Canopy Fuels Inventory and Mapping Using Large-Footprint Lidar

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This dissertation explores the efficacy of large-footprint, waveform-digitizing lidar for the inventory and mapping of canopy fuels for utilization in fire behavior simulation models. Because of its ability to measure the vertical structure of forest canopies lidar is uniquely suited among remote sensing instruments to observe the canopy structure characteristics relevant to fuels characterization and may help address the lack of high-quality fuels data for many regions, especially in more remote areas. Lidar data were collected by the Laser Vegetation Imaging Sensor (LVIS) over the Sierra National Forest in California. Various waveform metrics were calculated from the waveforms. Field data were collected at 135 plots co-located with a subset of the lidar footprints. The field data were used to calculate ground-based observations of canopy bulk density (CBD) and canopy base height (CBH). These observed values of CBD and CBH were used as dependent variables in a series of regression analyses using the derived lidar metrics as independent variables. Comparisons of observed and predicted resulted in an r2 of 0.71 for CBD and an r2 of 0.59 for CBH. These regression models were then used to generate grids of CBD and CBH from all of the lidar waveform data in the study area. These grids, along with lidar-derived grids of canopy height, were then used as inputs to the FARSITE (Fire Area Simulator-Model) fire behavior model in a series of simulations. Comparisons between conventionally derived and lidar-based model inputs showed differences between the two sets of data. Specifically, the lidar-derived inputs contained much more spatial heterogeneity. Outputs from FARSITE using the lidar-derived inputs were also compared to outputs using input maps of CBD and CBH generated from field observations. There were significant differences between the two sets of outputs, especially in the frequency and spatial distribution of crown fire. Experiments in manipulating the effective resolution of the lidar-based inputs confirmed that FARSITE outputs are affected by the spatial variability of the input data. Furthermore, a sensitivity analysis demonstrated that FARSITE is sensitive to potential errors in the canopy structure input grids. The results of this dissertation show that lidar can be used effectively to predict CBD and CBH for the purpose of fire behavior modeling and that investment in these lidar-based canopy structure data is worthwhile, especially for forests characterized by significant heterogeneity. This work affirms that lidar is a useful tool for future canopy fuels inventory and mapping.