An optimization model for locating fuel treatments across a landscape to reduce expected fire losses

Locating fuel treatments with scarce resources is an important consideration in landscape-level fuel management. This paper developed a mixed integer programming (MIP) model for allocating fuel treatments across a landscape based on spatial information for fire ignition risk, conditional probabilities of fire spread between raster cells, fire intensity levels, and values at risk. The fire ignition risk in each raster cell is defined as the probability of fire burning a cell because of the ignition within that cell. The conditional probability that fire would spread between adjacent cells A and B is defined as the probability of a fire spreading into cell B after burning in cell A. This model locates fuel treatments by using a fire risk distribution map calculated through fire simulation models. Fire risk is assumed to accumulate across a landscape following major wind directions and the MIP model locates fuel treatments to efficiently break this pattern of fire risk accumulation. Fuel treatment resources ...

[1]  Ian A. Munn,et al.  Modeling Forest Fire Probabilities in the South Central United States Using FIA Data , 2003 .

[2]  Richard D. Stratton,et al.  Assessing the Effectiveness of Landscape Fuel Treatments on Fire Growth and Behavior , 2004, Journal of Forestry.

[3]  Philip N. Omi,et al.  Estimating the Cost of Fuels Treatment , 1995 .

[4]  Craig Loehle,et al.  Applying landscape principles to fire hazard reduction , 2004 .

[5]  David T. Butry,et al.  Understanding Broadscale Wildfire Risks in a Human-Dominated Landscape , 2002, Forest Science.

[6]  J. Chew.  Development of a system for simulating vegetative patterns and processes at landscape scales , 1995 .

[7]  Philip N. Omi,et al.  A timing-oriented approach to spatial allocation of fire management effort. , 2000 .

[8]  C. Mcketta,et al.  Analyzing Fuel Treatment Costs , 1986 .

[9]  Hans R. Zuuring,et al.  Overview of MAGIS: A multi-resource analysis and geographic information system. Forest Service research note , 1995 .

[10]  D. Goldsman.  Operations Research Models and Methods , 2003 .

[11]  Mark A. Finney,et al.  The challenge of quantitative risk analysis for wildland fire , 2005 .

[12]  Richard A. Minnich,et al.  Mapping probability of fire occurrence in San Jacinto Mountains, California, USA , 1993 .

[13]  Alexander B. Sideridis,et al.  Assessing Wildfire Destruction Danger: a Decision Support System Incorporating Uncertainty , 2005 .

[14]  Hans R. Zuuring,et al.  Applying Simulation and Optimization to Plan Fuel Treatments at Landscape Scales 1 , 2007 .

[15]  T. Curt,et al.  Fire-driven oak regeneration in French Mediterranean ecosystems. , 2009 .

[16]  J. Beverly,et al.  Modeling fire susceptibility in west central Alberta, Canada , 2009 .

[17]  M. Finney.  Design of Regular Landscape Fuel Treatment Patterns for Modifying Fire Growth and Behavior , 2001, Forest Science.

[18]  P. Hessburg,et al.  Dry forests and wildland fires of the inland Northwest USA: Contrasting the landscape ecology of the pre-settlement and modern eras , 2005 .

[19]  Philip N. Omi,et al.  Random location of fuel treatments in wildland community interfaces: a percolation approach , 2004 .

[20]  David L. Martell,et al.  Productivity of Ontario initial-attack fire crews: results of an expert-judgement elicitation study , 2004 .

[21]  A. Bartuska,et al.  Review and update of the 1995 Federal wildland fire management policy , 2001 .

[22]  Lucy A. Salazar,et al.  Spatial Relationship of a Wildfire, Fuelbreaks, and Recently Burned Areas1 , 1987 .