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Strong regression relationships between light detection and ranging (LIDAR) metrics and indices of forest structure have been reported in the literature. However, most papers focus on empirical results and do not consider LIDAR metric selection and biological interpretation explicitly. In this study, three different variable selection methods (stepwise regression, principle component analysis [PCA], and Bayesian modeling averaging [BMA]) were compared using LIDAR data from three study sites: Capitol Forest in western Washington State, Mission Creek in central Washington State, and Kenai Peninsula in south central Alaska. Separate aboveground biomass regression models were developed for each site as well as common models using three study sites simultaneously. Final biomass models have R 2 values ranging from 0.67 to 0.88 for three study sites. PCA indicates that three LIDAR metrics (mean height, coefficient variation of height, and canopy LIDAR point density) explain the majority of variation contained within a larger set of metrics. Within each study area, forest biomass models using these three predictor variables had similar R 2 values as the stepwise and BMA regression models. Individual site models using these three variables are recommended because these models are straightforward in terms of model form and biological interpretation and are easily adopted for application.

A Comparison of Statistical Methods for Estimating Forest Biomass from Light Detection and Ranging Data