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Forest structure, roads and soil moisture provide realistic predictions of fire spread in modern Swedish landscape

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  • Jones, Sara Sharon
  • Matsala, Maksym
  • Delin, Emily Viola
  • Subramanian, Narayanan
  • Nilsson, Urban
  • Holmström, Emma
  • Drobyshev, Igor

Abstract

Recent increases in fire activity in Sweden call for the quantification of forest fire susceptibility, in order to develop management strategies to mitigate fire risk. Using the data from 100 large Swedish forest fires (>10 ha), mapped from sentinel-2 images from 2016 to 2022, we explored the predictive power of vegetation properties in estimating relative likelihood of fires within a landscape using logistic regression. To model spatially explicit fire susceptibility within a given landscape, we used the outcome of logistic regression as an input into a cellular automata model (CA model), which simulates fire spread in a 2D grid.

Suggested Citation

  • Jones, Sara Sharon & Matsala, Maksym & Delin, Emily Viola & Subramanian, Narayanan & Nilsson, Urban & Holmström, Emma & Drobyshev, Igor, 2025. "Forest structure, roads and soil moisture provide realistic predictions of fire spread in modern Swedish landscape," Ecological Modelling, Elsevier, vol. 499(C).
    • Handle: RePEc:eee:ecomod:v:499:y:2025:i:c:s0304380024003302
    DOI: 10.1016/j.ecolmodel.2024.110942
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    References listed on IDEAS

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    1. Zheng, Zhong & Huang, Wei & Li, Songnian & Zeng, Yongnian, 2017. "Forest fire spread simulating model using cellular automaton with extreme learning machine," Ecological Modelling, Elsevier, vol. 348(C), pages 33-43.
    2. Holmström, Emma & Karlsson, Matts & Nilsson, Urban, 2017. "Modeling birch seed supply and seedling establishment during forest regeneration," Ecological Modelling, Elsevier, vol. 352(C), pages 31-39.
    3. Jellouli, Omar & Bernoussi, Abdes Samed, 2022. "The impact of dynamic wind flow behavior on forest fire spread using cellular automata: Application to the watershed BOUKHALEF (Morocco)," Ecological Modelling, Elsevier, vol. 468(C).
    4. Shruti Sachdeva & Tarunpreet Bhatia & A. K. Verma, 2018. "GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1399-1418, July.
    5. Hamed Adab, 2017. "Landfire hazard assessment in the Caspian Hyrcanian forest ecoregion with the long-term MODIS active fire data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(3), pages 1807-1825, July.
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