International Journal of Wildland Fire

@ijwildlandfire

Journal of the International Association of Wildland Fire Publishing fundamental & applied fire research, including fire modelling, management, ecological & societal impacts.

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International Journal of Wildland Fire@ijwildlandfire· 1d

🔥 New in IJWF: Bennett et al. assess W.I.S.E. fire-spread predictions against 19,848 satellite-derived daily wildfire perimeters across Canada. Default simulations substantially overpredicted spread, while optimizing burn duration and wind direction. 🔗 doi.org/10.1071/WF26... #IJWildlandFire

Simulation of one wildfire burn day for three wildfires showing the true boundary from the Canadian Fire Spread Dataset (CFSDS) and simulated perimeters from scenarios 1, 2 and 3.

Distributions of normalized area difference and F1 score for scenarios 1, 2 and 3. Normalized area difference values near 0 indicate high levels of agreement, while positive values indicate overprediction and negative values indicate underprediction.

Tornado chart depicting the sensitivity of Wildfire Intelligence and Simulation Engine (W.I.S.E.) performance to input variables and fire characteristics in scenario 1. Annotations at either side of each bar provide ranges for bins associated with the worst (left) and best (right) average F1 score. BUI, Build Up Index; DMC, Duff Moisture Code; FFMC, Fine Fuel Moisture Code; FWI, Fire Weather Index; DC, Drought Code; ISI, Initial Spread Index.

International Journal of Wildland Fire@ijwildlandfire· 5d

🔥New in IJWF: Price et al. show that 94% of houses destroyed in the 2019–20 NSW fires were within 100 m of flammable vegetation, supporting current bushfire risk mapping—although extreme fire weather pushed house losses further into surrounding landscapes. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

Map of New South Wales showing Bush Fire Prone Land (BFPL) with the footprint of the 2019–20 bushfires overlaid. The locations of the 2475 destroyed houses is indicated.

Flowchart showing how each of the two building datasets (Building Impact Assessment (BIA) and Microsoft Building Footprints (MBF)) were subsetted and used in the four analyses.

Two examples of buildings destroyed more than 400 m from flammable vegetation. (a) The destroyed buildings most distant from flammable vegetation. This area is 20 km west of Rappville, New South Wales (NSW) (maximum 1464 m from flammable vegetation), burnt by the Busbys Flat Rd fire, 8 October 2019 at Forest Fire Danger Index (FFDI) 16. The smaller red dots are destroyed outbuildings and the larger dot is a destroyed house (840 m from flammable vegetation). These buildings were burnt on 8 October 2019 by the Busby’s Flat Rd fire. The overlays are vegetation categories. (b) Cobargo, 488 m from flammable vegetation, burnt by the Badja fire, 31 December 2019 under FFDI 73. The eucalypt (Euc.)-Forest category is the Bush Fire Prone Land (BFPL) category one and the non Euc.-Forest category are the BFPL categories 2 and 3 combined. (c) Shows the location of the two communities and the two fires in NSW.

International Journal of Wildland Fire@ijwildlandfire· 11d

🔥 New in IJWF: Ren et al. present a high-accuracy workflow for georeferencing drone thermal imagery during a fast-moving prescribed canyon fire. Using fire anatomy, terrain-aware transformation and strategic tie point. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

Synchronous Red, Green and Blue (RGB) and Thermal Infrared (TIR) keyframes after 2 min 00 s since the TIR data collection started. The RGB image (a) is covered by heavy smoke, while the TIR image (b) shows clear temperature diversity between pixels.

Georeferenced Thermal Infrared Thermal (TIR) Keyframe, showing tie points in the back burnt area and the right flank, other than in the intensive head fire (at a cumulative TIR video time of 34 min 55 s).

Thermal Infrared (TIR) images of oak trees during the head fire of the same area at different time intervals, from 3 min 00 s (3’00’) to 6 min 00 s (6’00’) since the TIR data collection started. Labels a, b, c and d indicate individual oak trees. Note: RGB = Red, Green and Blue.

