International Journal of Wildland Fire

(a) Post-treatment forest structure and two people sampling fuels from the 0 to 1.7 m and 2.8 to 4.5 m plots within the clustered design. (b) A plot placed between two whiskers to mark the distance intervals and the strings stretched across the PVC sampling frame to denote the randomly generated 20 × 20 cm subplot for collecting the plot’s observation. Black markings on the PVC sampling frame are at 20 cm intervals to create the grid of 36 subplots. (c) Two people collecting a dead fine fuel sample into a pre-labeled and pre-weighed polyethylene resealable bag.

The observation period was organized into three phases: early summer (Julian day 138–189), mid-summer (Julian day 192–236) and late summer (Julian day 243–287) for the following plots. The dashed lines on the axes depicting FMC indicate the commonly used 30% moisture of extinction threshold for dead understory forest fuels ( Rothermel et al. 1986). (a) Empirical cumulative density functions of the three distributions (early summer, mid-summer, late summer) of observed fuel moisture content. (b) Kernel density plots of the three intraseasonal period distributions. (c) Boxplots colored by intraseasonal period depicting the distribution of FMC on each observation day. The box indicates the inter-quartile range (25th to 75th percentiles). The mid-band indicates the median, and the whiskers indicate points within 1.5 times the interquartile range. Points outside the whiskers are outliers. Note: to visualize the data more effectively, the axes depicting FMC (%) were visually constrained to 150%, preserving all values while focusing on a more relevant range.

The smoothed, marginal effects of understory cover, canopy cover, heat load index and precipitation on FMC transformed to show the fitted GAM function on the response scale. The x axis shows values of the covariate, the rug indicates the distribution of covariate observations, and the y axis shows expected FMC values. The gray bands correspond to the 95% confidence interval and represent model uncertainty in the transformed (response scale) estimate. Though negative values are not possible in FMC or under a Gamma distribution, the confidence interval below 0 in the precipitation plot is an artifact of transformation and reflects wide uncertainty.

🔥New in IJWF:

Ohlson et al. map fine dead fuel moisture across a Colorado mixed-conifer forest using 1-h fuel samples from 80 plots. They reveal strong fine-scale spatiotemporal variability shaped by canopy, understory, and aspect.

🔗 doi.org/10.1071/WF25...

#IJWildlandFire

Fire frequency between 1989 and 2021 and land use and land cover (LULC) in 2021 from the Carajás National Forest, Eastern Amazon, Pará, Brazil. Forest areas are not delineated; i.e. they correspond to areas outside the anthropised and canga areas. The embedded pie chart shows the accumulated fire scars per LULC class; note that profound changes in LULC during the observation period ( Fig. 1) were considered for construction of the chart.

Time series data of the annual proportion of burned areas (%) within Campo Ferruginosos National Park and Carajás National Forest and possible fire drivers (Amazonian and regional deforestation, annual precipitation and climatic severity) for the period 1989–2021 (a) and correlations between potential fire drivers and the annual proportion of burned areas, separated by area (b). The dashed lines in (a) represent the 5-year trends. The dashed lines in the scatterplots (b) represent the linear relationships between the rainfall, climatic severity, and deforestation trends and the annual proportion of burned areas.

🔥New in IJWF

Sanjuan et al. analyse 33 years of fires in Amazonian canga, showing stark contrasts between long-protected Forest and recently protected Forest. They find land-use history drives fire occurrence while dry-season rainfall controls intensity.

🔗 doi.org/10.1071/WF24...

#IJWildlandFire

Fig. 1. The geographic, environmental and management context of the four study locations in the Australian deserts. (a) The main vegetation types (data from National Vegetation Information System (NVIS) 6.0 downloaded July 2023) and rainfall isohyets (data from Bureau of Meteorology (BoM)). Non-shaded areas represent either minor vegetation types, or non-desert vegetation of the northern savannas. (b) Areas that are owned or managed/comanaged by Indigenous groups; and areas that are managed for conservation as part of the National Reserve System by the government, private organisations, and Indigenous groups. Non-shaded areas on the map are non-Indigenous tenures not managed for conservation. Data on Indigenous ownership and management were obtained from Australian Bureau of Agricultural and Resource Economics and Sciences; and data on protected areas from Collaborative Australian Protected Areas Database (downloaded 12 March 2023). (c) The annual rainfall for the four study locations, over the study period 1997–2019 (data resourced from Scientific Information for Land Owners (SILO), downloaded 18 Jan 2023). Note the high rainfall in 2000–2001 and 2010–2011, especially at Newhaven and Tanami.

Fig. 2. Annual fire extent and fire season. (a) The annual fire extent in the managed and baseline periods.(b) Annual fire extent in relation to variation in rainfall during the preceding 2 years; data for Pinpi, Katjarra and Newhaven are grouped, and shown separately to data from Tanami. (c) The proportion of each year's fire extent that occurred in the hot season, in conditions of low, moderate, and high rainfall in the preceding two years; data for the baseline period and the managed period are shown separately. Data for Pinpi, Katjarra and Newhaven are grouped, and shown separately to data from Tanami. (d) An example of the accumulated cool (blue shading) and hot season (orange shading) fires for the baseline and managed periods is shown for Pinpi.

