Abstract
Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale.
Highlights
In many wildland and prescribed fires, the fire is spread through ground fuels
Various factors determining the rate of spread (RoS) in pine straw, as well as many other fuels, have been condensed into fuel models that produce a single RoS value under given conditions via a fire spread model (e.g., Rothermel [2])
Cruz and Alexander [3] analyzed a large number of fire spread models and observations, and the level of uncertainty that they reveal demonstrates the need for further understanding of fire RoS in realistic settings
Summary
In many wildland and prescribed fires, the fire is spread through ground fuels. A typical ground fuel in these fires is dispersed pine straw beds, as pine forests are common native habitats across large areas of continental landscapes. These spread rates are typically based on uniform conditions and are not meant to represent local variations of spread in the full range of fuels and wind variability experienced in the field. While a laboratory setting allows for detailed small-scale measurements to be made, key environmental factors, such as fuel density variations, wind turbulence, and solar radiation are often absent. Field measurements of prescribed fires emphasizing the larger scale aspects of spread may not resolve important small-scale features [5]
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