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A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts

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  • Minas, James P.
  • Hearne, John W.
  • Martell, David L.

Abstract

Elevated fuel loads are contributing to an increase in the occurrence of, and area burned by, severe wildfires in many regions across the globe. In an attempt to reverse this trend, fire and land management agencies are investing in extensive fuel management programs. However, the planning of fuel treatment activities poses complicated decision-making problems with spatial and temporal dimensions. Here, we present a mixed integer programming model for spatially explicit multi-period scheduling of fuel treatments. The model provides a flexible framework that allows for landscape heterogeneity and a range of ecological and operational considerations and constraints. The model’s functionality is demonstrated on a series of hypothetical test landscapes and a number of implementation issues are discussed.

Suggested Citation

  • Minas, James P. & Hearne, John W. & Martell, David L., 2014. "A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts," European Journal of Operational Research, Elsevier, vol. 232(2), pages 412-422.
    • Handle: RePEc:eee:ejores:v:232:y:2014:i:2:p:412-422
    DOI: 10.1016/j.ejor.2013.07.026
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    References listed on IDEAS

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    1. Kim, Young-Hwan & Bettinger, Pete & Finney, Mark, 2009. "Spatial optimization of the pattern of fuel management activities and subsequent effects on simulated wildfires," European Journal of Operational Research, Elsevier, vol. 197(1), pages 253-265, August.
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    Cited by:

    1. Adán Rodríguez-Martínez & Begoña Vitoriano, 2020. "Probability-Based Wildfire Risk Measure for Decision-Making," Mathematics, MDPI, vol. 8(4), pages 1-18, April.
    2. Rashidi, Eghbal & Medal, Hugh & Gordon, Jason & Grala, Robert & Varner, Morgan, 2017. "A maximal covering location-based model for analyzing the vulnerability of landscapes to wildfires: Assessing the worst-case scenario," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1095-1105.
    3. Matsypura, Dmytro & Prokopyev, Oleg A. & Zahar, Aizat, 2018. "Wildfire fuel management: Network-based models and optimization of prescribed burning," European Journal of Operational Research, Elsevier, vol. 264(2), pages 774-796.
    4. Araya-Córdova, P.J. & Vásquez, Óscar C., 2018. "The disaster emergency unit scheduling problem to control wildfires," International Journal of Production Economics, Elsevier, vol. 200(C), pages 311-317.
    5. Mendes, André Bergsten & e Alvelos, Filipe Pereira, 2023. "Iterated local search for the placement of wildland fire suppression resources," European Journal of Operational Research, Elsevier, vol. 304(3), pages 887-900.
    6. Mikael Rönnqvist & Sophie D’Amours & Andres Weintraub & Alejandro Jofre & Eldon Gunn & Robert Haight & David Martell & Alan Murray & Carlos Romero, 2015. "Operations Research challenges in forestry: 33 open problems," Annals of Operations Research, Springer, vol. 232(1), pages 11-40, September.
    7. Belavenutti, Pedro & Ager, Alan A. & Day, Michelle A. & Chung, Woodam, 2022. "Designing forest restoration projects to optimize the application of broadcast burning," Ecological Economics, Elsevier, vol. 201(C).
    8. Bhuiyan, Tanveer Hossain & Moseley, Maxwell C. & Medal, Hugh R. & Rashidi, Eghbal & Grala, Robert K., 2019. "A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior," European Journal of Operational Research, Elsevier, vol. 277(2), pages 699-718.
    9. Álvarez-Miranda, Eduardo & Garcia-Gonzalo, Jordi & Ulloa-Fierro, Felipe & Weintraub, Andrés & Barreiro, Susana, 2018. "A multicriteria optimization model for sustainable forest management under climate change uncertainty: An application in Portugal," European Journal of Operational Research, Elsevier, vol. 269(1), pages 79-98.
    10. Khakzad, Nima, 2021. "Optimal firefighting to prevent domino effects: Methodologies based on dynamic influence diagram and mathematical programming," Reliability Engineering and System Safety, Elsevier, vol. 212(C).

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