Date of Award

Spring 2025

Project Type

Thesis

Program or Major

Natural Resources

Degree Name

Master of Science

First Advisor

Mark J. Ducey

Second Advisor

John S. Gunn

Third Advisor

Olivia L. Fraser

Abstract

The goal of this study was to determine whether TreeMap 2016, a forest model created by Riley et al. (2022a), is appropriate for fine-scale forestry applications in the northeastern United States.The integration of remotely sensed data and forest inventories to create multi-source forest inventories has become standard practice in many countries around the world. Wall-to-wall models of terrestrial biomass are of particular interest in light of the role of forests in carbon sequestration. TreeMap 2016 makes tree-level predictions of forest conditions across the U.S. at a 30x30-meter pixel resolution using USFS Forest Inventory and Analysis data. TreeMap has been used in the public and private sectors for estimating terrestrial carbon stocks, wildland fire fuel treatment and snag hazard mapping, and for preliminary summaries of forest characteristics. I compared TreeMap-derived estimates of common forest metrics including basal area, stand diameter, stand height, and forest carbon to ground estimates from two study sites in the White Mountain National Forest in New Hampshire: Bartlett Experimental Forest, a 3530-acre site with forests representative of typical northern New England, and the Wonalancet Bowl, a 70-acre site within a rare stand of old growth northern hardwoods. Equivalence between ground and model data was tested at plot, compartment (management units within Bartlett ranging from 20-220 acres) and property levels. Ground and model data were not equivalent at plot or compartment level for any of the variables tested at either study site, and only one variable per study site agreed at the property level. Fractional savings of using TreeMap as a sampling covariate, calculated from correlation coefficients, were very low for all variables except forest composition (percent hardwood versus softwood) at Bartlett. TreeMap was also unable to predict old growth habitat. It was concluded that TreeMap, while appropriate for broad scale applications, is not sufficiently accurate to be used for finer-scale forestry applications in the northeastern U.S.

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