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 of interest. 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 wall-to-wall 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：提出一种两阶段分层贝叶斯模型，用于在稀疏采样LiDAR与地理参考森林清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林务局森林清查与分析项目对阿拉斯加内陆偏远塔纳纳清查单元生物量估算的需求。所提模型可为任意大小的感兴趣区域提供分层级生物量估计值。将基于模型的估算结果与塔纳纳清查单元基于设计的分层后估计值进行比较。同时，针对塔纳纳清查单元内两个实验林，将模型所得小区域估计值与利用密集独立样地网络生成的各林区基于设计估计值进行对比。模型参数估计与生物量预测依托于：林务局清查样地实测数据、与部分样地空间匹配的LiDAR数据、以及用于定义土地利用/土地覆盖分层与林冠覆盖度的全覆盖遥感数据。结果表明，当清查数据稀疏或资源限制导致无法通过基于设计方法采集足够数据以达到预期精度时，基于模型的森林变量估算方法具有可行性。