LINKING ALLOMETRIC SCALING THEORY WITH LIDAR REMOTE SENSING FOR IMPROVED BIOMASS ESTIMATION AND ECOSYSTEM CHARACTERIZATION
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Accurate quantification of forest carbon stocks and fluxes is critical for the successful modeling and mitigation of climate change. This research focuses on forest carbon stock quantification, both in terms of testing emerging remote sensing approaches to forest carbon modeling, and examining allometric equations used to estimate biomass stocks in field plots. First, we test controversial theoretical predictions of forest allometry through the mapping of the allometric variability using field plots across the U.S. we find that there is considerable variability in forest allometry across space, largely driven by local environment and life history. However, in tall forests, allometries tend to converge toward theoretical predictions, suggesting that theory may be a useful constraint on allometry in certain forests. Second, we shift to an analysis of empirical allometries by developing an algorithm to extract individual crown information from forest systems and using it for biomass mapping and allometric equation testing. Third, we test whether individual tree structure bolsters biomass modeling capabilities in comparison to tradition, plot-aggregated LiDAR metrics. As part of this analysis we also test an allometric scaling-based approach to biomass mapping. We find that individual tree-level structure only improves biomass models when there is considerable spatial heterogeneity in the forest. Also, allometric scaling-based only worked in one study site, and failed in the other two sites because there was little or no relationship between basal area and maximum canopy height. Finally, we applied LiDAR datasets to an analysis of the effects of sample size on empirical allometry development. We found that small samples sizes tend to result in an under sampling of large stems, which yields a more linear fit than the true allometry. An assessment of the potential carbon implications of this problem yielded site-level biomass predictions with biases of 10-178%. We suggest that empirical allometric equations developed on small sample sizes, as applied in the U.S., yield potentially large errors in biomass and therefore require careful reassessment. In combination with our findings regarding the spatial variability of forest allometry, we believe that the limiting factor to forest carbon estimation is the use of allometric equations.