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A two-stage hierarchical Bayesian model is proposed to estimate forestbiomass density and total given sparsely sampled LiDAR and georeferenced forestinventory plot measurements. The model is motivated by the United StatesDepartment of Agriculture (USDA) Forest Service Forest Inventory and Analysis(FIA) objective to provide biomass estimates for the remote Tanana InventoryUnit (TIU) in interior Alaska. The proposed model yields stratum-level biomassestimates for arbitrarily sized areas. Model-based estimates are compared withthe TIU FIA design-based post-stratified estimates. Model-based small areaestimates (SAEs) for two experimental forests within the TIU are compared witheach forest's design-based estimates generated using a dense network ofindependent inventory plots. Model parameter estimates and biomass predictionsare informed using FIA plot measurements, LiDAR data that is spatially alignedwith a subset of the FIA plots, and complete coverage remotely sensed data usedto define landuse/landcover stratum and percent forest canopy cover. Resultssupport a model-based approach to estimating forest variables when inventorydata are sparse or resources limit collection of enough data to achieve desiredaccuracy and precision using design-based methods.

Models to support forest inventory and small area estimation using sparsely sampled LiDAR: A case study involving G-LiHT LiDAR in Tanana, Alaska