Classifying Wood Properties of Loblolly Pine Grown in Southern Brazil Using NIR-Hyperspectral Imaging

Article Classifying Wood Properties of Loblolly Pine Grown in Southern Brazil Using NIR-Hyperspectral Imaging

Laurence Schimleck 1,*, Jorge L. M. Matos 2 , Antonio Higa 2 , Rosilani Trianoski 2, José G. Prata 2 and Joseph Dahlen 3

1 Department of Wood Science and Engineering, College of Forestry, Oregon State University, Corvallis, OR 97331, USA 2 Department of Wood Science and Technology, Federal University of Paraná, 80.210-170 Curitiba, Brazil; [email protected] (J.L.M.M.); [email protected] (A.H.); [email protected] (R.T.); [email protected] (J.G.P.) 3 Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA; [email protected] * Correspondence: [email protected]

Received: 29 May 2020; Accepted: 16 June 2020; Published: 18 June 2020

Abstract: Loblolly pine (Pinus taeda L.) is one of the most important commercial timber species in the world. While the species is native to the southeastern United States of America (USA), it has been widely planted in southern Brazil, where it is the most commonly planted exotic species. Interest exists in utilizing nondestructive testing methods for wood property assessment to aid in improving the wood quality of Brazilian grown loblolly pine. We used near-infrared hyperspectral imaging (NIR-HSI) on increment cores to provide data representative of the radial variation of families sampled from a 10-year-old progeny test located in Rio Negrinho municipality, Santa Catarina, Brazil. Hyperspectral images were averaged to provide an individual NIR spectrum per tree for cluster analysis (hierarchical complete linkage with square Euclidean distance) to identify trees with similar wood properties. Four clusters (0, 1, 2, 3) were identified, and based on SilviScan data for air-dry density, microfibril angle (MFA), and stiffness, clusters differed in average wood properties. Average ring data demonstrated that trees in Cluster 0 had the highest average ring densities, and those in Cluster 3 the lowest. Cluster 3 trees also had the lowest ring MFAs. NIR-HSI provides a rapid approach for collecting wood property data and, when coupled with cluster analysis, potentially, allows screening for desirable wood properties amongst families in tree improvement programs.

Keywords: density; loblolly pine; microﬁbril angle; near-infrared hyperspectral imaging; nondestructive evaluation; Pinus taeda; SilviScan; stiﬀness; tree improvement; wood quality

1. Introduction Increasingly, plantation forests are being relied upon to supply society with a multitude of ﬁber products. The shift from harvesting native forests to plantations has occurred in many countries, and subsequently, there has been a substantial increase in the total area of plantations globally (estimated to be 278 million ha in 2015, up from 167.5 million ha in 1990) [1]. At 7.83 million ha, Brazil has one of the largest plantation estates in the world and also one of the highest utilization rates (91%) of plantation-grown wood for industrial needs. A number of exotic species are planted in Brazil, with pines representing over 1.6 million ha of the resource [2]. The largest conifer plantation areas are in southern Brazil, with a majority in the states of Paraná (42%), Santa Catarina (34%), Rio Grande do Sul (12%), and São Paulo (8%) [2]. Loblolly pine (Pinus taeda L.), a native of the southeastern United States of America (USA), is the most frequently planted species with a plantation area exceeding one

