Theses and Dissertations

ORCID

https://orcid.org/0009-0000-9746-0553

Advisor

Poudel, Krishna P.

Committee Member

McConnell, Thomas Eric

Committee Member

Headlee, William L.

Date of Degree

8-13-2024

Original embargo terms

Immediate Worldwide Access

Document Type

Graduate Thesis - Open Access

Major

Forestry

Degree Name

Master of Science (M.S.)

College

College of Forest Resources

Department

Department of Forestry

Abstract

Current carbon and bioenergy markets shifted the focus of typical forest attribute estimation from volume to biomass. We used multiple linear regression and the dataset collected as part of the National Scale Volume and Biomass modeling effort to develop biomass prediction models for Pinus taeda L., Pinus elliottii Engelm. var. elliottii, Pinus echinata Mill., and Pinus palustris Mill. In addition to utilizing traditional forest measurements such as diameter at breast height and total tree height, biomass was estimated as functions of volume, latitude, and longitude. We also evaluated the differences in wood density by geographic location for these species. The best results were obtained when models were fitted using the combined dataset and a log transformed model. Wood density estimates were improved by including latitude and longitude in the model. These findings will be useful to managers seeking improved biomass yield estimates and density by geographic regions.

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