Paper Info
Title
Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets.
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. (C) 2012 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2013
10.1016/j.jag.2012.04.007
International Journal of Applied Earth Observation and Geoinformation
Keywords
Field
DocType
LiDAR,Hyperspectral,Bayesian hierarchical spatial models,Gaussian Predictive process,Forestry
Random effects model,Data mining,Spatial dependence,Dimensionality reduction,Multivariate statistics,Regression analysis,Forest inventory,Remote sensing,Hyperspectral imaging,Experimental forest,Statistics,Geography
Journal
Volume
ISSN
Citations 
22
0303-2434
5
PageRank 
References 
Authors
0.58
2
4
Name
Order
Citations
PageRank
Andrew Finley1448.09
Sudipto Banerjee25411.54
Bruce D. Cook39314.69
John B. Bradford491.49