Forests 2020, 11, 686; doi:10.3390/f11060686 www.mdpi.com/journal/forests Forests 2020, 11, 686 2 of 13 million ha [2]. Demonstrating good frost tolerance and growth, the species is well suited to southern Brazil, and owing to a combination of advanced genetics and silvicultural practices, high rainfall, good soils, and an extended growing season, growth is rapid. For a 16-year rotation, an average mean annual increment (MAI) of 30.1 m3/ha/year for pine in Brazil are reported [2] with an MAI of approximately 40 m3 ha/year with genetically improved trees on exceptional sites [3,4]. In comparison, loblolly pine over its natural range in the SE USA has MAIs of 9–12 m3/ha/year for plantations on a 25-year rotation [5,6], with an estimate of 20 m3/ha/year utilizing optimal silviculture and the best available genetic material [6]. While the growth of loblolly pine in Brazil is impressive, it is widely acknowledged that such dramatic improvements in growth can have a negative influence on wood quality owing to an increase in the proportion of corewood (juvenile wood) for trees of merchantable size [7,8]. The impact of accelerated growth on Brazilian loblolly pine was first reported by Higa et al. [9], who noted decreases in both wood density and stiffness compared to loblolly pine grown in its natural range. More recently Hart [7], in an evaluation of loblolly pine grown in the state of Santa Catarina for the production of unbleached Kraft pulp, reported a decrease in wood density (after adjusting for age differences) compared to loblolly pine used by pulp mills in Alabama, South Carolina, and Texas. Based on 2018 wood consumption data [2], the major forest products’ sectors utilizing plantation conifers include lumber (27.9 million m3), followed by pulp and paper (10.3 million m3), panels (7.4 million m3), and industrial fuelwood (4.0 million m3). With an increasing trend of wood use in construction, demand for domestic softwood lumber is increasing, but based on limits specified in standards, the volume of lumber satisfactory for construction is low. Hence, there is great interest in identifying productive loblolly pine families that can produce wood with acceptable properties. To achieve this outcome, the assessment of wood properties on a large-scale is required. A nondestructive approach, i.e., trees are not cut down for sampling, is desired, and a range of tools exist that make the rapid assessment of wood properties of standing trees possible. Techniques, that include acoustics, near-infrared (NIR) spectroscopy, Rigidimeter, Resistograph, and SilviScan, have all been employed in tree improvement programs for the assessment of wood properties. However, it is important to recognize that the properties assessed, their resolution, cost, and ease of measurement all depend on the nondestructive method utilized [10]. Acoustic tools and the Rigidimeter provide no information regarding radial variation within trees, and Resistograph, which provides a radial trace of resistance to drilling which approximates density variation, has largely been used to provide an estimate of average density for a site or population (Schimleck et al. 2019). In comparison, SilviScan and NIR analysis can provide radial wood property information. However, both rely on increment cores. SilviScan has been used in a wide variety of wood-related research and has greatly assisted in our understanding of wood variability. It has been used in studies of within-tree variation, to assess wood property responses to silvicultural treatments, for the estimation of genetic parameters and genetic markers, to explore relationships between wood properties and product properties, and for the dendrochronological study of environmental effects on wood properties [10]. For the assessment of the radial variation of wood properties based on increment cores, SilviScan is the only nondestructive option that provides high-resolution data. Typically, NIR analysis of cores has involved collecting spectra from the surface of the core at a specific spatial resolution, which has ranged from 10 mm to 1 mm [11–13]. To conduct a cluster analysis, spectra from the surface of individual cores can be averaged to provide a single spectrum representative of the whole core and, as such, incorporates radial variation in wood properties. An alternative approach involves collecting an NIR hyperspectral image (HSI) of the core. As noted by Burger and Gowen [14], “HSI combines spectroscopy and imaging resulting in three-dimensional multivariate data structures (‘hypercubes’). Each pixel in a hypercube contains a spectrum representing its light absorbing and scattering properties. This spectrum can be used to estimate chemical composition and/or physical properties of the spatial region represented by that pixel”. Hence, NIR-HSI provides a rapid approach to collect a large number of spectra from a surface (the hyperspectral image), and Forests 2020, 11, 686 3 of 13 Forests 2020, 11, x FOR PEER REVIEW 3 of 13 hyperspectralall spectra within image) an image, and all can spectra be averaged, within an providing image can a singlebe averaged spectrum, providing that represents a single thespectrum whole thatarea represents scanned. the whole area scanned. NIR-HSINIR-HSI has has recently recently been been used used in many in many studies studies of plant of and plant biological and biological materials materials [15,16], including [15,16], woodincluding [17– wood24]. Wood-specific [17–24]. Wood applications-specific applications include mappinginclude mapping spatial variationspatial variation of properties of properties across acrostransverses transverse [17,18] and[17,18] radial–longitudinal and radial–longitudinal surfaces surfaces [19,20], mapping[19,20], mapping of moisture of moisture and density and variation density withinvariation boards within [21 boards,22], and [21,22] detection, and ofdetection compression of compression wood [23] wood and resin [23] defectsand resin [24 defects]. [24]. As we are interested in bothboth rapidlyrapidly screening trees and understanding how wood properties vary radially amongst trees within families,families, we utilized NIR hyperspectral imaging (NIR-HSI)(NIR-HSI) of increment cores coupled with cluster cluster analysis analysis to to identify identify families families possessing possessing similar similar wood wood propertie properties.s. SilviScan provided provided high high-resolution-resolution wood wood property property data data,, which which were were used used to toverify verify the the proper propertiesties of treesof trees identified identified by bythe the cluster cluster analysis. analysis. We Weare arenot notaware aware of NIR of NIR-HSI-HSI being being used used to characterize to characterize the woodthe wood properties properties of trees of trees of recognized of recognized families families within within a species a species.. Hence Hence,, the objectives the objectives of this ofstudy this were:study were:  To utilize utilize NIR NIR-HSI-HSI and and cluster cluster analysi analysiss to identify to identify Brazilian Brazilian loblolly loblolly pine trees pine sharing trees sharingsimilar • similarcharacteristics; characteristics;  To analyzeanalyze wood properties amongst clusters based on on density, density, microfibril microfibril angle ( (MFA),MFA), and • stistiffnffnessess datadata providedprovided byby SilviScan;SilviScan;  To provideprovide anan approach for identifying specificspecific familiesfamilies for future commercial plantations based • on the potential to produce aa rangerange ofof desirabledesirable forestforest products.products.

