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.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-7245 |
| Date | 13 August 2024 |
| Creators | Driskill, Chris |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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