稀疏抽样高维LiDAR和森林变量的联合层次模型
Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables
摘要：
遥感技术的最新进展，特别是光探测和测距（LiDAR）传感器，提供了以精细的空间分辨率量化大地理区域森林特征所需的数据。从推理的角度来看，有兴趣在预测和插值的稀疏采样和空间错位激光雷达信号和森林变量。我们提出了一个完全基于过程的贝叶斯分层模型，地上生物量（AGB）和激光雷达信号。基于过程的框架提供了丰富的推理能力，例如，对整个基本过程进行推断，而不是仅在预先指定的点进行估计。我们面临的主要挑战包括AGB观测和LiDAR信号之间的不一致，以及LiDAR信号与大量空间位置相结合产生的模型中的高维性。我们提供模拟实验来评估我们提出的模型，并将其应用于一个具有挑战性的数据集，包括LiDAR和空间重合的森林资源调查变量收集的Penobscot实验森林（PEF），缅因州。我们的主要实质性贡献包括AGB数据产品与PEF的相关不确定性措施，更广泛地说，一种方法，应该在各种当前和即将到来的森林变量绘图工作中使用稀疏采样的遥感高维数据。
英文摘要：
Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide the data needed to quantify forest characteristics at a fine spatial resolution over large geographic domains. From an inferential standpoint, there is interest in prediction and interpolation of the often sparsely sampled and spatially misaligned LiDAR signals and forest variables. We propose a fully process-based Bayesian hierarchical model for above ground biomass (AGB) and LiDAR signals. The process-based framework offers richness in inferential capabilities, e.g., inference on the entire underlying processes instead of estimates only at pre-specified points. Key challenges we obviate include misalignment between the AGB observations and LiDAR signals and the high-dimensionality in the model emerging from LiDAR signals in conjunction with the large number of spatial locations. We offer simulation experiments to evaluate our proposed models and also apply them to a challenging dataset comprising LiDAR and spatially coinciding forest inventory variables collected on the Penobscot Experimental Forest (PEF), Maine. Our key substantive contributions include AGB data products with associated measures of uncertainty for the PEF and, more broadly, a methodology that should find use in a variety of current and upcoming forest variable mapping efforts using sparsely sampled remotely sensed high-dimensional data.
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