2. Materials and Methods

2.1. Sample Origin 2.1. Sample Origin Wood samples were collected from a 10-year-old progeny trial (Figure1) established by BattiStella Wood samples were collected from a 10-year-old progeny trial (Figure 1) established by in the Rio Negrinho municipality, Santa Catarina, Brazil (coordinates 26 40 07.24” S 49 37 37.38” W). BattiStella in the Rio Negrinho municipality, Santa Catarina, Brazil ◦ (coordinates0 26°40′07.24″◦ 0 S The trial was planted in randomized blocks with 120 families, five replicates, and five plants per linear 49°37′37.38″ W). The trial was planted in randomized blocks with 120 families, five replicates, and plot. Stem form, growth, and internode distribution and distance were assessed, and the best 600 trees five plants per linear plot. Stem form, growth, and internode distribution and distance were assessed, identified for nondestructive (breast height increment cores from the best performing individuals) and and the best 600 trees identified for nondestructive (breast height increment cores from the best destructive (breast height disc) sampling. performing individuals) and destructive (breast height disc) sampling.

Figure 1. BattiStellaBattiStella loblolly loblolly pine pine at at age age 10 10 in in the the Rio Rio Negrinho Negrinho municipality municipality of of Santa Santa Catarina, Catarina, Brazil. Brazil.

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2.2. Sample Preparation: Radial Strips For discs obtained from destructive sampling, pith-to-bark radial sections were cut using a bandsaw. Section dimensions were 12.5 mm longitudinally (L), and 12.5 mm tangentially (T); tree diameter determined radial (R) length. Sections were dried, glued into core holders, and cut using a twin-blade saw to give radial strips with dimensions: 2 mm (T) 12.5 mm (L), R varied as described × earlier in Section 2.2. Similar strips were prepared using the cores sampled non-destructively.

2.3. Wood Property Analysis From the 600 samples, a subsample of 52 was available for SilviScan and NIR-HSI analysis.

2.3.1. SilviScan SilviScan analysis was conducted at FPInnovations, Canada, on unextracted radial strips having dimensions, 2 mm tangentially, 7 mm longitudinally, with the radial dimension varying owing to variation in the diameter of trees sampled. The following wood properties were determined:

1. Air-dry density (referred to as density in the following text) was measured in 25-micron steps using X-ray densitometry [25]; 2. MFA was measured over 5 mm intervals using scanning X-ray diﬀractometry [26,27], and 3. Wood stiﬀness estimated at the same resolution as MFA [28].

A controlled environment of 40% relative humidity and 20 ◦C was used for all measurements.

2.3.2. NIR-HSI Analysis Hyperspectral images were collected using a Specim FX17 camera (wavelength range 900–1700 nm, 224 wavelengths) fitted with a Specim (OLET 17.5 F/2.1) focusing lens (Specim, Spectral Imaging Ltd., Oulu, Finland). Tungsten halogen lamps illuminated samples, while sample motion and image acquisition (which included a dark current and white reference before collecting an image of each set of samples) were controlled using Lumo software supplied with the system. Samples were analyzed on a black background, and care was taken to minimize exposure of the samples to the intense light of the lamps. Relative reflectance values for each image was calibrated with the corresponding dark current and white reference data and then transformed to absorbance (A), where A = log10 1/R. Regions of interest (ROI), corresponding to each of the 4 samples analyzed per set, were identified, cropped, and these images averaged to give an individual spectrum per sample using MATLAB. The resultant spectra were used for the cluster analysis.

