to megafires. The most common lizard species was Psammodromus algirus; both adults and juveniles were found in all unburnt and burnt plots. This species showed lower abundances in burnt areas compared with the unburnt and a slow short-term abundance recovery. The lizard Psammodromus edwarsianus was much less abundant and showed a tendency to increase its abundance in burnt plots compared with unburnt plots. Within the megafire area, P. algirus and P. edwarsianus abundances correlated with the thermal-moisture environment and vegetation recovery regardless of the distance from the fire edge. These results indicated the absence of a short-term reptile recolonization from the unburnt zone, demonstrating that reptiles are resilient (in situ persistence) to megafires when environmental conditions are favorable.

Black bear spatial responses to the Wallow Wildfire in Arizona

AbstractThe Wallow Fire, the largest wildfire in Arizona history, encompassed 2,170 km2 and provided a rare opportunity to examine habitat selection and home ranges of American black bears (Ursus americanus) before and after a wildfire. We had fitted global positioning system (GPS) collars on 47 bears from 2005 to April 2011, and 10 of these were still collared when the fire started in May 2011. We captured and collared an additional 7 black bears within the fire perimeter post‐fire (Jul–Sep 2011 and Jun 2012). To evaluate how black bears were affected by the fire, we fit a step selection function using a conditional mixed effects Poisson regression model to estimate the relative strength of black bear habitat selection in response to burn severity. Additionally, we estimated home range sizes using an autocorrelated kernel density estimator by means of a continuous‐time movement model. We then used a generalized linear model with a negative binomial error distribution and mixed effects to estimate the effect of the burn severity on black bear home range size, while controlling for sex and drought. In spring and summer in years prior to the fire, bears selected areas that later burned in the fire. After the fire, bears used all burn severities, but their selection for high‐severity burns decreased significantly in summer 2011 and fall 2012. Home range sizes were 3.06 times larger pre‐fire than post‐fire. Our study demonstrates that black bears continued to use all burn severities after a major wildfire, and that post‐fire conditions did not result in expanded black bear home ranges.

Siberian taiga and tundra fire regimes from 2001–2020

Circum-boreal and -tundra systems are crucial carbon pools that are experiencing amplified warming and are at risk of increasing wildfire activity. Changes in wildfire activity have broad implications for vegetation dynamics, underlying permafrost soils, and ultimately, carbon cycling. However, understanding wildfire effects on biophysical processes across eastern Siberian taiga and tundra remains challenging because of the lack of an easily accessible annual fire perimeter database and underestimation of area burned by MODIS satellite imagery. To better understand wildfire dynamics over the last 20 years in this region, we mapped area burned, generated a fire perimeter database, and characterized fire regimes across eight ecozones spanning 7.8 million km2 of eastern Siberian taiga and tundra from ∼61–72.5° N and 100° E–176° W using long-term satellite data from Landsat, processed via Google Earth Engine. We generated composite images for the annual growing season (May–September), which allowed mitigation of missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. The annual dNBR images were converted to binary burned or unburned imagery that was used to vectorize fire perimeters. We mapped 22 091 fires burning 152 million hectares (Mha) over 20 years. Although 2003 was the largest fire year on record, 2020 was an exceptional fire year for four of the northeastern ecozones resulting in substantial increases in fire activity above the Arctic Circle. Increases in fire extent, severity, and frequency with continued climate warming will impact vegetation and permafrost dynamics with increased likelihood of irreversible permafrost thaw that leads to increased carbon release and/or conversion of forest to shrublands.

