Pixel-Level Statistical Analyses of Prescribed Fire Spread
Miles Currie, Kevin Speer, Kevin Hiers, Joseph O'Brien and, Scott Goodrick, Bryan Quaife
TL;DR
This paper uses infrared imagery to statistically analyze cellular automata models for predicting wildland fire spread, revealing their strengths and limitations across scales.
Contribution
It introduces a method to estimate CA model parameters from infrared images and compares results across scales to assess CA's effectiveness.
Findings
CA parameters can be statistically derived from infrared images
CA model performance varies with spatial scale
Limitations of CA in capturing fire dynamics are identified
Abstract
Wildland fire dynamics is a complex turbulent dimensional process. Cellular automata (CA) is an efficient tool to predict fire dynamics, but the main parameters of the method are challenging to estimate. To overcome this challenge, we compute statistical distributions of the key parameters of a CA model using infrared images from controlled burns. Moreover, we apply this analysis to different spatial scales and compare the experimental results to a simple statistical model. By performing this analysis and making this comparison, several capabilities and limitations of CA are revealed.
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