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Spatial optimization of the pattern of fuel management activities and subsequent effects on simulated wildfires

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  • Kim, Young-Hwan
  • Bettinger, Pete
  • Finney, Mark

Abstract

Methods for scheduling forest management activities in a spatial pattern (dispersed, clumped, random, and regular) are presented, with the intent to examine the effects of placement of activities on resulting simulated wildfire behavior. Both operational and fuel reduction management prescriptions are examined, and a heuristic was employed to schedule the activities. The main hypothesis is that simulated wildfire effects during a severe fire season may be mitigated by scheduling activities in a pattern across the landscape. Results suggest: (1) operational management prescriptions, designed to promote the development of forest structure within a desired range of stand density, were not appropriate for mitigating wildfire effects, and (2) increased harvest levels obscure spatial patterns of activity, making patterns less clear as harvests increase. Results also suggest that fuel reduction management prescriptions may marginally minimize wildfire severity during a severe fire season, when scheduled in a spatial pattern.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:197:y:2009:i:1:p:253-265
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    References listed on IDEAS

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    1. Bettinger, Pete & Boston, Kevin & Kim, Young-Hwan & Zhu, Jianping, 2007. "Landscape-level optimization using tabu search and stand density-related forest management prescriptions," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1265-1282, January.
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    Cited by:

    1. Esther Jose & Puneet Agarwal & Jun Zhuang, 2023. "A data-driven analysis and optimization of the impact of prescribed fire programs on wildfire risk in different regions of the USA," 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. 118(1), pages 181-207, August.
    2. Calkin, David C. & Finney, Mark A. & Ager, Alan A. & Thompson, Matthew P. & Gebert, Krista M., 2011. "Progress towards and barriers to implementation of a risk framework for US federal wildland fire policy and decision making," Forest Policy and Economics, Elsevier, vol. 13(5), pages 378-389, June.
    3. 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.
    4. Bernardo K. Pagnoncelli & Adriana Piazza, 2017. "The optimal harvesting problem under price uncertainty: the risk averse case," Annals of Operations Research, Springer, vol. 258(2), pages 479-502, November.
    5. Kıbış, Eyyüb Y. & Büyüktahtakın, İ. Esra, 2017. "Optimizing invasive species management: A mixed-integer linear programming approach," European Journal of Operational Research, Elsevier, vol. 259(1), pages 308-321.
    6. 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.
    7. Tian, Fenghao & Li, Mingyu & Han, Xulong & Liu, Hui & Mo, Boxian, 2020. "A Production–Living–Ecological Space Model for Land-Use Optimisation: A case study of the core Tumen River region in China," Ecological Modelling, Elsevier, vol. 437(C).

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