International Journal of Wildland Fire@ijwildlandfire· 21d

🔥 New in IJWF: Girardin et al. show that rising atmospheric dryness coordinates boreal forest change: higher VPD suppresses tree growth, increases annual area burned, and indirectly reduces biomass accumulation through linked growth and fire pathways. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

Fig. 1. (a) Geographic depiction of the study area located within the forested regions of the Core Study Domain of NASA’s Arctic–Boreal Vulnerability Experiment (ABoVE) in northwestern boreal Canada colored in blue. Plot locations of Canada’s National Forest Inventory tree samples used for this study are indicated by white circles. Fire polygons from the Canadian National Fire Database (CNFDB) are orange colored. (b) ABoVE’s aboveground biomass estimates for 1984 (grid cells of 30 m resolution). (c) ABoVE’s average annual aboveground biomass increment (AGBI) estimates from 1985–2014 for each of the 79 grid tiles, each measuring 180 × 180 km.

Fig. 2. Time series of (a) spring and summer mean daylight vapor pressure deficit (VPD, 1951–2022); (b) annual mean basal area increment (BAI) change (1950–2009); (c) annual sums of area burned (AAB, 1950–2020); and (d) spatially averaged Landsat-derived annual aboveground biomass increments (AGBI, 1985–2014) in northwestern boreal Canada within the NASA Arctic–Boreal Vulnerability Experiment (ABoVE) Core Study Domain. Shaded areas represent 95% bootstrap confidence intervals, reflecting spatial uncertainty. For BAI, the period after 2009 was excluded due to inflated confidence intervals resulting from low sample size (n < 80 trees).

Fig. 4. Structural equation model linking climate drivers to boreal forest productivity. This diagram illustrates the hypothesized causal relationships among vapor pressure deficit (VPD), basal area increment (BAI), annual aboveground biomass increment (AGBI) and annual area burned (AAB) in boreal forests. Arrows represent fully standardized path coefficients (with values indicated) estimated using structural equation modeling (SEM). Red solid arrows indicate statistically significant relationships (P < 0.05), while gray dashed arrows denote non-significant paths.

International Journal of Wildland Fire@ijwildlandfire· 21d

🔥 New in IJWF: Yang et al. show that accounting for uncertainty in forecast fuel moisture improves 1–4 day spatially explicit forecasts of human-caused wildfires across Ontario, reducing underprediction and producing better-calibrated prediction intervals. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

Color-coded maps for the predicted fire occurrence probabilities and the observed fire incidents (blue dots) for 11 June 2018 using two-days ahead forecasted Fine Fuel Moisture Code (FFMC) values. The corresponding 95% prediction intervals for the total number of fires were [4, 16] for SIMEX and [0, 8] for naïve. Seven human-caused fires occurred.

International Journal of Wildland Fire@ijwildlandfire· Jul 13

Lampman et al. combine drone thermal imagery and AI to quantify fire behaviour and accurately predict short-term fire spread during a prescribed grassland burn. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

Fire progression intervals represented as Fire Radiative Power (FRPD; fine-scale colored pixels along each progression line 1–7) across the burn unit with rate of spread vectors (ROS; colored lines between Intervals 1–6) from right to left and for the multi-layer perceptron (MLP) predictions (beyond Interval 6). The wind rose indicates the direction the wind was coming from. The ROS rose is for the observed intervals.

Visual workflow for deriving rate of spread, fireline intensity and fire radiative power using UAS-based TIR images of spreading wildland fire. (a) Individual TIR images to orthomosaics using Structure from Motion (SfM); orthomosaics to Fire Radiative Power Density (FRPD) fire front interval rasters using temperature threshold and Stefan–Boltzmann law. (b) Fire front interval rasters converted to polygons and then minimally smoothed with points generated along the edges. (c) Flaming front depth (FD) vectors generated for points within fire front intervals to derive fireline intensity (FI) using the sum of Fire Radiative Power Density (FRPD) pixels along the FD vectors multiplied by the FD length (FRPD-FD method); rate of spread (ROS) vectors generated for points between fire front interval leading edges to measure spread distance and time difference between fire front interval (a) and fire front interval (b). PAEK, Polynomial Approximation with Exponential Kernel.