🔥New in IJWF:
Cliff et al. evaluate a decade of Indigenous-led fire management across 4 Australian desert sites. The shift to cooler-season, smaller burns improved fire heterogeneity and cultural engagement.
🔗 doi.org/10.1071/WF25...

#IJWildlandFire

🔥New in IJWF:

Desservettaz et al. provide a comprehensive review addressing Australian firefighters’ concerns about bushfire smoke. The article outlines health risks, gaps in PPE protection, off-gassing, dermal exposure, and mitigation strategies.

🔗 doi.org/10.1071/WF25138

#IJWildlandFire

1/4 - Check our new paper in @ijwildlandfire.bsky.social

We use MOEA optimisation to support decisions for #wildfire risk reduction (e.g., where should we use prescribed burning to reduce risk?). Optimisation improves fuel treatment plan effectiveness by 80–280% and adapts to changing objectives.

Top-left plot shows the performance of Pareto-optimal fuel treatment plans (black points) for the BP-area objective that aims to reduce burn probability (BP) to the entire Adelaide Hills region. Light grey points represent non-Pareto fuel treatment plans that were considered as part of the optimisation process. Other plots show selected fuel treatment plans matching a range of different levels of area treated (AT), as indicated by crosses on the Pareto front. The selected solutions show that the specific treatment blocks chosen in a plan can change significantly for different levels of AT. Comparisons are made between plans with an AT of 1.01 or 1.07% and 4.91 or 5.17%. Areas that are unique between the plans at the two levels of comparison are shown in colour and areas chosen by both plans are shown in black. Reduction in BP is calculated using the baseline BP as modelled with the metamodel.

🔥New in IJWF:
Radford et al. introduce a simulation-optimisation framework using neural network metamodels and NSGA-II to create fuel treatment plans that reduce burn probability by up to 284%, this method balances risk reduction and resource use.

🔗 doi.org/10.1071/WF25080

#IJWildlandFire

🔥New in IJWF:

Desservettaz et al. review the complex composition and health risks of bushfire smoke for firefighters, offering evidence-based guidance on exposure reduction, PPE use, and decontamination.

🔗 doi.org/10.1071/WF25138

#IJWildlandFire

In short: connectsci.au brings a new look and features, but with the same high standards and publication ethics.
Browse International Journal of Wildland Fire:
connectsci.au/wf

Powered by @csiropublishing.bsky.social , connectsci.au prioritises accessibility, discoverability & functionality, incl:
- better search filtering across article types&subjects
- journal article split screen view
- nuanced email notification options, inc. saved search alerts: connectsci.au/sign-in

Today @csiropublishing.bsky.social launched ConnectSci, a new global destination for trusted science content, hosting our journal, eBooks and a science news service.
You can now find International Journal of Wildland Fire here:
connectsci.au/wf
So, what's new for readers and authors?

Fig. 1. Geometric structure of symmetric canyon (a); experimental set-up (b); and schematic diagram (c).

🔥New in IJWF:

Fan et al. investigate canyon fire dynamics under varied terrains, revealing critical slope thresholds (α ≥ 27.5°, δ ≥ 20°) for eruptive fire. Strong convective heating ahead of the fire front drives rapid spread, challenging strategies.

🔗 doi.org/10.1071/WF24...
#IJWildlandFire

The most common barrier identified in each dispatch zone (top); and the most common barrier constructed through human landscape modification (bottom).

🔥Most Read

Epstein & Seielstad analyse WFDSS text from 6,630 large US wildfires (2011–2023). Barriers appear in 75%—mainly roads, burn scars and fuel variation. Prior fires more often stopped spread than treatments.

🔗 doi.org/10.1071/WF25051
#IJWildlandFire

Fuel treatment sequence and categorization. In a two-stem process, large number of specific treatments were categorized and combined based on their sequence, and then further combined based on the dominant characteristic of the modification of fuels.

🔥Most Read

Fallon et al. present a novel methodology to assess fuel treatment effectiveness in California forests. Using FTEM and FACTS data, they show 61% of treatments modified fire behavior, with fire or removal-based treatments most effective.

🔗 doi.org/10.1071/WF24220
#IJWildlandFire

Wildfire risk assessment in Sichuan Province, China: hazard modeling approach considering different combinations of classification criteria and connection values of factor attributes Background Current wildfire risk research has primarily focused on hazard assessment, lacking a comprehensive framework that integrates vulnerability and adaptive capacity. Moreover, the influence of ...

🔥Most Read

Wagner et al. highlight elevated PTSD, depression, anxiety, and sleep problems in wildland firefighters, but baseline risks unclear. They urge clearer separation of general vs fire-specific stress, focus on everyday exposures, long-term impacts.

🔗 doi.org/10.1071/WF24159
#IJWildlandFire

Wildfire hazard maps generated using different connection methods coupled with the logistic regression (LR) algorithm. Subfigures (a–f) are based on wildfire samples as classification criteria of factor attributes, and subfigures (g–l) are based on the whole area (PS: probability statistics; FR: frequncy ratio; IV: information value; CF: certainty factor; WOE: weights of evidence; EBF: evidential belief function).

