Skip to main content

A Landscape-Scale Optimisation Model to Break the Hazardous Fuel Continuum While Maintaining Habitat Quality


Wildfires have demonstrated their destructive powers in several parts of the world in recent years. In an effort to mitigate the hazard of large catastrophic wildfires, a common practice is to reduce fuel loads in the landscape. This can be achieved through prescribed burning or mechanically. Prioritising areas to treat is a challenge for landscape managers. To help deal with this problem, we present a spatially explicit, multiperiod mixed integer programming model. The model is solved to yield a plan to generate a dynamic landscape mosaic that optimally fragments the hazardous fuel continuum while meeting ecosystem considerations. We demonstrate that such a multiperiod plan for fuel management is superior to a myopic strategy. We also show that a range of habitat quality values can be achieved without compromising the optimal fuel reduction objective. This suggests that fuel management plans should also strive to optimise habitat quality. We illustrate how our model can be used to achieve this even in the special case where a faunal species requires mature habitat that is also hazardous from a wildfire perspective. The challenging computational effort required to solve the model can be overcome with either a rolling horizon approach or lexicographically. Typical Australian heathland landscapes are used to illustrate the model but the approach can be implemented to prioritise treatments in any fire-prone landscape where preserving habitat connectivity is a critical constraint.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. 1.

    Agee, J.K., & Skinner, C.N. (2005). Basic principles of forest fuel reduction treatments. Forest Ecology And Management, 211(1), 83–96.

    Article  Google Scholar 

  2. 2.

    Ager, A.A., Vaillant, N.M., McMahan, A. (2013). Restoration of fire in managed forests: a model to prioritize landscapes and analyze tradeoffs. Ecosphere, 4(2), 1–19.

    Article  Google Scholar 

  3. 3.

    Alcasena, F.J., Ager, A.A., Salis, M., Day, M.A., Vega-García, C. (2018). Optimizing prescribed fire allocation for managing fire risk in central catalonia. Science of the Total Environment, 621, 872–885.

    Article  CAS  Google Scholar 

  4. 4.

    Bezanson, J., Edelman, A., Karpinski, S., Shah, V.B. (2017). Julia: a fresh approach to numerical computing. SIAM Review, 59(1), 65–98.

    Article  Google Scholar 

  5. 5.

    Boer, M.M., Sadler, R.J., Wittkuhn, R.S., McCaw, L., Grierson, P.F. (2009). Long-term impacts of prescribed burning on regional extent and incidence of wildfires-evidence from 50 years of active fire management in SW Australian forests. Forest Ecology and Management, 259(1), 132–142.

    Article  Google Scholar 

  6. 6.

    Brown, S., Clarke, M., Clarke, R. (2009). Fire is a key element in the landscape-scale habitat requirements and global population status of a threatened bird: the Mallee Emu-wren (Stipiturus Mallee). Biological Conservation, 142(2), 432–445.

    Article  Google Scholar 

  7. 7.

    Burrows, N. (2008). Linking fire ecology and fire management in south-west Australian forest landscapes. Forest Ecology and Management, 255(7), 2394–2406.

    Article  Google Scholar 

  8. 8.

    Carey, H., & Schumann, M. (2003). Modifying wildfire behavior – the effectiveness of fuel treatments, the status of our knowledge. National Community Forestry Center, Southwest Region Working Paper 2. Available at: [Verified 2018].

  9. 9.

    Cheal, D. (2010). Growth stages and tolerable fire intervals for Victoria’s native vegetation data sets. In Fire and adaptive management report No. 84. East Melbourne, Victoria, Australia: Department of Sustainability and Environment.

  10. 10.

    Chung, W. (2015). Optimizing fuel treatments to reduce wildland fire risk. Current Forestry Reports, 1(1), 44–51.

    Article  Google Scholar 

  11. 11.

    Stefano, J.D., McCarthy, M.A., York, A., Duff, T.J., Slingo, J., Christie, F. (2013). Defining vegetation age class distributions for multispecies conservation in fire-prone landscapes. Biological Conservation, 166, 111–117.

    Article  Google Scholar 

  12. 12.

    Dunning, I., Huchette, J., Lubin, M. (2017). Jump: a modeling language for mathematical optimization. SIAM Review, 59(2), 295–320.

    Article  Google Scholar 

  13. 13.

    Fernandes, P.M., & Botelho, H.S. (2003). A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire, 12(2), 117–128.

