- 저자송학수, 권오성, 이상희
-
학술지 Korean Journal of Agricultural and Forest Meteorology 16(4), 259-266
- 등재유형
- 게재일자(2014)
Understanding the forest fire patterns is necessary to comprehend the stability of the forest ecosystems.
Thus, researchers have suggested the simulation models to mimic the forest fire spread dynamics,
which enables us to predict the forest damage in the scenarios that are difficult to be experimentally
tested in laboratory scale. However, many of the models have the limitation that many of them did not
consider the complicated environmental factors, such as fuel types, wind, and moisture. In this study,
we suggested a simple model with the factors, especially, the geomorphological structure of the forest
and two types of fuel. The two fuels correspond to susceptible tree and resistant tree with different
probabilities of transferring fire. The trees were randomly distributed in simulation space at densities
ranging from 0.5 (low) to 1.0 (high). The susceptible tree had higher value of the probability than the
resistant tree. Based on the number of burnt trees, we then carried out the sensitivity analysis to
quantify how the forest fire patterns are affected by the structure and tree density. We believe that
our model can be a useful tool to explore forest fire spreading patterns.
Understanding the forest fire patterns is necessary to comprehend the stability of the forest ecosystems.
Thus, researchers have suggested the simulation models to mimic the forest fire spread dynamics,
which enables us to predict the forest damage in the scenarios that are difficult to be experimentally
tested in laboratory scale. However, many of the models have the limitation that many of them did not
consider the complicated environmental factors, such as fuel types, wind, and moisture. In this study,
we suggested a simple model with the factors, especially, the geomorphological structure of the forest
and two types of fuel. The two fuels correspond to susceptible tree and resistant tree with different
probabilities of transferring fire. The trees were randomly distributed in simulation space at densities
ranging from 0.5 (low) to 1.0 (high). The susceptible tree had higher value of the probability than the
resistant tree. Based on the number of burnt trees, we then carried out the sensitivity analysis to
quantify how the forest fire patterns are affected by the structure and tree density. We believe that
our model can be a useful tool to explore forest fire spreading patterns.