Real-time and non-destructive characterization of wood log properties using near-infrared and ground penetrating radar sensors
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Date
2014
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University of New Brunswick
Abstract
Three sensors were tested for non-destructive and real-time monitoring of wood log
moisture content (MC), air-dry density and basic specific gravity (BSG): a near-infrared
(NIR) spectrometer, a time-resolved NIR instrument and a ground penetrating radar
(GPR). Statistical and physical models were established to relate the NIR and GPR data
to the wood properties investigated. The physical models involved the computation of
intermediate physical properties: the optical coefficients and the relative permittivity for
the NIR and GPR models, respectively. Additionally, the ability of these sensors to
characterize properties from thawed and frozen wood, softwood and hardwood, sapwood
and heartwood as well as from the cross section (CS) and through the bark (TB) of the
logs was also investigated. The best estimation accuracies were achieved with the
statistical models using partial least squares (PLS) regression. MC was best predicted
using the GPR (with a root mean square error [RMSE] of 7% and a coefficient of
determination [R2
] of 0.95), while BSG was best predicted using the NIR spectrometer
(RMSE = 0.019, R2
= 0.78). Air-dry density was estimated with a RMSE of 0.047 g·cm·3
(R2 = 0.56) using time-resolved NIR analysis and one single wavelength (846 nm). The
statistical models were capable of handling the influence of the log state, which was not
taken into account in the physical models. However, the physical models presented are
more easily transferable among sensors. Using the GPR, larger wood volumes are also
sampled than when using the NIR systems. This work demonstrates that portable sensors
based on NIR and GPR technologies could be used to determine MC and BSG of logs in
the field. Such sensors could be used to reduce energy consumption, reduce waste,
increase product quality and decrease production costs in the forest industry.