Climate & Environmentarticle2026-10-02

A method for characterising fire events in high fire frequency landscapes: case study application to North Kimberley fire regimes, Western Australia

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Abstract

Background Identifying fire events provides insights into fire size distribution, the timing of significant fire events, their behaviour and ignition causes. However, this can be challenging when using temporally coarse datasets across high-fire-frequency savanna landscapes. Aims Develop and validate a fire event delineation (FED) methodology. Methods We introduce a fire size distribution (FSD) method combining high-resolution burnt area mapping with high temporal resolution hotspot data to produce detailed day-of-burn attributed mapping. This approach is applied to 2014–2023 fire mapping of North Kimberley savannas, northern Australia. Key results Analysis of the ten largest fires per year (n = 100) indicated their responsibility for 33.3% of total area burnt annually, with 69% occurring during the late dry season. Additionally, only 35% of these fires started in locations with on-ground access. Remote ignition fires burnt 51 and 64% of total area with all land burnt late in the year, respectively. A ~50% decrease in large fire sizes was observed, due to improved intensive fuel management. Conclusions The FED method provided an easily implementable and useful new approach for understanding fire regimes in high fire frequency landscapes. Implications Previous FSD methods underestimated total fire size for fires burning over multiple months. The method presented here improves on previous approaches.

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View paper (DOI)Open access versionOpenAlexInternational Journal of Wildland FirePublished 2026-10-02

Authors: Rohan Fisher, Porni Mollick, Leigh‐Ann Woolley, Tom Vigilante, Luke Russ

Institutions: Charles Darwin University, The University of Western Australia, Department of Physics, Mathematics and Informatics, Tharawal Aboriginal, Bush Heritage Australia, Northern Research Institute, Institute of Livelihood Research and Training