2.4. Cluster Analysis of Wood Properties Based on NIR-HSI Spectra To group the 52 trees into groups with similar characteristics, a cluster analysis (hierarchical complete linkage with square Euclidean distance), was performed on the hyperspectral data with a matrix that consisted of 224 columns (variables–wavelengths) and 52 rows (trees) using The Unscrambler X, version 10.2 (64 bit). (Camo Software AS, Oslo, Norway). Clustering is an agglomerative method that begins by treating each sample as a single cluster and, from there, begins to group the samples based on their similarity, as measured by their square Euclidian distance, until they form a large cluster. The smaller groups, formed from the larger dataset, are known as clusters. Grouping allows identification of similar observations that can potentially categorize them. Prior to cluster analysis, the reflectance values were normalized for each wavelength by dividing by the standard deviation calculated for each wavelength. Three parameters were used to determine the final number of clusters identified. These included the cubic clustering criterion, pseudo F statistic, and pseudo t2 statistic [29]. Two and four clusters were most frequently selected, but we decided to use four as we wanted to have an indication of the most and least suitable trees, something we could not have achieved using Forests 2020, 11, 686 5 of 13 two clusters. Prior to cluster analysis, the reflectance values were normalized for each wavelength by dividing by the standard deviation calculated for each wavelength.

2.5. Graphics Figures were produced in R [30] using the RStudio interface [31] and produced using ggplot2 [32] and the tidyverse [33] packages.

Forests 2020, 11, x FOR PEER REVIEW 5 of 13 3. Results 3. Results 3.1. Cluster Analysis Based on NIR Spectra 3.1. Cluster Analysis Based on NIR Spectra The clusterThe analysis cluster usinganalysis the using spectral the spectral data data obtained obtained forfor each tree tree identified identiﬁed four groups four groups (Figure (Figure2). Based on each2). Based cluster, on each mean cluster, wood mean and wood tree and growth tree growth values values were were determined determined for each each measured measured wood property andwood summarized property and insummarized Table1. Clusterin Table 1. 0 Cluster trees (six0 trees in (six total) in total) had had superior superior wood wood quality quality having having the highest mean density and stiffness. Average height (10.9 m) for Cluster 0 exceeded that of the highest meanthe other density clusters and, while sti theﬀness. average Average diameter height at breast (10.9 height m) (DBH for) Cluster(213 mm) 0was exceeded similar amongst that of the other clusters, whileclusters. the average diameter at breast height (DBH) (213 mm) was similar amongst clusters.

Figure 2. TreesFigure determined 2. Trees determined to have to similar have similar wood wood properties properties based based onon complete linkage linkage clustering clustering with square Euclidianwith square distance Euclidian of near-infrared distance of near- (NIR)infrared spectral (NIR) spectral data. data.

Table 1. ClusterTable means,1. Cluster standard means, standard deviations, deviations and, and median median values values (in (initalics) italics) for SilviScan for SilviScan determined determined wood properties (density, microfibril angle (MFA) and stiffness) and tree height and diameter at wood properties (density, microfibril angle (MFA) and stiffness) and tree height and diameter at breast breast height (DBH). height (DBH). Density MFA Stiffness Average Tree height 1 (m) Average Tree DBH (mm) Density(kg/m3) (deg.)MFA (GPa) Stiffness Average Tree Average Tree Cluster 0 (kg/m3) 24.9,(deg.) 7.2 7.9, 4.6 (GPa) 10.9, 1.0 height 1 (m) 213, 6.2DBH (mm) 546.8, 51.7550.8 (6 trees) 27.2 6.3 11.2 213 Cluster 0 546.8, 51.7 24.9, 7.2 7.9, 4.6 10.9, 1.0 213, 6.2 Cluster 1 550.8520.0, 63.8 24.5,27.2 6.4 7.7, 3.6 6.3 10.3, 1.3 11.2 216, 25.4 213 (6 trees)(21 trees) 522.8 25.4 6.9 10.5 221 Cluster 1 520.0, 63.8 24.5, 6.4 7.7, 3.6 10.3, 1.3 216, 25.4 (21 trees)Cluster 2 522.8524.5, 61.5 25.9,25.4 6.3 7.2, 3.3 6.9 9.8, 1.0 10.5 212, 20.1 221 (21 trees ) 522.0 26.6 6.6 9.6 217 Cluster 2 524.5, 61.5 25.9, 6.3 7.2, 3.3 9.8, 1.0 212, 20.1 (21 trees)Cluster 3 522.0493.6, 39.7 21.2,26.6 6.6 7.5, 3.4 6.6 9.5, 1.5 9.6 211, 36.1 217 (4 trees) 487.4 21.3 6.7 9.0 217 Cluster 3 493.6, 39.7 21.2, 6.6 7.5, 3.4 9.5, 1.5 211, 36.1 1 (4 trees) Tree height487.4 was measured to a21.3 diameter of 80 mm (minimum6.7 for wood product9.0 manufacturing). 217 1 Tree height was measured to a diameter of 80 mm (minimum for wood product manufacturing).

