The Annals of Applied Statistics

Hierarchical spatial models for predicting tree species assemblages across large domains

Andrew O. Finley, Sudipto Banerjee, and Ronald E. McRoberts

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Appl. Stat. Volume 3, Number 3 (2009), 1052-1079.

Dates
First available in Project Euclid: 5 October 2009

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1254773278

Digital Object Identifier
doi:10.1214/09-AOAS250

Zentralblatt MATH identifier
05758451

Mathematical Reviews number (MathSciNet)
MR2750386

Citation

Finley, Andrew O.; Banerjee, Sudipto; McRoberts, Ronald E. Hierarchical spatial models for predicting tree species assemblages across large domains. Ann. Appl. Stat. 3 (2009), no. 3, 1052--1079. doi:10.1214/09-AOAS250. http://projecteuclid.org/euclid.aoas/1254773278.


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