A two-stage hierarchical Bayesian model is proposed 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 is spatially aligned with a subset of the FIA plots, and complete coverage remotely sensed 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.

翻译：本文提出一种两阶段分层贝叶斯模型，用于在稀疏采样LiDAR数据与地理参考森林普查样地测量数据条件下估计森林生物量密度及总量。该模型源于美国农业部（USDA）林务局森林普查与分析（FIA）项目对阿拉斯加内陆偏远的塔纳纳普查单元（TIU）提供生物量估算的需求。所提模型可生成任意规模区域的层水平生物量估计值。将基于模型的估计结果与TIU FIA基于设计的后分层估计值进行对比。针对TIU内两个实验森林的基于模型的小区域估计值（SAEs），分别与各森林利用密集独立样地网络生成的基于设计估计值进行对比。模型参数估计与生物量预测以FIA样地测量数据、与FIA样地子集空间配准的LiDAR数据，以及用于定义土地利用/土地覆盖层和森林冠层覆盖百分比的完整覆盖遥感数据为支撑。结果表明，在普查数据稀疏或资源限制导致无法通过基于设计方法获取足够数据以达到期望精度时，基于模型的估计方法可有效支撑森林变量估算。