Fuel-Specific Aggregation of Active Fire Detections for Rapid Mapping of Forest Fire Perimeters in Mexico

Context and Background. Active fires have the potential to provide early estimates of fire perimeters, but there is a lack of information about the best active fire aggregation distances and how they can vary between fuel types, particularly in large areas of study under diverse climatic conditions. Objectives. The current study aimed at analyzing the effect of aggregation distances for mapping fire perimeters from active fires for contrasting fuel types and regions in Mexico. Materials and Methods. Detections of MODIS and VIIRS active fires from the period 2012–2018 were used to obtain perimeters of aggregated active fires (AGAF) at four aggregation distances (750, 1000, 1125, and 1500 m). AGAF perimeters were compared against MODIS MCD64A1 burned area for a total of 24 fuel types and regions covering all the forest area of Mexico. Results/findings. Optimum aggregation distances varied between fuel types and regions, with the longest aggregation distances observed for the most arid regions and fuel types dominated by shrubs and grasslands. Lowest aggregation distances were obtained in the regions and fuel types with the densest forest canopy and more humid climate. Purpose/Novelty. To our best knowledge, this study is the first to analyze the effect of fuel type on the optimum aggregation distance for mapping fire perimeters directly from aggregated active fires. The methodology presented here can be used operationally in Mexico and elsewhere, by accounting for fuel-specific aggregation distances, for improving rapid estimates of fire perimeters. These early fire perimeters could be potentially available in near-real time (at every satellite pass with a 12 h latency) in operational fire monitoring GIS systems to support rapid assessment of fire progression and fire suppression planning.

Modelling fire perimeter formation in the Canadian Rocky Mountains

Wildfires produce a mosaic of burned and unburned patches across varying temporal and spatial scales and provide a range of essential ecosystem services. Fire perimeters mark the separation between the burned and unburned matrix of a fire. Analysis of fire perimeters in the United States, Australia, and Alberta, have identified several key factors that influence the formation of a fire boundary, including fire environment variables, such as fuel, weather, and topographic conditions, as well as anthropogenic factors. We used matched case-control conditional logistic regression to assess the fire environment's influence on the formation of fire boundaries on the 2017 Verdant Creek Fire in the western Canadian Rocky Mountain region. Results indicated that fire boundary formation was strongly influenced by non-fuels, alpine and subalpine vegetation, wetland areas, and low and sparse shrub assemblages generally associated with avalanche paths. Fire cessation was more likely near waterways; however, the fire weather conditions that characterized most burning periods likely overrode other topographic influences. Fire cessation was most likely to occur one day following precipitation events when Vapour Pressure Deficit and Fire Weather Index values decreased. To explore the potential influence of spatial data resolution and fire mapping limitations on our results, we varied the distance between matched sample points selected on each side of the mapped fire perimeter during statistical modelling. Results demonstrated matched pairs of burned and unburned sample points were effective at representing discrete states when separated by at least 100 m and that insufficient or excessive separation between matched pairs confounds results, highlighting the importance of sensitivity analysis for determining the appropriate separation distance to represent burned and unburned states. We demonstrate a practical application of the model to predict spread potential, enabling rapid visual assessment of landscape locations most likely to limit fire spread.

Suppression resources and their influence on containment of forest fires in Victoria

Background Wildfire suppression is becoming more costly and dangerous as the scale and severity of impacts from fires increase under climate change. Aims We aim to identify the key environmental and management variables influencing containment probability for forest fires in Victoria and determine how these change over time. Methods We developed Random Forest models to identify variables driving fire containment within the first 24 h of response. We used a database of ~12 000 incident records collected across Victoria, Australia. Key results Response time, fire size at first attack, number of ground resources deployed (e.g. fire fighters), ignition cause, and environmental factors that influence fire spread (e.g. elevation, humidity, wind, and fuel hazard) were key drivers of suppression success within the first 24 h. However, certainty about the factors influencing suppression reduced as the containment period increased. Conclusions Suppression success hinges on a balance between the environmental factors that drive fire spread and the rapid deployment of sufficient resources to limit fire perimeter growth. Implications Decreasing the period between an ignition and the time of arrival at the fire will allow first responders to begin suppression before the fire size has exceeded their capability to construct a control line.