International Journal of Wildland Fire@ijwildlandfire· Jul 2

Douglas Radford ( bsky.app/profile/doug...) for "Optimising fuel treatment plans to reduce burn probability", using simulation-optimisation to boost burn probability reduction by 81–284% over existing approaches. 🔗 doi.org/10.1071/WF25080

International Journal of Wildland Fire@ijwildlandfire· Jul 2

Kendra Fallon for "A novel methodology to assess fuel treatment effectiveness: application to California's forests", 61% of treated areas modified fire behavior, with wildland fire/fuel removal treatments most effective. 🔗 doi.org/10.1071/WF24220

International Journal of Wildland Fire@ijwildlandfire· Jun 18

🔥New in IJWF: Theodori et al. link reconstructed flame and ember exposure from the 2017 Tubbs Fire with structure-level damage data, showing how WUI codes, spacing and vegetation clearance shape destruction probability. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

Modeled hazard metrics from the 2017 Tubbs Fire and observed structure outcomes. (Left) Maximum flame length (m) and (right) cumulative ember count, both derived from the ELMFIRE reconstruction over the first 12 h post-ignition at 30 m resolution. Observed structures are overlaid, with green points indicating not destroyed and magenta points indicating destroyed buildings. The final CAL FIRE perimeter (red outline, mapped 31 October 2017) is shown for context. These hazard outputs provide spatially explicit exposure metrics used to develop structure fragility functions.

Location of structures affected by the 2017 Tubbs Fire in relation to California Fire Hazard Severity Zones (FHSZ). The maps distinguish between (left) structures not subject to the California Building Code (CBC) Chapter 7A and (right) structures subject to the code (built post-2008 in an FHSZ). Points indicate structure outcomes: green, not destroyed; magenta, destroyed, with 46.5% destroyed (left) and 47.7% destroyed (right). Shaded areas denote FHSZ categories (very high, high, moderate), and the red outline marks the final Tubbs Fire perimeter.

International Journal of Wildland Fire@ijwildlandfire· Jun 10

🔥New in IJWF: Johnson et al. surveyed Carolina residents and found support for prescribed burning, with prescribed fire smoke perceived as less concerning than wildfire smoke. The study highlights why smoke-risk communication matters as prescribed fire use expands 🔗 10.1071/WF25193 #IJWildlandFire

(a) Survey responses and locations of state parks conducting prescribed burning. Responses grouped by North Carolina ZIP code. Responses from ZIP codes intersecting a 20 km radius around parks are classified as ‘20 km’ and those within a 20 km radius of a park denoted by a star are classified as ‘High Burn.’ (b) Wildland–Urban Interface (WUI) classification resampled to the North Carolina ZIP code level. WUI areas include ‘intermix’ and ‘interface’ designations. Urban areas include ‘high’ and ‘medium housing density non-vegetated’ designations. Low population density vegetated (LDV) areas include ‘very low density, vegetated’ and ‘uninhabited vegetated’ designations. (c) Total Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m Active Fire and Thermal Anomalies product detections summarized by North Carolina ZIP code for 2015–2019. (d) Average daily smoke PM2.5 concentrations for 2015–2019, as predicted by the National Oceanic and Atmospheric Administration (NOAA) operational smoke forecast. Concentrations are resampled to the North Carolina ZIP code level.

Acceptability of smoke from different sources among survey respondents and respondent categories. Percentage of respondents that selected either ‘acceptable’ or ‘somewhat acceptable’ is listed next to each color bar. Shading indicates respondent category types. Differences in responses significant at α ≤ 0.05 by A NC State Parks burning activity (20 km and High Burn) and land type (Wildland–Urban Interface (WUI), low population density in vegetated areas (LDV), Urban); B land type; and C NC State Parks burning activity, land type and fire detects (High and Low Detects).

Actions respondents are willing to take to reduce exposure to smoke among respondent categories. Participants could choose more than one response. Colored rings indicate the percentage of the respondent group that chose a statement and ring colors correspond to different statements. Gray rings show reference percentages. Shading indicates respondent category types. Note: WUI = Wildland–Urban Interface, LDV = low population density in vegetated areas, AQ = air quality, IAQ = indoor air quality.

