Deriving fire behavior metrics from UAS imagery

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A rotor-wing unmanned aerial system (UAS) hovering above a fire provides a static, scalable sensing platform that can characterize terrain, vegetation, and fire coincidently. This study presents 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. 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.

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