🔥 New in IJWF:

Yue et al. present a comprehensive wildfire risk framework for Sichuan, China, integrating hazard and vulnerability. Using six statistical connection methods with logistic regression, they identify the Point-IV-LR model as most effective.

🔗 doi.org/10.1071/WF25089
#IJWildlandFire

🏠🔥New in IJWF:

Gjedrem et al. (2025) examine fire risk perception and garden adaptation in Tasmania’s WUI. Residents often underestimate hazards, but personalised garden hazard reports motivated change despite knowledge, resource, and emotional barriers.

🔗 doi.org/10.1071/WF24213
#IJWildlandFire

Brief:

Wildland-urban interface fires pose a challenge, driven by climate change, expanding settlements, and evolving fire regimes. Recent fires in LA, highlight the need to advance scientific understanding, policy, and adaptive management strategies to mitigate risks and enhance resilience.

🔥Call for Papers: Wildland-Urban Interface Fires Special Collection in IJWF!🌳🏠🌳

Share research on fire dynamics, risk modelling, resilience & more. If you have any questions, reach out to the team.

📆 Submit by 31 Dec 2025

More Information: www.publish.csiro.au/wf/content/C...

#FireScience

🌿 New in IJWF:

Krix et al. (2025) developed impact models for the Australian Fire Danger Rating System to predict structure loss from wildfires. Using structure density, cleared land, canopy height and terrain ruggedness.

🔗 doi.org/10.1071/WF24148
#IJWildlandFire

🌳New in IJWF
Wong et al. show that heat yields in wet sclerophyll fuels vary widely by species and season—especially in live understorey fuels. Fixed values in fire models may overestimate fire intensity.

📄 doi.org/10.1071/WF24227
#IJWildlandFire

🌿 New in IJWF:

O’Grady et al. shows how Machine Learning with Landsat can reconstruct fire histories across US military lands. Models achieved >93% accuracy, offering local-scale insights into ignition patterns & fire management for defense landscapes.

🔗 doi.org/10.1071/WF24214
#IJWildlandFire

The figure shows 2023 fire spread in two deserts and how recent past burns helped stop the fires.

🌿 New in IJWF:

Fisher et al. study the 2023 “Black Spring” in Australia’s northern spinifex deserts. Indigenous ranger-led burns created breaks that slowed or redirected wildfires, showing the potential of large-scale desert fire management to limit megafires. 🔥🌳

🔗 doi.org/10.1071/WF25002

🌿 New in IJWF:

Farquhar et al. (2025) studied how reptiles and mammals respond to fire in Triodia-mallee woodlands. Some lizards peaked 1 year post-fire, while others like Ningaui yvonneae preferred long-unburnt, mature spinifex habitat. 🔥🦎
🔗 doi.org/10.1071/WF24...

#IJWildlandFire

🌿 New in IJWF:

🔥 Quiñones et al. show that vegetation phenology—captured via remote sensing—strongly influences wildfire spread in NW Europe. Fires spread faster during vegetation dormancy, not peak fire weather. 🌿📡
🔗 doi.org/10.1071/WF24...

#IJWildlandFire

🌿 New in IJWF:

Belcher et al. explore seasonal changes in flammability across UK heathlands, showing that live fuel moisture dynamics drive shifts in fire behaviour. Their fuel modelling reveals up to 4× differences in fire spread based on phenology.
👉 doi.org/10.1071/WF24...

#IJWildlandFire

High cita📖 in IJWF:

Qin et al. show deep peat fires smoulder for 10+ days, moving up, down and sideways at ~300 °C under limited oxygen, yet losing <25 % mass and emitting detectable CO at the surface — a clue to locate hidden hotspots.
www.publish.csiro.au/WF/WF22143
#IJWildlandFire

Most read📖 in IJWF

Joshi et al. produced Australia’s first nationally consistent wildland fuel type map using satellite-derived vegetation structure. The dataset supports fire behaviour modelling and risk assessment across jurisdictions.
🛰️🔥
📖 doi.org/10.1071/WF24...
#IJWildlandFire

🙏 Editorial thanks from IJWF Co-EiC Martin Girardin
Dr. Martin celebrates IJWF’s growth, global reach, and evolving role in a rapidly changing field. From embracing AI and climate-fire complexity to diversifying the editorial board.
📖 : doi.org/10.1071/WF25...
##IJWildlandFire

🔥 High Cita in IJWF:
Keeley’s review clarifies the critical distinctions among fire intensity, fire severity, and burn severity.
This paper argues for consistent terminology, separating energy release, organic matter loss, and ecosystem responses.
📖 Read: doi.org/10.1071/WF07...
#IJWildlandFire

🔥 New in IJWF:
How will climate change reshape fuel hazards across landscapes?
McColl-Gausden et al. modelled future fire risks across Victoria, Australia and their findings highlight the urgent need for adaptive fuel management in a warming climate.
📖 Read: doi.org/10.1071/WF24...
#IJWildlandFire