International Journal of Wildland Fire@ijwildlandfire· Jun 7

🔥New in IJWF: Cerovski-Darriau et al. tracked post-fire soil infiltration across northern California fires, finding chaparral soils showed increased hydraulic conductivity after burning, while most vegetation–lithology units recovered to pre-fire conditions. 🔗 10.1071/WF25141 #IJWildlandFire

Fig. 1. Location of 41 field sites that spanned four fires and four simplified lithology types (granitic (i.e. intrusive), volcanic, serpentinite (i.e. peridotite and serpentinite) and sedimentary (i.e. mélange, clastic and meta-clastic sedimentary). The infiltration measurement sites are shown as gray (burned) and white (unburned) semi-circles. Rainfall measurements (Fig. 8) were collected at a subset of the infiltration sites, as indicated by the blue triangles. The rock types are indicated by color, adapted from the US Geological Survey (USGS) State Geologic Map Compilation (SGMC) (Horton et al. 2017), with the continuous extent shown on the statewide inset. The burn perimeters are outlined (black) and labeled by fire abbreviation (LNU, 2020 LNU; WAL, 2020 Walbridge; GLA, 2020 Glass; DIX, 2021 Dixie) (CAL FIRE 2024). All the sites are in northern California (USA), as shown in the inset location map. Location (a) shows the northern California Bay Area sites in predominately sedimentary rocks with some volcanic and serpentinite sites. Location (b) shows the Dixie fire sites in the northeastern Sierra Nevadas, which are mostly granitic with some volcanic and serpentinite sites. The base map is 90-m shaded relief from the Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM).

Fig. 3. Hydraulic conductivity (Kfs) (mm h−1) for (a) mini disk (MD), and (b) bottomless bucket (BB) measurements in the months after fire separated by lithology. The individual measurements are overlain by boxplots. The boxplots show the interquartile range (25th–75th percentile) of the data with a horizontal line at the median. The boxplots are binned by post-fire number of months with paired burned (orange) and unburned (green) sites grouped with median values denoted with horizontal lines. The number of samples is shown in the corresponding color. Statistically significant differences between burned and unburned groups are shown by *P < 0.05, **P < 0.01, or ***P < 0.001. Note: low sample sizes will decrease statistical power.

Fig. 5. Boxplots of hydraulic conductivity (Kfs) (mm h−1) for (a) mini disk (MD), and (b) bottomless bucket (BB) by hydrologic response units (HRUs). The boxplots show the interquartile range (25th–75th percentile) of the data with a horizontal line at the median. The HRUs group lithology and vegetation classes with boxplots showing burned (orange) and unburned (green) site variability and median values over time. The number of samples is shown in the corresponding color. Statistically significant differences between burned and unburned groups are shown by *P < 0.05, **P < 0.01, or ***P < 0.001. The variability in the early months post-fire is shown with a large spread in the data, including large outliers for the granitic–chaparral (1) sites, which generally decreases through time. BB values are typically order(s) of magnitude higher. Note: not all burned HRUs have paired unburned sites; low sample sizes will decrease statistical power.

International Journal of Wildland Fire@ijwildlandfire· May 31

🔥New in IJWF: Ng et al. show that early dry-season burning supports greater long-term woody carbon accumulation than late dry-season fires across northern Australian savanna fire trials, while site conditions outweigh fire frequency effects. 🔗 doi.org/10.1071/WF26... #IJWildlandFire

International Journal of Wildland Fire@ijwildlandfire· May 29

🔥New in IJWF: White argues that wildfire personnel health is core fire management infrastructure, proposing a readiness–response–recovery framework to better support workforce safety, performance and long-term sustainability. 🔗 doi.org/10.1071/WF26... #IJWildlandFire

Conceptual readiness–response–recovery cycle for treating wildfire personnel health as a managed component of fire operations.

