Overview

Forest fires are a natural component of many of Canada's forested ecosystems but they also pose threats to public safety, property and forest resources. Every year, forest fires cause millions of dollars worth of damage and force the evacuation of some communities. Such problems will be exacerbated as people establish more homes and cottages in and near forested areas and climate change alters forest vegetation and weather.

The forest fire research community has made steady progress over the past four decades, in increasing our understanding of the nature of forest fires. Mathematical models for predicting fire occurrence have been developed. Deterministic spread models are being implemented for planning purposes and for use in computer simulations to aid in prediction of the future behavior of existing and potential fires. Such models are used in conjunction with queueing models in the strategic management of fire-fighting resources such as aircraft and fire fighters. Although much has been learned about the interactions of weather and fuel-types and their effects on fire spread, and intensity a large number of questions remain. For example, how can jump-fires ignited by burning bark and other firebrands carried by the wind, in advance of a spreading fire, be modelled as a stochastic process? How can the fire hazard in particular areas be estimated reliably? What are the potential impacts of climate change on fire regimes and fire management systems?

Statisticians can play a role in helping to answer these and other questions. Stochastic models, and point process models in particular, should prove to be very useful in attacking these kinds of problems. In recent years, point process intensity models have been successfully used in the study of earthquakes and volcanoes. Related intensity models have high potential in the forest fire context. For example, the times and locations of lightning strokes and fire ignitions can be viewed as a bivariate point process. A mutually exciting process is a possible model for this process where the lightning process drives the ignition process, but where the ignition process itself may have a self-exciting component representing the generation of jump-fires. The parameters of such models can be estimated efficiently using maximum likelihood; covariates such as fuel-type and moisture can be accounted for, and a form of residual analysis can be performed to assess the appropriateness of such models for given data. Interacting particle system models offer a way to stochastically model the spatio-temporal dynamics of a forest fire.

The purpose of the proposed workshop is to bring forest fire researchers and point process modellers and other interested statisticians together. The objective is for members of each group to inform the other group of open problems and possible solutions.

Invited Speakers and Abstracts

Larry Bradshaw, Fire Sciences Lab, MT David Brillinger, UC, Berkeley Dave Butry, Forestry Sciences Lab, North Carolina Steven G. Cumming, Boreal Ecosystems Research Andre Dabrowski, University of Ottawa Charmaine Dean, Simon Fraser Sylvia Esterby, UBC-Okanagan Marie-Josee Fortin, Toronto Fuensanta Saura Igual , Jaume I, Spain Marcia Gumpertz, North Carolina State Gail Ivanoff, University of Ottawa Ed Johnson, University of Calgary

David Vere-Jones, Victoria University of Wellington, NZ Rafal Kulik, University of Ottawa Reg Kulperger, UWestern Ontario Rob McAlpine, OMNR Haiganoush Preisler, Southwest Research Station, USDA Forest Service Dean Slonowsky, University of Manitoba David Stanford, University of Western Ontario Brian Stocks, CFS Rolf Turner, University of New Brunswick Domingos Xavier Viegas, Coimbra, Portugal Douglas Woolford, University of Western Ontario Mike Wotton, Faculty of Forestry, University of Toronto

Registration fee:

Reg'n fee $100 CDN, $25CDN for PDF's and Students (includes banquet on Thursday May 26)

Mailing List

---

Back to top