Hierarchical spatial models for predicting tree species assemblages across large domains

TL;DR

This paper develops hierarchical spatial multinomial logistic models that combine forest inventory data with environmental variables to accurately predict forest types across large regions, addressing computational challenges with dimension reduction.

Contribution

It introduces a novel spatially-varying multinomial logistic modeling approach that enhances forest type prediction accuracy at large scales, incorporating dimension reduction techniques.

Findings

Improved prediction accuracy of forest types in Michigan.

Effective incorporation of spatial associations and environmental predictors.

Demonstrated computational feasibility with dimension reduction.

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.