International Journal of Wildland Fire@ijwildlandfire· May 21

🔥New in IJWF: Provencher et al. show that spatial simulations can change vegetation reference conditions, especially in small or long-fire-interval systems, with implications for fuels management. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

International Journal of Wildland Fire@ijwildlandfire· Apr 20

🔥New in IJWF: Zhao et al. evaluate two common tools for estimating fine dead fuel moisture. The Wiltronics meter performed best at low FMC, while the 10-h fuel stick often underestimated moisture and responded more slowly to environmental change. 🔗https://doi.org/10.1071/WF25174 #IJWildlandFire

International Journal of Wildland Fire@ijwildlandfire· Apr 17

Congratulations to Dr Xinyan Huang on receiving the 2025 Outstanding Associate Editor Award from the IJWF. An Associate Editor since 2021, Dr Huang has made outstanding contributions to wildfire research and rigorous peer review. A well-deserved honour. 🔗 doi.org/10.1071/WFv3... #IJWildlandFire

International Journal of Wildland Fire@ijwildlandfire· Apr 11

🔥New in IJWF: Schenk et al. validate WEPPcloud after Arizona’s 2022 Pipeline Fire, showing the model can reasonably estimate post-fire sediment and ash delivery to the WUI, while spatial erosion predictions still need improvement. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

International Journal of Wildland Fire@ijwildlandfire· Apr 2

🔥New in IJWF: Chakraborty et al. use bivariate LISA to map where wildfire risk and social vulnerability overlap across the continental US, showing priority hotspots mainly in western and southern states. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

International Journal of Wildland Fire@ijwildlandfire· Mar 24

🔥New in IJWF: Ondei et al. show that airborne LiDAR can reliably capture vegetation cover and spatial arrangement in gardens at the wildland–urban interface, supporting scalable wildfire hazard assessments of defensible space, though hazards still require checks. 🔗 10.1071/WF25218 #IJWildlandFire

Fig. 1. Location of the gardens assessed in this study showing (a) distribution of dry and wet eucalypt forest and
woodland (data from TASVEG 4.0; Department of Natural Resources and Environment Tasmania 2020), and (b)
distribution of the wildland–urban interface (WUI) in the area (data from Chen et al. 2024). To avoid disclosing
the exact location of the gardens, the outline of the statistical areas in which they are located, and the number if
gardens for each statistical area, are shown. The inset shows the location of the study area within Australia.

Fig. 6. Comparison between hazard scores obtained using a combination of field and high point density Light Detection
and Ranging (LiDAR) (Combined HD Hazard) or field and low point density LiDAR (Combined LD LiDAR). Results are shown
for the (a) overall garden, as well as the (b) Fuel-free Zone (FFZ), (c) Open Zone (OZ), and (d) Tree Zone (TZ).

International Journal of Wildland Fire@ijwildlandfire· Mar 17

🔥New in IJWF: Ellis et al. show that prescribed burns are most likely to limit later wildfire spread when burns are larger, more recent, and linked with roads or active control lines, while extreme fire weather reduces their effectiveness. 🔗 doi.org/10.1071/WF25... #IJWildlandFire

Fig. 1. (a) Distribution of treatments (1999–2019) highlighting the treatment time since fire in years for all encounters with wildfires.
(b) Spatial distribution of treatments that encountered wildfires across the state of Victoria, Australia, with fill colour representing the
equivalent treatment time since fire in (a). (c) Distribution of treatment–wildfire encounters by wildfire season. (d) Spatial distribution of relevant wildfires (2003–2020) across the state of Victoria, Australia, with fill colour representing the equivalent wildfire season in (c). Hatching in (a, c) represents the subset of case study encounters.

Fig. 2. Example of our method to calculate the through-burn percentage of a treatment as a measure of its effectiveness. (a) A treatment–wildfire encounter, with a 50 m buffer around the treatment perimeter. (b) The 50 m buffer is segmented into 1 m lengths (vertices), which are intersected with the relevant wildfire. Wildfire encounters are scored as 1, no wildfire as 0. (c) For each vertex, we calculate the percentage of burnt vertices in the half-perimeter centred on that vertex using a smoothing operation. (d) The through-burn percentage for the treatment is defined as the minimum of these values, which occurs in the central vertex of the half-perimeter furthest from the wildfire. Note that one or more vertices can share this value (in this example, the circled vertices all share the value of 28%). See Supplemental material for the code for this example encounter.

