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Deriving Fire Behavior Metrics from UAS Imagery

National Center for Landscape Fire Analysis, University of Montana, 32 Campus Drive, CHCP 428, Missoula, MT 59812, USA
US Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, 5775 W. Highway 10, Missoula, MT 59801, USA
The Nature Conservancy, Sycan Marsh Preserve, OR 97621, USA
Author to whom correspondence should be addressed.
Received: 21 May 2019 / Revised: 12 June 2019 / Accepted: 20 June 2019 / Published: 22 June 2019
(This article belongs to the Special Issue Unmanned Aircraft in Fire Research and Management)
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. View Full-Text
Keywords: drones; fire rate of spread; thermal imagery; spatial autocorrelation; pseudo-replication; analytical units drones; fire rate of spread; thermal imagery; spatial autocorrelation; pseudo-replication; analytical units
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MDPI and ACS Style

Moran, C.J.; Seielstad, C.A.; Cunningham, M.R.; Hoff, V.; Parsons, R.A.; Queen, L.; Sauerbrey, K.; Wallace, T. Deriving Fire Behavior Metrics from UAS Imagery. Fire 2019, 2, 36.

AMA Style

Moran CJ, Seielstad CA, Cunningham MR, Hoff V, Parsons RA, Queen L, Sauerbrey K, Wallace T. Deriving Fire Behavior Metrics from UAS Imagery. Fire. 2019; 2(2):36.

Chicago/Turabian Style

Moran, Christopher J., Carl A. Seielstad, Matthew R. Cunningham, Valentijn Hoff, Russell A. Parsons, LLoyd Queen, Katie Sauerbrey, and Tim Wallace. 2019. "Deriving Fire Behavior Metrics from UAS Imagery" Fire 2, no. 2: 36.

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