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In terms of end-use, trees identified in Cluster 0 have the greatest potential (at age 10) for producing In terms of end-use, trees identified in Cluster 0 have the greatest potential (at age 10) for structural lumber. In contrast, Cluster 3, which included four trees, had the least desirable solid-wood producing structural lumber. In contrast, Cluster 3, which included four trees, had the least desirable properties, having the lowest average density and second-lowest stiffness (Cluster 2 had a lower solid-wood properties, having the lowest average density and second-lowest stiffness (Cluster 2 had stiffness owing to its higher MFA). Owing to low density and stiffness, trees in Cluster 3 have a lower a lower stiffness owing to its higher MFA). Owing to low density and stiffness, trees in Cluster 3 have potential for producing structural lumber and would be better suited for the manufacture of pulp or a lower potential for producing structural lumber and would be better suited for the manufacture of composites, such as particleboard and medium-density fiberboard. In such products, lower wood pulp or composites, such as particleboard and medium-density fiberboard. In such products, lower density may be beneficial in avoiding the manufacture of panels that are overly heavy, which can be wood density may be beneficial in avoiding the manufacture of panels that are overly heavy, which problematic if density becomes too high. Clusters 1 and 2, which included the majority of trees (42), can be problematic if density becomes too high. Clusters 1 and 2, which included the majority of trees had a very similar average density, while Cluster 1 had a higher average stiffness owing to its lower (42), had a very similar average density, while Cluster 1 had a higher average stiffness owing to its MFA. These characteristics indicate that wood from these clusters may be suited to low-value solid lower MFA. These characteristics indicate that wood from these clusters may be suited to low-value wood applications, such as pallet stock and furniture parts. However, the characteristics of the two solid wood applications, such as pallet stock and furniture parts. However, the characteristics of the groups are so similar that the lower-value uses identified for both clusters could be interchanged. two groups are so similar that the lower-value uses identified for both clusters could be interchanged. The cluster analysis relies on NIR spectra, reflecting differences in wood chemical and physical The cluster analysis relies on NIR spectra, reflecting differences in wood chemical and physical propertiesproperties (no (no wood wood property property information information was was utilized utilized in in the the analysis). analysis). The The average average NIR NIR spectra spectra of of eacheach cluster cluster show show clear clear differences differences (Figure (Figure 33)) with Cluster 0 trees demonstrating the largest upward babaselineseline shift shift relative relative to to the the other other clusters, clusters, with with Cluster Cluster 3 3 trees trees having having the the smallest. smallest. In In their their stud studyy of of Eucalyptus delegatensis alpinealpine ash ash ( (Eucalyptus delegatensis R.R. T. T. Baker) Baker) solid solid wood wood samples samples,, Schimleck Schimleck et et al. al. [34] [34] associated associated suchsuch shifts shifts with with changes changes in in wood wood density. density.

FigureFigure 3. 3. AverageAverage NIR NIR spectra spectra for for each each cluster. cluster.

