Light Detection and Ranging (LiDAR) data has shown great potential to estimate spatially explicit forest variables, including above-ground biomass, stem density, tree height, and more. Due to its ability to garner information about the vertical and horizontal structure of forest canopies effectively and efficiently, LiDAR sensors have played a key role in the development of operational air and space-borne instruments capable of gathering information about forest structure at regional, continental, and global scales. Combining LiDAR datasets with field-based validation measurements to build predictive models is becoming an attractive solution to the problem of quantifying and mapping forest structure for private forest land owners and local, state, and federal government entities alike. As with any statistical model using spatially indexed data, the potential to violate modeling assumptions resulting from spatial correlation is high. This thesis explores several different modeling frameworks that aim to accommodate correlation structures within model residuals. The development is motivated using LiDAR and forest inventory datasets. Special attention is paid to estimation and propagation of parameter and model uncertainty through to prediction units. Inference follows a Bayesian statistical paradigm. Results suggest the proposed frameworks help ensure model assumptions are met and prediction performance can be improved by pursuing spatially enabled models.
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