Deriving Fire Behavior Metrics from UAS Imagery

Christopher J Moran,Matthew R Cunningham,Katie Sauerbrey,Carl A Seielstad,Lloyd Queen,Valentijn Hoff,Tim Wallace,Russell A Parsons

doi:10.3390/fire2020036

Abstract

The emergence of affordable unmanned aerial systems (UAS) creates new opportunities to study fire behavior and ecosystem pattern—process relationships. A rotor-wing UAS hovering above a fire provides a static, scalable sensing platform that can characterize terrain, vegetation, and fire coincidently. Here, we present methods for collecting consistent time-series of fire rate of spread (RoS) and direction in complex fire behavior using UAS-borne NIR and Thermal IR cameras. We also develop a technique to determine appropriate analytical units to improve statistical analysis of fire-environment interactions. Using a hybrid temperature-gradient threshold approach with data from two prescribed fires in dry conifer forests, the methods characterize complex interactions of observed heading, flanking, and backing fires accurately. RoS ranged from 0–2.7 m/s. RoS distributions were all heavy-tailed and positively-skewed with area-weighted mean spread rates of 0.013–0.404 m/s. Predictably, the RoS was highest along the primary vectors of fire travel (heading fire) and lower along the flanks. Mean spread direction did not necessarily follow the predominant head fire direction. Spatial aggregation of RoS produced analytical units that averaged 3.1–35.4% of the original pixel count, highlighting the large amount of replicated data and the strong influence of spread rate on unit size.

Highlights

We evaluated two cold target sizes as ground control points (GCPs), circular with 12 cm diameter, and square with 40 cm sides, at six altitudes above ground level (AGL) up to 150 m
unmanned aerial systems (UAS) can give an unprecedented perspective for data collection in active fire environments at favorable spatial and temporal scales if the software, hardware, and fire operations conflicts are resolved or minimized
Robust data collection workflows must constantly evolve while still maintaining coherent scientific rigor due to the rapid and ongoing development of UAS and sensor technology

Summary

Introduction

Laboratory and modeling approaches offer greater control of environmental conditions and better replicability than is possible in field settings, empirical studies of fire behavior remain important to a range of scientific inquires [1,2,3,4], including supporting theory of fire spread dynamics [5,6], evaluation/validation of mathematical fire models [7,8,9,10], and assessments of fire and other disturbance interactions [11,12], fuel treatment design and effectiveness [13,14,15], and fire behavior process-vegetation pattern relationships [16,17,18]. The performance of these metrics is well-documented in the literature, and many of the caveats are well-summarized in Table 6 of Hudak et al [35]

Methods

Results

Discussion

Conclusion