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

该项目的目标是制定收集和分析数据的标准和方法，以更好地了解荒野火灾周围的地形、燃料和天气，以支持火灾管理和消防决策。无人机辅以卫星图像，将用于收集视觉和多光谱植被(燃料)数据。将使用机器学习方法来推断感兴趣的植被特征，包括树木种类、大小、形状、连通性、密度、地表燃料组成、树冠燃料特性和梯形燃料分布。使用这些数据，将做出更好的火灾行为预测，这将允许更好地管理加拿大的野地火灾。具体地说，该项目探索了无人机在改进野地火灾规划和业务决策方面的四个应用：(1)使用收集的数据，在面临野火风险的社区周围评估树木和植被的接近程度和密度，用于火灾风险评估；(2)在野火发生期间，将无人机飞到野地火灾前的建筑物，以执行实地暴露评估并作出结构分类决策；(3)改进火势蔓延和方向的预测；以及(4)使用收集的数据，为改进部署扑救活动的决策提供信息，如空中加油机、水上轰炸机、直升机和地勤人员。

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.