Prediction of Acoustic Velocity Properties of Downed Pine Trees Using Near-Infrared Spectroscopy
Autor: | Munkaila Musah, Abiodun O. Alawode, Javier Hernandez Diaz, Osei Asafu-Adjaye, Tom Gallagher, Maria S. Peresin, Yucheng Peng, Dana Mitchell, Mathew Smidt, Brian Via |
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Rok vydání: | 2023 |
Předmět: | |
Zdroj: | Forest Products Journal. 73:133-141 |
ISSN: | 2376-9637 0015-7473 |
DOI: | 10.13073/fpj-d-22-00067 |
Popis: | Near-infrared reflectance (NIR) spectroscopy was used to determine correlations between acoustic velocity and stiffness properties of downed pine trees in the southern coastal plains of the United States. Three acoustic measurement methods (longitudinal, transverse, and offset) were used. From the measurement of the acoustics, the time of flight (TOF) was determined from the downed trees. Increment core samples were obtained from each thirty downed pine trees in the study. NIR spectra were obtained using a fiber probe on the radial surface of each core to rapidly correlate the speed of sound, estimate the strength properties of the downed trees, and the TOF acoustic assessments. The NIR prediction was very good for the transverse and offset methods. The predictability diagnostic was above an R2 of 0.70 for both offset measurements for the transverse methods for the acoustic velocity and dynamic modulus of elasticity (MOE). The longitudinal measurement exhibited the weakest model (R2 < 0.65) for both the acoustic velocity and the MOE with the highest standard error of prediction between 3.0 (ELVLSWV) and 0.31 (VLSWV) for the three measurement types. All the standard errors of calibration were below 1% except in ELVOSWV, which was ∼2%. The dry density measured from the increment cores had a moderate correlation (R2 ∼ 60%), compared with the lower correlation (R2 ∼ 50%) by the green density in the multiple linear regression output. The results of the acoustic model indicated that NIR spectroscopy has the potential to predict the acoustic velocity and corresponding stiffness of downed trees. |
Databáze: | OpenAIRE |
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