Fig. 5. Shapley value distributions for a subset of predictors in the Victorian fire history and case study record models. Distributions
were averaged for each observation across 25 permutations. Negative values reflect lower likelihood of predicted through-burn for the given predictor value, while positive values reflect a higher likelihood of predicted through-burn. Bars represent the mean values for each variable across all observations, while points represent the median. The range represents 50 and 89% highest density continuous intervals across all observations’ mean Shapley values. Selected predictors include (a) control line presence, (b) road presence, (c) the interaction between treatment time since fire and three key dominant fuel types, (d) landscape time since fire, (e) total treatment area, (f) the Forest Fire Danger Index, (g) the Keetch–Byram Drought Index, and (h) the total daily precipitation. Note that panels (f) through (h) are only for the case study records model.

Fig. 6. Relationship between the through-burn probability and the fire return interval in (a) the adjusted predictions when controlling for
the effects of all predictors for both the Victorian fire history and case study GAMs, and predicted outcomes over the full range of
treatment time since fire values using (b) worst- and (c) best-case scenario parameters for both the Victorian fire history and case study
records models. Note that the dominant fuel type is set to the most representative forest type, forest with shrub, to ensure
the scenarios reflect comparable vegetation classes. (d, e) show the distribution of permuted (B = 200) Shapley values for the worst- and
best-case scenarios, respectively. Bars represent the mean Shapley value for each predictor, while points represent the median. Range
lines represent 50 and 89% highest density continuous intervals for each predictor distribution.

International Journal of Wildland Fire@ijwildlandfire· Mar 9

🔥 New in IJWF: Baker et al. assess soil heating beneath prescribed burns by UK teams trained to first vegetation fire standards, showing minimal heating at 2–3 cm depth and little evidence of below-ground organic matter loss under mild burning conditions. 🔗 doi.org/10.1071/WF25224 #IJWildlandFire

Fig. 1. Map illustrating the site locations of the management burns
tested, and photographs of the sites in this study. 1. The Cawdor
Estate, Scotland (photographs a – 23 March 2023 burn; b – Fire 1;
c – Fire 2; d – Fire 3); 2. Spaunton Moor, England (photographs a – Fire 1; b – Fire 2); 3. Rempstone Forest, England; 4. Corfe Common, England.

Fig. 2. Lighting patterns and wind direction for each of the prescribed burns monitored. Measurements represent the area covered by thermocouples.

Fig. 3. Maximum soil temperatures recorded from thermocouples during each of the burns. Orange arrows depict the lighting pattern.

Fig. 4. Heat maps demonstrating maximum temperatures reached at the surface during each of the
prescribed burns monitored.

International Journal of Wildland Fire@ijwildlandfire· Feb 15

🔥New in IJWF: Chen et al. use CSIRO Pyrotron wind-tunnel burns in eucalypt litter to model fine woody debris consumption (6–50 mm). A two-step ML pipeline predicts full vs partial burnout (74% accuracy) and estimates combustion factor. 🔗 doi.org/10.1071/WF25255 #IJWildlandFire

Fig. 1. (a) Diagram of the dimensions and division of the fuel bed; (b) picture of the fuel bed before fire (under a FWD load of
1.2 kg m−2); and schematic of (c) heading, and (d) backing fire spread modes, where the black rectangles indicate the area of the fuel bed.

Fig. 4. Box plot with overlaid data distribution of the fire behaviour variables and FWD combustion factor in the dataset:
(a) ignition delay and residence time, (b) duration of flaming and smouldering combustion, (c) interval and cumulative flame
rate of spread, (d) interval and cumulative fireline intensity, (e) charring intensity, and (f) FWD combustion factor. In panels,
the number next to the hollow square in the box plot represents the mean and the other quantity presented is the median.

International Journal of Wildland Fire@ijwildlandfire· Feb 5

🔥New in IJWF: Rhea et al. examine the 2002 Hayman Fire (Colorado) and show stream nitrate export can stay ~19× higher even 17 years later—driven by reduced vegetation N demand and subsurface transport, not higher mineralization. 🔗 doi.org/10.1071/WF25145 #IJWildlandFire

(a) Sampling locations in watersheds
within and adjacent to the 2002 Hayman fire,
Colorado, USA. Triangles mark upland sites
(n = 20). Circles mark near-stream networks,
each of which contained six sites (i.e. riparian,
toeslope and midslope on both stream banks),
shown here as single points due to their close
proximity. Burned sites are orange and unburned
sites are blue. The Cheesman weather station is
denoted by a black square ( WRCC 2021). (b)
Conceptual diagram of the four topographic
positions along a hillslope used to structure
site placement and represent landscape transitions from riparian to upland environments.
USFS, United States Forest Service.

