Wildland fire management using near real time high resolution remote sensing data

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The goal of this project is to develop standards and methods to collect and analyze data to generate a better understanding of topography, fuel and weather around wildland fires to support fire management and firefighting decisions. Drones, supplemented by satellite imagery, will be used to collect visual and multispectral vegetation (fuel) data. Machine learning methods will be used to infer vegetation characteristics of interest, including tree species, size, shape, connectivity, density, surface fuel composition, crown fuel characteristics and ladder fuel distributions. Using this data, better fire behavior predictions will be made which will allow for better management of wildland fires in Canada. Specifically, the project explores four applications of drones for improved wildland fire planning and operational decision making (1) use collected data around the perimeter of a community at risk to wildland fires to assess closeness and density of trees and vegetation, which is used for fire risk assessments, (2) fly drones to structures ahead of wildland fires to perform field exposure assessments and make structure triage decisions during active fires, (3) improve prediction of fire spread and direction, and (4) use collected data to inform improved decision making on deployment of suppression activities such as airtankers, waterbombers, helicopters and ground crews.