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September 2009 Hierarchical spatial models for predicting tree species assemblages across large domains
Andrew O. Finley, Sudipto Banerjee, Ronald E. McRoberts
Ann. Appl. Stat. 3(3): 1052-1079 (September 2009). DOI: 10.1214/09-AOAS250


Spatially explicit data layers of tree species assemblages, referred to as forest types or forest type groups, are a key component in large-scale assessments of forest sustainability, biodiversity, timber biomass, carbon sinks and forest health monitoring. This paper explores the utility of coupling georeferenced national forest inventory (NFI) data with readily available and spatially complete environmental predictor variables through spatially-varying multinomial logistic regression models to predict forest type groups across large forested landscapes. These models exploit underlying spatial associations within the NFI plot array and the spatially-varying impact of predictor variables to improve the accuracy of forest type group predictions. The richness of these models incurs onerous computational burdens and we discuss dimension reducing spatial processes that retain the richness in modeling. We illustrate using NFI data from Michigan, USA, where we provide a comprehensive analysis of this large study area and demonstrate improved prediction with associated measures of uncertainty.


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Andrew O. Finley. Sudipto Banerjee. Ronald E. McRoberts. "Hierarchical spatial models for predicting tree species assemblages across large domains." Ann. Appl. Stat. 3 (3) 1052 - 1079, September 2009.


Published: September 2009
First available in Project Euclid: 5 October 2009

zbMATH: 1196.62121
MathSciNet: MR2750386
Digital Object Identifier: 10.1214/09-AOAS250

Keywords: Bayesian inference , logistic regression , Markov chain Monte Carlo , spatial predictive process , spatially-varying coefficients , species assemblages

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.3 • No. 3 • September 2009
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