  The goal of this research was to develop and examine the performance of a
geostatistical coregionalization modeling approach for combining field
inventory measurements, strip samples of airborne lidar and Landsat-based
remote sensing data products to predict aboveground biomass (AGB) in interior
Alaska's Tanana Valley. The proposed modeling strategy facilitates pixel-level
mapping of AGB density predictions across the entire spatial domain.
Additionally, the coregionalization framework allows for statistically sound
estimation of total AGB for arbitrary areal units within the study area---a key
advance to support diverse management objectives in interior Alaska. This
research focuses on appropriate characterization of prediction uncertainty in
the form of posterior predictive coverage intervals and standard deviations.
Using the framework detailed here, it is possible to quantify estimation
uncertainty for any spatial extent, ranging from pixel-level predictions of AGB
density to estimates of AGB stocks for the full domain. The lidar-informed
coregionalization models consistently outperformed their counterpart lidar-free
models in terms of point-level predictive performance and total AGB precision.
Additionally, the inclusion of Landsat-derived forest cover as a covariate
further improved estimation precision in regions with lower lidar sampling
intensity. Our findings also demonstrate that model-based approaches that do
not explicitly account for residual spatial dependence can grossly
underestimate uncertainty, resulting in falsely precise estimates of AGB. On
the other hand, in a geostatistical setting, residual spatial structure can be
modeled within a Bayesian hierarchical framework to obtain statistically
defensible assessments of uncertainty for AGB estimates.
