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Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

International Journal of Applied Earth Observation and Geoinformation

By:
, , ,
DOI: 10.1016/j.jag.2012.04.007

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Abstract

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Geospatial Extents

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets
Series title:
International Journal of Applied Earth Observation and Geoinformation
DOI:
10.1016/j.jag.2012.04.007
Volume
22
Year Published:
2013
Language:
English
Publisher:
Elsevier
Publisher location:
Amsterdam, Netherlands
Contributing office(s):
Southwest Biological Science Center
Description:
14 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
International Journal of Applied Earth Observation and Geoinformation
First page:
147
Last page:
160
Country:
United States
State:
Maine
Other Geospatial:
Penobscot Experimental Forest