    Article  Google Scholar 

  14. 14.

    Finney, M.A. (2001). Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. Forest Science, 47(2), 219–228.

    Google Scholar 

  15. 15.

    Finney, M.A. (2008). A computational method for optimising fuel treatment locations. International Journal of Wildland Fire, 16(6), 702–711.

    Article  Google Scholar 

  16. 16.

    Finney, M.A., Seli, R.C., McHugh, C.W., Ager, A.A., Bahro, B., Agee, J.K. (2008). Simulation of long-term landscape-level fuel treatment effects on large wildfires. International Journal of Wildland Fire, 16(6), 712–727.

    Article  Google Scholar 

  17. 17.

    USGAO. (2003). Wildland fire management: Additional actions required to better identify and prioritize lands needing fuels reduction, GAO-03-805. Washington, DC.: USGAO.

    Google Scholar 

  18. 18.

    (2018). LLC Gurobi Optimization. Gurobi optimizer reference manual, p 786.

  19. 19.

    Hof, J., & Omi, P. (2003). Scheduling removals for fuels management, USDA Forest Service Proceedings RMRS-p-29, pp. 367–378. Available at : [Verified 2018].

  20. 20.

    Keith, D.A., McCaw, W.L., Whelan, R.J. (2002). Fire regimes in Australian heathlands and their effects on plants and animals. Flammable Australia: the fire regimes and biodiversity of a continent. In Bradstock, R.A., Williams, J.E., Gill, M.A. (Eds.) Flammable Australia: the fire regimes and biodiversity of a continent, 463p. Cambridge University Press (pp. 199–237).

  21. 21.

    Kim, Y.-H., Bettinger, P., Finney, M.A. (2009). Spatial optimization of the pattern of fuel management activities and subsequent effects on simulated wildfires. European Journal of Operational Research, 197(1), 253–265.

    Article  Google Scholar 

  22. 22.

    King, K.J., Bradstock, R.A., Cary, G.J., Chapman, J., Marsden-Smedley, J.B. (2008). The relative importance of fine-scale fuel mosaics on reducing fire risk in South-West Tasmania, Australia. International Journal of Wildland Fire, 17(3), 421–430.

    Article  Google Scholar 

  23. 23.

    King, K.J., Cary, G.J., Bradstock, R.A., Chapman, J., Pyrke, A., Marsden-Smedley, J.B. (2006). Simulation of prescribed burning strategies in south-west Tasmania, Australia: effects on unplanned fires, fire regimes, and ecological management values. International Journal of Wildland Fire, 15(4), 527–540.

    Article  Google Scholar 

  24. 24.

    Loehle, C. (2004). Applying landscape principles to fire hazard reduction. Forest Ecology and Management, 198(1), 261–267.

    Article  Google Scholar 

  25. 25.

    MacHunter, J., Menkhorst, P., Loyn, R.H. (2009). Towards a process for integrating vertebrate fauna into fire management planning. Arthur Rylah Institute for Environmental Research Department of Sustainability and Environment.

  26. 26.

    Minas, J.P., Hearne, J.W., Martell, D.L. (2014). A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts. European Journal of Operational Research, 232(2), 412–422.

    Article  Google Scholar 

  27. 27.

    Nguyen, D.T. (2015). Develop a multistage stochastic program with recourse for scheduling prescribed burning based fuel treatments with consideration of future wildland fires and fire suppressions. Ph.D. thesis, Colorado State University. Libraries.

  28. 28.

    Oliveira, T.M., Barros, A.M.G., Ager, A.A., Fernandes, P.M. (2016). Assessing the effect of a fuel break network to reduce burnt area and wildfire risk transmission. International Journal of Wildland Fire, 25(6), 619–632.

    Article  Google Scholar 

  29. 29.

    Penman, T.D., Christie, F.J., Andersen, A.N., Bradstock, R.A., Cary, GJ, Henderson, M.K., Price, O., Tran, C., Wardle, G.M., Williams, R.J. (2011). Prescribed burning: how can it work to conserve the things we value? International Journal of Wildland Fire, 20(6), 721–733.

    Article  Google Scholar 

  30. 30.

    Rachmawati, R., Ozlen, M., Hearne, J., Reinke, K. (2018). Fuel treatment planning: Fragmenting high fuel load areas while maintaining availability and connectivity of faunal habitat. Applied Mathematical Modelling, 54, 298–310.

