HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS

• 成果类型: Article
• 署名作者: Finley, Andrew O.; Banerjee, Sudipto; McRoberts, Ronald E.
• 署名单位: Michigan State University; Michigan State University; University of Minnesota System; University of Minnesota Twin Cities; United States Department of Agriculture (USDA); United States Forest Service
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/09-AOAS250
• 发表日期: 2009
• 页码: 1052-1079
• 关键词: logistic-regression; data sets; CONSTRUCTION; likelihood; algorithm; units

摘要

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.