Ion exchange resin (IER) (a) nitrate (NO3 ) and (b) ammonium (NH4
+) accumulation rates separated by summer monsoon and spring snowmelt seasons. This represents plant-available inorganic nitrogen (N) in the top 5 cm of mineral soil. Upslope positions only have winter data. The centerline of the boxplots denotes the median values, the upper and lower limits span the interquartile range, the whiskers include data within 1.5 times the interquartile range and the dots beyond the whiskers are outliers. Fire effect significance is
denoted by *P < 0.1 and **P < 0.05.

Annual terrestrial net primary productivity (NPP) (kg C/ha.year) by functional type for (a) unburned and (b) burned sites. The upper panels represent near-stream sites and lower panels upland sites. NPP is partitioned by plant functional type which is illustrated with color shading. The vertical black line represents the year of the 2002 Hayman Fire.

International Journal of Wildland Fire@ijwildlandfire· Feb 5

🔥New in IJWF: Finney et al. show wind-driven flame spread along single horizontal fuel particles follows an elliptical relationship with orientation—supporting why wildfire perimeters often approximate ellipses, even at particle scale. 🔗 doi.org/10.1071/WF25135 #IJWildlandFire

Experimental results from quadruple 3.175 mm fuel samples showing: (a) flame spread data and polar ellipse model ( Eqn 2,
Table 3) for each windspeed and wind direction (α); and (b) data and elliptical shapes of fires from the fitted model in Cartesian
coordinates. Note that data are displayed offset from actual α to distinguish different wind speeds.

Composite of photographs showing flame angle from vertical as a function of wind speeds for: (a) single 3.175 mm cardboard
fuel samples, and (b) quadruple 3.175 mm cardboard fuel samples, and examples of parallelogram analysis used for determining flame
angle from vertical for different wind speeds (c) 0.23 ms−1, (d) 0.36 ms−1, and (e) 0.62 m s−1 (aligned with spread direction).

International Journal of Wildland Fire@ijwildlandfire· Jan 23

🔥New in IJWF: Edwards et al. present a biome-scale fire severity mapping framework for Australia’s tropical savannas using MODIS data and 6478 field sites, achieving 93% accuracy. A major step for biodiversity, emissions, and Indigenous-led fire programs. 🔗 doi.org/10.1071/WF25044 #IJWildlandFire

Fig. 1. Workflow for biome-scale fire severity
mapping framework in northern Australia. The
process integrates MODIS surface reflectance,
MODIS-derived burnt area mapping, active fire
data, field data and classification and validation
steps to generate annual fire severity maps and a
confusion matrix.

Fig. 2. North Australia depicted as four main zones, from north to south: the High Rainfall, Low Rainfall, Southern Savannas and
Australian Mainland. Also delineated are the three north Australian pyro-geographic regions defining the Early and Late Dry Season
thresholds: (R1) Kimberley/Top End West (1 January to 30 June); (R2) Top End East/West Queensland (to 30 July); and (R3) Northeast
Queensland (to 31 August) delineated by the lines of longitude at 132°E and 142°E, respectively. The validation waypoints collected
from 2011 to 2016 are included.

Fig. 4. Scatterplots illustrating the Fire Severity class values of post-fire NIR vs RdNIR (relativised difference
of pre and post NIR) derived from the supervised Random Forest classification in the Early Dry Season (EDS)
on the left, and the Late Dry Season (LDS) on the right, 2016. Although there appears to be considerable
overlap, there are distinct individual values of Severe and Mild reflectance supported by the validation.

