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Due to climate change and historic land management practices, wildfires are becoming more prevalent, with increases in fire sizes, frequencies, and intensities. As a result, there is an increased need to accurately understand the environmental drivers of these wildfires, as well as their impacts. The primary objective of this dissertation was to meet this need by developing a suite of workflows that examine the utility provided by newly available remote sensing data that provide greater levels of detail than conventional data. This research was divided between pre- and post-fire management stages. The post-fire research questions evaluated the ability of remote sensing data acquired from cost-effective, remotely piloted aerial systems (RPAS) to create detailed pixel- and individual tree-based fire severity maps. The data used to produce these maps were acquired exclusively post-fire and included high resolution orthomosaics and dense digital aerial photogrammetric (DAP) point clouds. The pixel-based fire severity maps were produced at 5- and 1-meter resolutions using a supervised random forest classification approach, whereas the individual tree-approach estimated crown scorch height across segmented trees that were classified as having fire damage. The pre-fire questions were focused on characterizing the amount and arrangement of combustible fuels that drive wildfire behaviour, and did so at the plot- and individual tree-level. The plot-level approach estimated a full suite of canopy and surface fuel metrics that are commonly used in fire behaviour models. Due to the complex nature of these fuels, these estimations were carried out separately using RPAS-acquired light detection and ranging (LiDAR) and DAP point clouds, as well as ground-based mobile laser scanning (MLS) point clouds. The individual tree-based fuel characterizations were carried out by automatically segmenting and evaluating continuous portions of crown fuels from RPAS LiDAR point clouds and comparing against measurements collected from more detailed MLS point clouds. Through the investigation of these research questions, I provide detailed information that describes both the strengths and weaknesses of these data as well as a suite of methodologies that can be incorporated in conventional and next generation forest management frameworks.

The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster���s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m2 and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.