Author:
Musah Munkaila,Alawode Abiodun O.,Diaz Javier Hernandez,Asafu-Adjaye Osei,Gallagher Tom,Peresin Maria S.,Peng Yucheng,Mitchell Dana,Smidt Mathew,Via Brian
Abstract
AbstractNear-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.
Subject
Plant Science,General Materials Science,Forestry
Reference82 articles.
1. Alves, A., Santos, A. Rozenberg, P. Pâques, L. E. Charpentier, J. P. Schwanninger, M. and Rodrigues.J. 2012. A common near infrared–based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix× eurolepis. Wood Sci. Technol. 46(1–3): 157– 175.
2. Baillères, H., Davrieux, F. and Ham-Pichavant.F. 2002. Near infrared analysis as a tool for rapid screening of some major wood characteristics in a eucalyptus breeding program. Ann. Forest Sci. 59(5–6): 479– 490.
3. Batten, G. D. 1998. An appreciation of the contribution of NIR to agriculture. J. Near Infrared Spectrosc. 6(1): 105– 114.
4. Beaulieu, J., and Dutilleul.P. 2019. Applications of computed tomography (CT) scanning technology in forest research: A timely update and review. Can. J. Forest Res. 49(10): 1173– 1188.
5. Belonger, P., McKeand, S. Jett, J. and White.T. 1997. Wood density assessment of diverse families of loblolly pine using X-ray densitometry. In:Proceedings of the 24th Southern Forest Tree Improvement Conference, T. White, D. Huber, and G. Powel (Eds.), June 9–12, 1997, Orlando, Florida; US Department of Commerce, Springfield, Virginia. pp. 133–142.