To maximize utilization of our forest resources, detailed knowledge of wood property variation and the impacts this has on end-product performance is required at multiple scales (within and among trees, regionally). As many wood properties are difficult and time-consuming to measure our knowledge regarding their variation is often inadequate as is our understanding of their responses to genetic and silvicultural manipulation. The emergence of many non-destructive evaluation (NDE) methodologies offers the potential to greatly enhance our understanding of the forest resource; however, it is critical to recognize that any technique has its limitations and it is important to select the appropriate technique for a given application. In this review, we will discuss the following technologies for assessing wood properties both in the field: acoustics, Pilodyn, Resistograph and Rigidimeter and the lab: computer tomography (CT) scanning, DiscBot, near infrared (NIR) spectroscopy, radial sample acoustics and SilviScan. We will discuss these techniques, explore their utilization, and list applications that best suit each methodology. As an end goal, NDE technologies will help researchers worldwide characterize wood properties, develop accurate models for prediction, and utilize field equipment that can validate the predictions. The continued advancement of NDE technologies will also allow researchers to better understand the impact on wood properties on product performance.

Near-infrared (NIR) spectroscopy provides a rapid alternative to traditional methods of wood property assessment. For organizations who assess wood properties on a large scale, multisite, multispecies calibrations are of practical interest. We examined NIR spectroscopy for the estimation of density (at 12% moisture content), modulus of elasticity (MOE), and modulus of rupture (MOR) using clear wood samples obtained from several pine species (Pinus caribaeavar. bahamensis, var.hondurensis, and var. caribaea, P.chiapensis,P.maximinoi,P.oocarpa,P.taeda, andP.tecunumanii). We compared different methodologies for collecting spectra, that is, benchtop instrument versus benchtop fiber-optic probe and field portable fiber-optic probe, and different wood surfaces (radial and transverse). Calibrations based on the benchtop instrument were superior to those obtained using the fiber-optic probe systems. Difficulty with adequately representing the sample when collecting spectra using a fiber-optic probe and lower quality spectra explain the differences among the data sets. Spectra collected from radial and transverse surfaces provided similar calibration statistics. The calibrations obtained for density (R² = 0.81, SECV = 38.5 kg/m³) and MOE (R² = 0.81, SECV = 1124 GPa) using benchtop instrument spectra demonstrate that it is possible to obtain general calibrations for estimating the wood properties of a number of tropical, subtropical, and temperate pine species.

Hyperspectral imaging is a technique that combines spectroscopy and imaging. Originally utilized in the 1980’s by the remote sensing community it is now utilized in a wide variety of applications. Spectral imaging was first used for the detection of compression wood in the late 1990’s and since that time research focused on wood and wood products has steadily increased with a variety of applications reported. While there are several reviews of wood related research utilizing near infrared spectrometers a comprehensive summary of wood—hyperspectral imaging research is lacking. Near infrared hyperspectral imaging systems (NIR-HSI) typically have a wavelength range of 900–1700 nm, whereas short-wave infrared hyperspectral imaging (SWIR-HSI) systems range from 1000 to 2500 nm. We provide a detailed account of the various studies that have been published utilizing both camera types.

With conifer plantations having an increasingly important role in meeting the fiber needs of society, an understanding of the effect of silvicultural practices on wood quality is critical. The perception of wood quality varies, making it hard to define in a single statement; however, possibly the most succinct definition is “a measure of the aptness of wood for a given use”. In general, properties that have a positive influence on a specific product assist in defining changes in wood quality. Since wood properties exhibit large variability within annual rings, within trees, and among trees in a stand, and have both genetic and environmental components (i.e., vary with different physiographical regions), it is imperative to have an understanding of wood properties at multiple levels. In this paper, we review the typical variation patterns in wood properties of conifers, with specific emphasis on loblolly pine (Pinus taeda L.), and radiata pine (Pinus radiata D.Don), two of the most common conifer plantation species globally. We also describe the impact of conventional silvicultural treatments on wood quality. Modeling efforts to predict variation in wood properties within trees, and in response to silvicultural treatments are also summarized.

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R²) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

Maps developed using Akima’s interpolation method were used to compare patterns of within-tree variation for Pinus taeda L. (loblolly pine) wood properties in plantation-grown trees aged 13 and 22 years. Air-dry density, microfibril angle (MFA) and modulus of elasticity (MOE) maps represented the average of 18 sampled trees in each age class. Near infrared (NIR) spectroscopy models calibrated using SilviScan provided data for the analysis. Zones of high density, low MFA and high MOE wood increased markedly in size in maps of the older trees. The proportion of wood meeting the visually graded No. 1 (11 GPa) and No. 2 (9.7 GPa) MOE design values for southern pine lumber increased from 44 to 74% and from 58 to 83% respectively demonstrating the impact of age on end-product quality. Air-dry density increased from pith to bark at all heights but lacked a significant trend vertically, while radial and longitudinal trends were observed for MFA and MOE. Changes were consistent with the asymptotic progression of properties associated with full maturity in older trees.