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Acoustic-Based Non-Destructive Estimation of Wood Quality Attributes within Standing Red Pine Trees
The relationship between acoustic velocity (vd) and the dynamic modulus of elasticity (me), wood density (wd), microfibril angle, tracheid wall thickness (wt,), radial and tangential diameters, fibre coarseness (co) and specific surface area (sa), within standing red pine (Pinus resinosa Ait.) trees, was investigated. The data acquisition phase involved 3 basic steps: (1) random selection of 54 sample trees from 2 intensively-managed 80-year-old plantations in central Canada; (2) attainment of cardinal-based vd measurements transecting the breast-height position on each sample tree; and (3) felling, sectioning and obtaining cross-sectional samples from the first 5.3 m sawlog from which Silviscan-based area-weighted mean attribute estimates were determined. The data analysis phase consisted of applying graphical and correlation analyses to specify regression models for each of the 8 attribute-acoustic velocity relationships. Results indicated that viable relationships were obtained for me, wd, wt, co and sa based on a set of statistical measures: goodness-of-fit (42%, 14%, 45%, 27% and 43% of the variability explained, respectively), lack-of-fit (unbiasedness) and predictive precision (±12%, ±8%, ±7%, ±8% and ±6% error tolerance intervals, respectively). Non-destructive approaches for estimating the prerequisite wd value when deploying the analytical framework were also empirically evaluated. Collectively, the proposed approach and associated results provide the foundation for the development of a comprehensive and precise end-product segregation strategy for use in red pine management.
Acoustic-Based Non-Destructive Estimation of Wood Quality Attributes within Standing Red Pine Trees
The relationship between acoustic velocity (vd) and the dynamic modulus of elasticity (me), wood density (wd), microfibril angle, tracheid wall thickness (wt,), radial and tangential diameters, fibre coarseness (co) and specific surface area (sa), within standing red pine (Pinus resinosa Ait.) trees, was investigated. The data acquisition phase involved 3 basic steps: (1) random selection of 54 sample trees from 2 intensively-managed 80-year-old plantations in central Canada; (2) attainment of cardinal-based vd measurements transecting the breast-height position on each sample tree; and (3) felling, sectioning and obtaining cross-sectional samples from the first 5.3 m sawlog from which Silviscan-based area-weighted mean attribute estimates were determined. The data analysis phase consisted of applying graphical and correlation analyses to specify regression models for each of the 8 attribute-acoustic velocity relationships. Results indicated that viable relationships were obtained for me, wd, wt, co and sa based on a set of statistical measures: goodness-of-fit (42%, 14%, 45%, 27% and 43% of the variability explained, respectively), lack-of-fit (unbiasedness) and predictive precision (±12%, ±8%, ±7%, ±8% and ±6% error tolerance intervals, respectively). Non-destructive approaches for estimating the prerequisite wd value when deploying the analytical framework were also empirically evaluated. Collectively, the proposed approach and associated results provide the foundation for the development of a comprehensive and precise end-product segregation strategy for use in red pine management.
Acoustic-Based Non-Destructive Estimation of Wood Quality Attributes within Standing Red Pine Trees
Peter F. Newton (Autor:in)
2017
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
dynamic modulus of elasticity , wood density , microfibril angle , tracheid wall thickness , radial and tangential tracheid diameters , fibre coarseness and specific surface area , Silviscan-3 , time-of-flight , regression analysis , micro-drill resistance , predictive accuracy , Poisson ratio , Plant ecology , QK900-989
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