  A two-stage hierarchical Bayesian model is developed and implemented to
estimate forest biomass density and total given sparsely sampled LiDAR and
georeferenced forest inventory plot measurements. The model is motivated by the
United States Department of Agriculture (USDA) Forest Service Forest Inventory
and Analysis (FIA) objective to provide biomass estimates for the remote Tanana
Inventory Unit (TIU) in interior Alaska. The proposed model yields
stratum-level biomass estimates for arbitrarily sized areas. Model-based
estimates are compared with the TIU FIA design-based post-stratified estimates.
Model-based small area estimates (SAEs) for two experimental forests within the
TIU are compared with each forest's design-based estimates generated using a
dense network of independent inventory plots. Model parameter estimates and
biomass predictions are informed using FIA plot measurements, LiDAR data that
are spatially aligned with a subset of the FIA plots, and complete coverage
remotely detected data used to define landuse/landcover stratum and percent
forest canopy cover. Results support a model-based approach to estimating
forest variables when inventory data are sparse or resources limit collection
of enough data to achieve desired accuracy and precision using design-based
methods.
