Using Acoustic Measurements and Inventory Data to Estimate Stiffness in Standing Douglas-Fir Trees. Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9g54xn15j

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  • Total US lumber production in 2011 was 77.9 million cubic meters. Its primary use was for housing and construction. There is a growing concern that the structural properties for wood are being reduced as trees are harvested at much younger ages as the wood supply shifts from older to younger forests. Goal of this study is to promote the inclusion of wood properties, density and Modulus of Elasticity (MOE) in pre-harvest inventory of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Hitman ST-300, a non-destructive method based on acoustic velocity, was used to estimate MOE. Results from Pearson correlation showed a highly significant relationship between MOE with density (.405), acoustic velocity (.860) and DBH (-.327). A linear model was fitted to estimate MOE as function of acoustic velocity. Slope and intercept are significant for this model (p-value <.001) with an R² of .739. A second linear model was fitted including acoustic velocity and DBH as predictor variables. Slope and intercept are significant for this model (p-value <.001) with a R² of .768. Both models were compared obtaining an increase of.017 when DBH was included in the regression. Monte Carlo simulation was used to determine the impact of a subsample of density with acoustic velocity to determine MOE. It was found that an optimal sample size of ten percent when MOE was estimated using acoustic velocity and wood density cores. Using acoustic, non-destructive, evaluation along with these models can help to operationalize the collection of wood properties that can support the primary log supply chain. It also provides a significant opportunity for foresters to know the condition of the forest and its properties early in the supply chain management.
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