    Article  Google Scholar 

  31. 31.

    Rachmawati, R., Ozlen, M., Reinke, K.J., Hearne, J.W. (2015). A model for solving the prescribed burn planning problem. SpringerPlus, 4(1), 1–21.

    Article  Google Scholar 

  32. 32.

    Rachmawati, R., Ozlen, M., Reinke, K.J., Hearne, J.W. (2016). An optimisation approach for fuel treatment planning to break the connectivity of high-risk regions. Forest Ecology and Management, 368, 94–104.

    Article  Google Scholar 

  33. 33.

    Rayfield, B., Pelletier, D., Dumitru, M., Cardille, J.A., Gonzalez, A. (2015). Multipurpose habitat networks for short-range and long-range connectivity: a new method combining graph and circuit connectivity. Methods in Ecology and Evolution, 7, 222–231.

    Article  Google Scholar 

  34. 34.

    Rönnqvist, M., D’amours, S., Weintraub, A, Jofre, A., Gunn, E., Haight, R.G., Martell, D., Murray, A.T., Romero, C. (2015). Operations research challenges in forestry: 33 open problems. Annals of Operations Research, 232(1), 11–40.

    Google Scholar 

  35. 35.

    Rytwinski, A., & Crowe, K.A. (2010). A simulation-optimization model for selecting the location of fuel-breaks to minimize expected losses from forest fires. Forest Ecology And Management, 260(1), 1–11.

    Article  Google Scholar 

  36. 36.

    Salazar, L.A., & González-Cabán, A. (1987). Spatial relationship of a wildfire, fuelbreaks, and recently burned areas. Western Journal of Applied Forestry, 2(2), 55–58.

    Article  Google Scholar 

  37. 37.

    Southwell, D.M., Lechner, A.M., Coates, T., Wintle, B.A. (2008). The sensitivity of population viability analysis to uncertainty about habitat requirements: implications for the management of the endangered southern brown bandicoot. Conservation Biology, 22(4), 1045–1054.

    Article  Google Scholar 

  38. 38.

    Strom, B.A., & Fulé, P.Z. (2007). Pre-wildfire fuel treatments affect long-term ponderosa pine forest dynamics. International Journal of Wildland Fire, 16(1), 128–138.

    Article  Google Scholar 

  39. 39.

    Thompson, M.P., Bowden, P., Brough, A., Scott, J.H., Gilbertson-Day, J., Taylor, A., Anderson, J., Haas, J.R. (2016). Application of wildfire risk assessment results to wildfire response planning in the Southern Sierra Nevada, California, USA. Forests, 7, 64.

  40. 40.

    Venn, T.J., & Calkin, D.E. (2011). Accommodating non-market values in evaluation of wildfire management in the united states: challenges and opportunities. International Journal of Wildland Fire, 20(3), 327–339.

    Article  Google Scholar 

  41. 41.

    Wei, Y. (2012). Optimize landscape fuel treatment locations to create control opportunities for future fires. Canadian Journal of Forest Research, 42(6), 1002–1014.

    Article  Google Scholar 

  42. 42.

    Wei, Y., & Long, Y. (2014). Schedule fuel treatments to fragment high fire hazard fuel patches. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 6(1), 1–10.

    Google Scholar 

  43. 43.

    Wei, Y., Rideout, D., Kirsch, A. (2008). An optimization model for locating fuel treatments across a landscape to reduce expected fire losses. Canadian Journal of Forest Research, 38(4), 868–877.

    Article  Google Scholar 

Download references


The authors are very grateful to an anonymous reviewer whose comments and suggestions have helped considerably in enhancing this paper.


This work has been supported by the Marie Skłodowska-Curie RISE H2020 project GEO-SAFE (691161), Government of Spain (MTM2015-65803-R), the Goverment of Madrid (S2013/ICE-2845 CASI-CAM-CM) and the UCM grant (CT27/16-CT28/16).

Author information



Corresponding author

Correspondence to John W. Hearne.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

León, J., Reijnders, V.M.J.J., Hearne, J.W. et al. A Landscape-Scale Optimisation Model to Break the Hazardous Fuel Continuum While Maintaining Habitat Quality. Environ Model Assess 24, 369–379 (2019).

Download citation


  • Wildfires
  • Spatial optimisation
  • Fuel continuum
  • Habitat quality
  • Multiperiod landscape planning
  • Mixed integer programming