Remotely Sensed Fine-Fuel Changes from Wildfire and Prescribed Fire in a Semi-Arid Grassland

The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed burns and mitigating wildfire, although methods to produce high-resolution fuel estimates are still under development. To further understand how prescribed fire and wildfire influence fine-fuels in a semi-arid grassland invaded by non-native perennial grasses, we combined high-resolution Sentinel-2A imagery with in situ vegetation data and machine learning to estimate yearly fine-fuel loads from 2015 to 2020. The resulting model of fine-fuel corresponded to field-based validation measurements taken in the first (R2 = 0.52, RMSE = 218 kg/ha) and last year (R2 = 0.63, RMSE = 196 kg/ha) of this 6-year study. Serial prediction of the fine-fuel model allowed for an assessment of the effect of prescribed fire (average reduction of −80 kg/ha 1-year post fire) and wildfire (−260 kg/ha 1-year post fire) on fuel conditions. Post-fire fine-fuel loads were significantly lower than in unburned control areas sampled just outside fire perimeters from 2015 to 2020 across all fires (t = 1.67, p < 0.0001); however, fine-fuel recovery occurred within 3–5 years, depending upon burn and climate conditions. When coupled with detailed fuels data from field measurements, Sentinel-2A imagery provided a means for evaluating grassland fine-fuels at yearly time steps and shows high potential for extended monitoring of dryland fuels. Our approach provides land managers with a systematic analysis of the effects of fire management treatments on fine-fuel conditions and provides an accurate, updateable, and expandable solution for mapping fine-fuels over yearly time steps across drylands throughout the world.

Are fire refugia less predictable due to climate change?

Fire refugia—unburnt habitat within a wildfire’s perimeter—play a key role in wildlife persistence and recovery. While studies have shown that the location of refugia is influenced by local topographic factors, growing evidence points to extreme fire weather becoming the dominant factor driving high-severity wildfires that result in the location of fire refugia being less predictable. Between September 2019 and February 2020, a series of mega-fires in eastern Australia burned largely in broadleaf forest. We assessed burned and unburned areas of forest in eastern Australia using Sentinel-2 satellite data, aggregated monthly over the fire season to calculate a fire severity layer at a 20 m pixel resolution. We found that fires burned 5.7 × 106 ha−1 of forest and woodland. The total percentage area of unburned forest within the wildfire footprint was approximately 10%. The majority (94%) of the unburnt forest and woodland patches within the fire perimeter occurred as patches <1 ha (n = 842 622 and 111 707 ha) with far fewer large unburnt patches (>100 ha) (n = 575 and 286 080 ha). Boosted regression tree analyses of the relationships between fire severity and potential explanatory variables revealed that 63%–78% of the variable importance in the models were climatic and weather-related factors. Fire weather index was the single most important variable for analyses, accounting for 40%–52% of modelled results. Our results reinforce mounting evidence that a shift is underway in the balance between deterministic and contingent factors in the occurrence of fire refugia with local topographic controls being increasingly overridden by severe fire weather conditions, and declining topographic effects as fire severity increases. Further studies are needed over a longer time frame, inclusive of prior forest management impacts, to confirm that the ability to predict fire refugia is permanently declining.

Conditional natal dispersal provides a mechanism for populations tracking resource pulses after fire

AbstractAnimals that persist in spatially structured populations face the challenge of tracking the rise and fall of resources across space and time. To combat these challenges, theory predicts that species should use conditional dispersal strategies that allow them to emigrate from patches with declining resources and colonize new resource patches as they appear. We studied natal dispersal movements in the black-backed woodpecker (Picoides arcticus), a species known for its strong association with recent post-fire forests in western North America. We radio-tracked juveniles originating from seven burned areas and tested hypotheses that environmental and individual factors influence dispersal distance and emigration rates—investigating emigration while additionally accounting for imperfect detection with a novel Bayesian model. We found that juveniles were more likely to leave natal areas and disperse longer distances if they were heavier or hatched in older burned areas where resources are increasingly scarce. Juveniles were also more likely to leave their natal burn if they hatched in a nest closer to the fire perimeter. While dispersing across the landscape, black-backed woodpeckers selected for burned forest relative to unburned available habitat. Together, these results strongly support the hypothesis that black-backed woodpecker populations track resource pulses across fire-prone landscapes, with conditional natal dispersal acting as a mechanism for locating and colonizing newly burned areas. Lending empirical support to theoretical predictions, our findings suggest that changes in resource distribution may shape dispersal patterns and, consequently, the distribution and persistence of spatially structured populations.