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：本研究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清查样地测量值条件下估算森林生物量密度及总量。该模型源自美国农业部（USDA）林务局森林清查与分析（FIA）计划对阿拉斯加内陆偏远塔纳纳清查单元（TIU）进行生物量估算的需求。所提模型可为任意规模感兴趣区域提供分层级生物量估算值，并将模型估算结果与TIU的FIA基于设计的后分层估算值进行比较。同时，将TIU内两处实验林的模型小区域估算值（SAEs）与各森林基于独立密集样地网络的设计估算值进行对比。模型参数估计与生物量预测基于FIA样地实测数据、与部分FIA样地空间对齐的LiDAR数据，以及用于定义土地利用/土地覆盖类型及森林冠层覆盖百分比的连续遥感数据。研究结果表明，当清查数据稀疏或资源限制导致无法通过基于设计方法收集足够数据以达到目标精度时，可采用基于模型的森林变量估算方法。