Metabolic scaling theory and remote sensing to model large-scale patterns of forest biophysical properties
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Advanced understanding of the global carbon budget requires large-scale and long-term information on forest carbon pools and fluxes. In situ and remote sensing measurements have greatly enhanced monitoring of forest carbon dynamics, but incomplete data coverage in space and time results in significant uncertainties in carbon accounting. Although theoretical and mechanistic models have enabled continental-scale and global mapping, robust predictions of forest carbon dynamics are difficult without initialization, adjustment, and parameterization using observations. Therefore, this dissertation is focused on a synergistic combination of lidar measurements and modeling that incorporates biophysical principles underlying forest growth. First, spaceborne lidar data from the Geoscience Laser Altimeter System (GLAS) were analyzed for monitoring and modeling of forest heights over the U.S. Mainland. Results showed the best GLAS metric representing the within-footprint heights to be dependent on topography. Insufficient data sampling by the GLAS sensor was problematic for spatially-complete carbon quantification. A modeling approach, called Allometric Scaling and Resource Limitations (ASRL), successfully alleviated this problem. The metabolic scaling theory and water-energy balance equations embedded within the model also provided a generalized mechanistic understanding of valid relationships between forest structure and geo-predictors including topographic and climatic variables. Second, the ASRL model was refined and applied to predict large-scale patterns of forest structure. This research successfully expanded model applicability by including eco-regional and forest-type variations, and disturbance history. Baseline maps (circa 2005; 1-km2 grids) of forest heights and aboveground biomass were generated over the U.S. Mainland. The Pacific Northwest/California forests were simulated as the most favorable region for hosting large trees, consistent with observations. Through sensitivity and uncertainty analyses, this research found that the refined ASRL model showed promise for prognostic applications, in contrast to conventional black-box approaches. The model predicted temporal evolution of forest carbon stocks during the 21st century. The results demonstrate the effects of CO2 fertilization and climate feedbacks across water- and energy-limited environments. This dissertation documents the complex mechanisms determining forest structure, given availability of local resources. These mechanisms can be used to monitor and forecast forest carbon pools in combination with satellite observations to advance our understanding of the global carbon cycle.