Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables

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• 作者：Andrew O. Finley^a ; ; Sudipto Banerjee^b ; Yuzhen Zhou^c ; Bruce D. Cook^d ; Chad Babcock^e
• 关键词：Forest biomass ; Uncertainty quantification ; Functional analysis ; Dimension reduction ; Predictive process ; Hierarchical models ; Markov chain Monte Carlo
• 刊名：Remote Sensing of Environment
• 出版年：2017
• 出版时间：1 March 2017
• 年：2017
• 卷：190
• 期：Complete
• 页码：149-161
• 全文大小：2280 K
• 卷排序：190

文摘

Proposed joint model for high-dimensional LiDAR and forest variables Yields LiDAR and forest variable prediction at partially and unobserved locations Automatic LiDAR variable selection to explain variability in AGB Bayesian modeling paradigm provides full uncertainty quantification. Explicit spatial modeling of within and among LiDAR signals and forest variables