Online Program Home
My Program

Abstract Details

Activity Number: 457 - Statistical Methods for Remote Sensing Data
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract #326780 Presentation
Title: Joint Hierarchical Models for Sparsely Sampled High-Dimensional LiDAR and Forest Variables
Author(s): Andrew Oliver Finley* and Hans-Erik Andersen and Sudipto Banerjee and Bruce Douglas Cook and Abhi Datta and Douglas C Morton
Companies: Michigan State University and USDA Forest Service and UCLA School of Public Health and NASA Goddard Space Flight Center and Johns Hopkins Bloomberg School of Public Health and NASA Goddard Space Flight Center
Keywords: Gaussian Process; Nearest Neighbor Gaussian Process; forestry; MCMC; parallelization; carbon cycle

Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide 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 forest variables 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 prespecified locations. Key challenges we obviate using a Nearest Neighbor Gaussian Process (NNGP) include misalignment between the forest variable observations and LiDAR signals and the high-dimensionality in the model emerging from LiDAR signals in conjunction with the large number of spatial locations.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program