We calibrated upward sensing profiling and downward sensing scanning LiDAR systems to estimates of canopy fuel loading developed from field plots and allometric equations, and then used the LiDAR datasets to predict canopy bulk density (CBD) and crown fuel weight (CFW) in wildfire prone stands in the New Jersey Pinelands. LiDAR-derived height profiles were also generated in 1-m layers, and regressed on CBD estimates calculated for 1-m layers from field plots to predict three-dimensional canopy fuel loading. We then produced maps of canopy fuel metrics for three 9 km2 forested areas in the Pinelands.
Correlations for standard LiDAR-derived parameters between the two LiDAR systems were all highly significant, with correlation coefficients ranging between 0.82 and 0.98. Stepwise linear regression models developed from the profiling LiDAR data predicted maximum CBD and CFW (r2 = 0.94 and 0.92) better than those developed from the scanning LiDAR data (r2 = 0.83 and 0.71, respectively). A single regression for the prediction of CBD at all canopy layers had r2 values of 0.93 and 0.82 for the profiling and scanning datasets, respectively. Individual bin regressions for predicting CBD at each canopy height layer were also highly significant at most canopy heights, with r2 values for each layer ranging between 0.36 and 0.89, and 0.44 and 0.99 for the profiling and scanning datasets, respectively. Relationships were poorest mid-canopy, where highest average values and highest variability in fuel loading occurred. Fit of data to Gaussian distributions of canopy height profiles facilitated a simpler expression of these parameters for analysis and mapping purposes, with overall r2 values of 0.86 and 0.92 for the profiling and scanning LiDAR datasets, respectively. Our research demonstrates that LiDAR data can be used to generate accurate, three-dimensional representations of canopy structure and fuel loading at high spatial resolution by linking 1-m return height profiles to biometric estimates from field plots.