Statistical advances in forest modeling

Forest management and production increasingly require estimates of forest variables at fine spatial resolutions. National Forest Inventory (NFI) data provide a robust resource to inform local management objectives, yet classical approaches are not well-suited to do so. We develop advanced statistical frameworks for leveraging NFI data to provide insights on forest parameters at management-relevant scales.

We developed a multivariate spatial Bayesian model to generate more precise estimates of forest parameters by species or species groups. We applied our approach for estimating county-level biomass across the U.S. Southeast for the 20 most common species in the region Doser et al. 2025 Forest Ecology and Management.

Active projects in the lab include building spatially-explicit height diameter models using Forest Inventory and Analysis data and incorporating the Forest Vegetation Simulator within a Bayesian framework for generating stochastic estimates of growth and yield.