Fire Modeling in GIS

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Fire Modeling in GIS

• Ashton Erler
• 27th March 2007

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Why Model Forest Fires?
It roared from behind, below, and across, and the
crew, inside it, was shut out from all but a
small piece of the outside world. An Excerpt
from Young Men and Fire by Norman Maclean.
Written about the fire of Mann Gulch in Montana
(1949) that claimed the lives of no less than 13
Fire Fighters
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Why Model Forest Fires?

• Modeling forest fires is an essential part of
  forest stand management.
• Modeling is essential to aiding fire fighters and
  rescue workers deploy and manage fires safely.
  Allowing prior warning to residents who may be in
  the path of the flames and indicating the best
  points to attack a fire.
• Allows ecologist and forest managers to safely
  plan prescribed burns, assessing the extent of
  the spread of a fire.
• In short it is essential to forest management and
  safety!

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Methods of Wild Fire Modeling

• There are three main methods for modeling fires,
  each with their own advantages and disadvantages
• Statistical (Empirical)
• Physical (Theoretical or Analytical)
• Semi-Empirical

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Statistical Fire Modeling

• Statistical fire models predict fire spread based
  on fixed probabilities collected from years of
  observation. These models are usually limited to
  two dimensional models and are largely only
  useful for fairly uniform stands. E.g. in Canada
  or Australia

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Physical Fire Models

• These are perhaps the most intricate models, and
  therefore the most accurate and sensitive.
  Physical models demand a complete knowledge of
  the physical properties of the system (in this
  case a forest stand.) For the purposes of
  modeling and GIS, the most common models used are
  the semi-empirical models.

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Semi-Empirical Models

• Semi-empirical models allow for simple
  multifaceted analysis that lends itself to
  developing operational tools that can be applied
  across a range of situations. Most modeling
  programs apply semi-empirical methods.
• There are three main semi-empirical models that
  are applied to fire-modeling programs in GIS
• Rothermels Model Applied to programs such as
  BEHAVE and FARSITE.
• Huygens Model
• Cellular Automata

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A Brief Summary of Cellular Automata

• For the remainder of this speech I will be
  dealing with Rothermels and Huygens models and
  the applications that have sprung from them.
  However, here is a brief game to demonstrate the
  principles of cellular automata.
• Java Applet Cellular Automata example

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Rothermels Equations

• Rothermel deals with with fire intensity (Ib) and
  rate of spread (ROS).
• Rate of Spread (ROS) Variables
• ROS steady state spread rate (meters/min)
• Reaction intensity (Kjoules burned per minute as
  a function of the rate of acceleration)
• Bulk density of dry (combustible) material
• Heat necessary for ignition
• Fire Intensity Variables
• Energy release per square meter of fire front (Kj
  released)
• Heat yield of fuel (Kj/kg)
• Mass of Fuel per unit area (kg/m2)
• Finally Rate of Spread

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What this all Means for GIS

• These different variables are applied spatially
  and temporaly to generate tabular and visual data
  for fire spread. Huygens Formulae is applied to
  the shape of the fire front, and can be
  manipulted to take into account variables such as
  slope, wind e.t.c.
• And now for something completely different!

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On to the programs

• BehavePlus (the predecessor of BEHAVE) is a
  commonly used program, the most popular program
  however is FARSITE and its companion program
  FlamMap.
• FARSITE (Fire Area Simulator) , is a seperate
  executable file that uses programs such as ArcGIS
  to manage spatial data.

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FARSITE

• Farsite is used to assess
• Current Fires
• Potential Fires
• Fire Suppression
• Producing the following data
• Tabular
• Vector
• Raster

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FlamMap

• Flam Map is an add-on to the FARSITE program
  allowing the display of raster data over an
  entire FARSITE landscape, removing the need for
  an external GIS program for the plotting of
  spatial data. It is however a less functional
  program than ArcGIS.
• See www.fire.org for further information

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ArcGIS ver 9.2

• In order to deal with the all important temporal
  aspect of modeling in spatial environments (not
  just for fire modeling but also for weather,
  urban growth e.t.c) ESRI has improved the ability
  to add temporal elements to data coming into the
  GIS program in their 9.2 release of Arc GIS.
• This form of modelling in ArcGIS 9.2 is called
  process modeling or dynamic stochastic modeling

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Process Modeling in ArcGIS 9.2

• Process Modeling is similar to Cellular Automata
  modeling, working under under logic flow chart
  models using spatial analyst and advancing that
  model over iterations in time.
• Process
• Open Model builder in ArcGIS and create a flow
  chart for the rules of fire behaviour

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Time Modeling in ArcGIS
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References

• http//www.esri.com/news/arcnews/fall05articles/gi
  s-helping-manage2of2.html
• Finney, M. A. "FARSITE Fire Area SimulatorModel
  Development and Evaluation." Evaluation (1998).
• Maclean, Norman. Young Men Fire. Chicago
  University of Chicago Press, 1992,
• Rothermel, R. C. A Mathematical Model for
  Predicting Fire Spread in Wildland
  FuelsIntermountain Forest Range Experiment
  Station, Forest Service, US Dept. of Agriculture,
  1972.