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Forest Fire Management Using Geospatial Information System
Effective factors
The parameters considered in this model are:
- Wind: Wind increases flame of the burning cell and on the other hand increases life of fire in the cell. Also wind increases rate of heat transfer toward its direction and reduces rate of heat transfer against its direction. The more is the wind velocity, the more is its effect on fire.
- Topography: Slope which is usually extracted from a DEM has some effect on fire spread. As heat has a natural tendency to move upward, therefore, rate of heat transfer increases by an increase in the slope.
- Index of flammability: In this model these characteristics are divided into three classes. As mentioned before, temperature of a burning cell is assumed to follow a normal curve. But this curve is not unique. Standard deviation of this curve is the first parameter which is extracted from index of flammability. When the standard deviation of a cell increases, fire life for that cell increases too. Furthermore, the extracted values from normal curve are multiplied by a coefficient to achieve a logical value for its temperature or heat. This coefficient is the second parameter which is extracted from the index of flammability. The third quantitative value which is extracted from index of flammability is the degree of flammability.
- Regional temperature: This factor and moisture are from the most important factors which determine forest fire risk. At the time of forest fire, loss of heat in cells that receive the heat, is strongly depended on regional temperature. Therefore, in cold weather the domain of forest fires are more limited and speed of fire spread is much lower.
- Precipitation: This factor cools both burning cells and cells adjacent to the burning cells.
- Proximity: A burning cell has its most effect on its adjacent cells which have a common edge with it. However, adjacent cells with a common vertex receive heat as well. So these cells can not be neglected but the coefficient of heat capture for these cells is lower than the adjacent cells with a common edge.
Combined effects
The effect of above mentioned factors is more complex when they act together:
- Combined effect of wind and topography: As mentioned before, wind increases the flame of a burning cell and decreases resistance to fire. If the aspect of a cell is against the wind, the effect of wind is increased and if it is toward the wind, the effect of wind is decreased and even sometimes it can be neglected. Effect of the wind can be evaluated by determining the angle between normal vector (perpendicular to the surface) of a cell and vector of wind direction.
- Combined effect of wind, topography and temperature: Another effect of wind is to accelerate cooling. In the case of adjacent cells to the burning ones, wind increases the loss of received energy. If the aspect of a cell is against the wind direction, the cooling effect of the wind will increase and reduction of temperature makes this effect more severe.
Simulation
Normal model was implemented by Matlab 6.1. Simulated region is a 2 km x 2 km region with 20 m resolution cells. The index of flammability is simulated with four different regions which can be used with different DEM scenarios. Figure 3 shows the assumed index of flammability. According to this index, forest No.3 has a low degree of flammability, forest No.1 has a medium degree of flammability and degree of flammability for forest No.2 is a bit more than the forest No.1. Region of meadow has the lowest degree of combustibility.
This model has many coefficients and setting these coefficients properly has a great effect on the model performance. To set these coefficients, some experiments are done on fire. For instance, coefficient of the diagonal neighbors, coefficient of the effect of slope and some other coefficients are determined in this way. Due to the lack of sufficient, appropriate and up to date information about forest fires in Iran, a simulated forest with a scale of 1:100 is put on fire. Burning this simulated forest provides a good tool to assess the performance of the model. Figure 4 shows one of the simulations. In this figure the terrain has a constant slope of 13 percent downward, the wind with the azimuth of 150 degrees has a velocity of 15 kilometer per hour. Each color shows fire spread after one hour.
Figure 3: Index of flammability for simulated area. This simulation has three different forest regions and a meadow.
Figure 4: Simulated model, fire starting point has been shown. Each color shows fire spread after one hour.
Conclusions and recommendations
Concentric, pseudo-conical, polygonal and network models have obvious problems and are not compatible with normal model. Therefore, their results are not compatible either. For assessment of the normal model, information acquired from the simulated as well as forest fire records in developed countries are used. This comparison shows that normal model can be an acceptable approximation of fire spread. The degree of compatibility between the result of normal model and the simulated forest fire is about 70 percent. However, just comparison with a real forest fire can reveal compatibility and power of this model.
Fire is a complex and dynamic phenomenon and therefore, simulation of a forest fire is a real challenge. For example velocity and direction of the wind can vary continuously but real time determination of these variations is almost impossible. On the other hand, the combined effect of wind and topography can not be easily determined. Moreover, the behavior of wind in the valleys curl current of wind is not predictable.
More assessment on the relative effect of the fire parameters is one of the requirements of this model in the future. In case a real forest fire record exists, setting coefficients with artificial neural networks seems to be appropriate. Also because of the similar effect of fire in all directions, one can use a network with hexagonal cells instead of square ones.
References
- Brimicombe A., (2003), GIS, Environmental Modeling and Engineering, Taylor & Francis
- Wainwright J., Mulligan M., (2004), Environmental Modeling, John Wiley & Sons
- Cunningham W.P., Woodworth Saigo B., (1999), Environmental Science, McGraw-Hill
- Forest Fire in the American Southwest, http://forestfire.nau.edu/archives_june03.htm
- Burrough A. and McDonnell R.A. (1998), Principles of Geographical Information Systems, Oxford University Press.
- Ahmed Saidi, A. MISSOUMI, CNTS, (1999), The use of the GIS into the Forest Fire prediction The Simulation Model, http://www.cs.wright.edu/~bwang/course/ceg434634/pa1.pdf
- Borlawsky T., (2000), Forest Fire Simulation using Percolation Theory, www.dbmi.columbia.edu/~tbb7001/projects.htm
- Canadian Wildland Fire Information System (CWFIS), http://fire.cfs.nrcan.gc.ca
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