International Journal of Wildland Fire@ijwildlandfire· Jan 19

🔥 Call for Papers: Wildland–Urban Interface Fires 🌳🏠 Submission deadline extended to 1 March 2026. We welcome research on fire dynamics, risk modelling, exposure & impacts, mitigation, recovery, and community resilience. 🔗: connectsci.au/wf/pages/cal... #FireScience #WUI #IJWildlandFire

International Journal of Wildland Fire@ijwildlandfire· Jan 15

🔥New in IJWF: Little et al. reveal that persistent high-pressure systems drive spring vegetation fires in England, while summer fires are less weather-dependent. Forecasting fire risk? Look to both surface and synoptic signals. 🔗 doi.org/10.1071/WF25158 #IJWildlandFire

Graphic showing the three layers of
relationships investigated in this paper, from
top to bottom: synoptic weather patterns –
surface fire weather – vegetation fires. Mean
sea level pressure (MSLP) anomalies and
Canadian Fire Weather Index (FWI) values are
for June 26, 2018, the day the Saddleworth Moor
Fire in England was declared a major incident,
which remains one of the largest fires experienced in the UK at 18 km2 ( Graham et al. 2020).
The vegetation fire layer shows all spring (blue)
and summer (orange) vegetation fires >1 ha
recorded between April 2009 and April 2020.

Summary of fire data from the incident recording system (IRS) database. (a) Total number of fires (orange line) and total
burned area (hectares; pale orange bars) of crop, grassland and heathland/moorland vegetation fires in England, 2009–2020. (b) Total
number of fires across the entire data period (2009–2020) in each land cover category during spring (March–May) and summer
(June–August). (c) Total number of fires across the entire data period (2009–2020), on each day of the year. Dashed line represents
the division between spring and summer (31st May).

Ranked percentile curve score (intercept of Theil–Sen regression ± 95% confidence interval) for surface weather metrics on fire
days. Weather variables obtained from E-OBS (‘raw’ weather metrics) are MnT = mean daily temperature; MxT = maximum daily
temperature; GR = global radiation; RH = relative humidity. Weather indices obtained from the Canadian Fire Weather Index System
(CFWIS) are FWI = fire weather index; FFMC = fine fuel moisture code; DMC = duff moisture code; DC = drought code; ISI = initial spread
index; BUI = build-up index. Higher values indicate that the variable performs better at predicting fire occurrence.

International Journal of Wildland Fire@ijwildlandfire· Dec 27

🔥New in IJWF: Accary et al. argue that fire-induced wind—often ignored in models—plays a key role in extreme fire behaviour. They call for new experiments and simulations to quantify its feedback and improve predictions. 🔗 doi.org/10.1071/WF25258 #IJWildlandFire

Fig. 1. Characteristic velocities associated to the forces governing
wildfire behaviour on a sloping terrain: wind inertia (parallel to the
ground), buoyancy (vertical) having two components that are parallel
and normal to the direction of fire propagation. Ue: effective wind
speed, Uw: prevailing wind speed, UB: buoyancy characteristicvelocity.

International Journal of Wildland Fire@ijwildlandfire· Dec 18

🔥New in IJWF: Cardil et al. present an Initial Attack Assessment (IAA, 1–5) to flag fires likely to escape initial suppression. Analysis of 26,907 California ignitions shows higher IAA(especially terrain and fire-behavior)lead lower initial-attack success. 🔗 doi.org/10.1071/WF24160 #IJWildlandFire

Initial Attack Assessment index (IAA) for Californian fires retrieved by IRWIN from 2020 to 2023
(n = 26,907) considering the initial attack success (left: success (fire size <4 ha); right: fail (fire size >4 ha).
Wildfires were independently simulated using WFA to obtain their corresponding IAA. The actual reported fire
size is represented by graduated circles.

Schematic modeling process that includes the three main research objectives related with the
wildfire data subsets employed and their corresponding univariate logistic regression models.

(a) Fire simulations (n = 360) were automatically conducted for the period of 6–9 January
2025. The colored dots represent the IAA category assigned to each simulation. (b) The number of
incidents per IAA level, along with the IA success rate (%), and the names of escaped (>4 ha) and large
wildfires are shown across IAA classes (1–5). Fire names are followed by their respective burned area in
hectares.

International Journal of Wildland Fire@ijwildlandfire· Dec 14

🔥 New in IJWF: Ren et al. model future wildfire susceptibility in Portugal with climate and vegetation change. Results show climate-only projections miss local shifts, while climate-driven eucalyptus expansion can increase risk even under low-emission. 🔗 doi.org/10.1071/WF25092 #IJWildlandFire