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Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

A computational method for optimising fuel treatment locations

Mark A. Finney
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USDA Forest Service, Missoula Fire Sciences Laboratory, PO Box 8089, Missoula, MT 59808, USA. Email: mfinney@fs.fed.us

International Journal of Wildland Fire 16(6) 702-711 https://doi.org/10.1071/WF06063
Submitted: 18 May 2006  Accepted: 6 September 2007   Published: 17 December 2007

Abstract

Modelling and experiments have suggested that spatial fuel treatment patterns can influence the movement of large fires. On simple theoretical landscapes consisting of two fuel types (treated and untreated), optimal patterns can be analytically derived that disrupt fire growth efficiently (i.e. with less area treated than random patterns). Although conceptually simple, the application of these theories to actual landscapes is made difficult by heterogeneity (fuels, weather, and topography). Here a computational method is described for heterogeneous landscapes that identifies efficient fuel treatment units and patterns for a selected fire weather scenario. The method requires input of two sets of spatial input data: (1) the current fuel conditions; and (2) the potential fuel conditions after a treatment is conducted (if treatment is permitted in a particular location). The contrast in fire spread rate between the two landscapes under the weather scenario conditions indicates where treatments are effective at delaying the growth of fires. Fire growth from the upwind edge of the landscape is then computed using a minimum travel time algorithm. This identifies major fire travel routes (areas needing treatment) and their intersections with the areas where treatments occurred and reduced the spread rate (opportunity for treatment). These zones of treatment ‘need and opportunity’ are iteratively delineated by contiguous patches of raster cells up to a user-supplied constraint on percentage of land area to be treated. This algorithm is demonstrated for simple and for complex landscapes.

Additional keywords: fire modelling, prescribed burning.


Acknowledgements

The present study was funded by the USA Joint Fire Sciences Program, the Bureau of Land Management (BLM), and the US Forest Service, Missoula Fire Sciences Laboratory. The author is grateful to Charles McHugh, Rob Seli, and Rick Stratton for their efforts in testing the optimisation model and helping prepare data and graphics for this paper. Howard Roose provided critical funding from the BLM for software development.


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