Deriving Fire Behavior Metrics from UAS Imagery
Deriving Fire Behavior Metrics from UAS Imagery
复制标题
从 UAS 图像中获取火灾行为指标
作者：
C. J. Moran;C. Seielstad;M. Cunningham;Valentijn Hoff;R. Parsons;L. Queen;Katie Sauerbrey;Tim Wallace
廉价无人机系统（UAS）的出现为研究火灾行为和生态系统模式-过程关系创造了新的机会。悬停在火灾上方的旋翼无人机提供了一个静态的、可扩展的传感平台，可以同时表征地形、植被和火灾。在这里，我们提出了使用UAS机载近红外和热红外相机收集复杂火灾行为中火灾蔓延率（RoS）和方向的一致时间序列的方法。我们还开发了一种技术，以确定适当的分析单位，以提高火灾与环境相互作用的统计分析。使用混合温度梯度阈值的方法与数据从两个规定的火灾在干针叶林，方法表征复杂的相互作用，观察到的标题，侧翼，和备份火灾准确。RoS范围为0-2.7 m/s。RoS分布均为重尾正偏态分布，面积加权平均扩散率为0.013-0.404 m/s。可以预见的是，RoS是最高的沿着主要载体的火灾旅行（头火）和较低的沿着侧翼。平均传播方向并不一定遵循主要的头火方向。RoS的空间聚合产生的分析单元平均为原始像素数的3.1-35.4%，突出了大量的复制数据和扩散率对单元大小的强烈影响。
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.
植被火灾火焰发射率中波红外和长波红外灰体假设的实验验证
影响因子：
3.1
作者：
Johnston J
通讯作者：
Johnston J
Smokey 成熟了：用于消防管理的无人机系统
影响因子：
10.3
作者：
Twidwell, Dirac;Allen, Craig R;Detweiler, Carrick;Higgins, James;Laney, Christian;Elbaum, Sebastian
通讯作者：
Elbaum, Sebastian