Hierarchical spatial models for predicting tree species assemblages across large domains. (English) Zbl 1196.62121

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

Ann. Appl. Stat. 3, No. 3, 1052-1079 (2009).

Summary: 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.

MSC:

62M30	Inference from spatial processes
62P12	Applications of statistics to environmental and related topics
62F15	Bayesian inference
62P10	Applications of statistics to biology and medical sciences; meta analysis
65C60	Computational problems in statistics (MSC2010)
65C40	Numerical analysis or methods applied to Markov chains

Keywords:

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

Software:

SemiPar; gss; FRK; BayesDA; clusfind

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