3.2.3.2. Radial Radial Variation Variation in in Wood Wood Properties Properties RingRing averages averages based based on on SilviScan SilviScan data data were were determined determined for for each each tree tree to to examine examine wood wood property property differencesdifferences amongst amongst clusters. clusters. Figure Figure 4 (density), shows radial variation for eac eachh cluster, with fittedfitted LoessLoess regression regression lines. Based on Figure4 4,, trees trees from from the the two two extreme extreme clusters clusters were were clearly clearly di differentfferent in interms terms of averageof average ring ring densities, densities, while while Clusters Clusters 1 and 1 2and were 2 were very similar.very similar. After ringAfter 2, ring average 2, average density densityvalues forvalues Cluster for Cluster 0 were 0 higher were higher than the than other the clusters,other clusters and the, and di fftheerence difference between between Cluster Cluster 0 and 0Clusters and Clusters 1 and 21 became and 2 bec moreame pronounced more pronounced as age increased.as age increased. In contrast, In contrast, the average the ringaverage densities ring densitiesof the Cluster of the 3Cluster samples, 3 samples, with the with exception the exception of ring 1,of were ring clearly1, were lowerclearly than lower the than other th clusters.e other 3 clusters.Despite similarDespite initialsimilar densities initial densities (470 to 480 (470 kg to/m 480), thekg/m ring3), the averages ring averages for clusters for showedclusters dishowedfferent differenttrends. Cluster trends. 0 Cluster ring densities 0 ring densities increased increased rapidly compared rapidly compared to the other to clusters,the other reaching clusters an, reaching average 3 anof average 630 kg/m of by630 age kg/m 9.3 Clusters by age 9. 1 Clusters and 2 also 1 and increased, 2 also increased, however the however rate of the increase rate of was increase inferior was to inferior to Cluster 0, and ring 9 had an average density of approximately 570 kg/m3 for both clusters.

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Cluster 3 trees had no change in average density (470 kg/m3) for rings 1 to 3. By ring 6, the average density was 520 kg/m3, after which little change was observed. Direct comparisons of our density values with loblolly pine grown in the SE USA is imperfect owing to a different measure of wood density often being employed (specific gravity versus air-dry density), different sampling ages, and differences in what an estimate of average density represents. Jordan et al. [35] provide the most comprehensive study of density in loblolly pine across its natural range. However, they reported basic specific gravity (SG) values (determined from oven dry weight, green volume, and density of water), and the trees sampled were older (average age for the different physiographic regions sampled ranged from 22.4 to 24.2 years). To facilitate direct comparison, studies that utilized SilviScan to analyze loblolly pine breast height cores of similar age were identified. For example, Isik et al. [36] reported mean SilviScan density values for two sites each in Forests 2020, 11, 686 7 of 13 Georgia aged 15 and 16 years (486 kg/m3), North Carolina aged 18 and 19 years (542 kg/m3), and South Carolina aged 14 and 15 years (499 kg/m3). These density values are similar to the average Clusterdensities 0, andreported ring 9for had the an respective average density clusters of in approximately this study (Table 570 kg1),/ mand3 for the both trees clusters. were also Cluster older. 3 treesDespite had the no changedifferen inces average in age, densityDBH values (470 kg (185/m 3mm) for Georgia, rings 1 to 228 3. Bymm ring North 6, the Carolina average, and density 235 wasmm 520South kg /Carolina)m3, after whichwere similar little change owing was to the observed. improved growth rates of loblolly pine in southern Brazil.

FigureFigure 4. 4.DensityDensity radial radial variation variation for for trees trees representing thethe averageaverage wood wood property property values values of of each each cluster. cluster.

DirectWhile comparisonsit is not possible of our to compare density valuesring densities with loblolly, percent pine latewood grown (% in theLW) SE may USA provide is imperfect useful owingcomparative to a di ffdataerent, and, measure as noted of wood by Jordan density et al. often [35], being % LW employed is of critical (specific importance gravity in versus determining air-dry density),ring density diff erentand subsequently sampling ages,, whole and di-treefferences density. in whatJordan an et estimate al. [35] used of average an SG densityof 0.48 represents.to separate Jordanearlywood et al. ( [EW35]) provide from LW, the while most we comprehensive used a density study of 480 of densitykg/m3 [37]. in loblollyHence, the pine point across within its natural a ring range.at which However, the commencement they reported basic of LW specific production gravity (SG) may values be slightly (determined different, from but oven the dry practical weight, greensignificance volume, of andthe densitydifference of water),will be small and the as treesEW changes sampled rapidly were older to LW (average in loblolly age forpine the [37,38]. different For physiographicCluster 0 trees, regions average sampled % LW was ranged initially from low 22.4 (ring to 24.2 1 years).= 17.5%, To ring facilitate 2 = 24.8%) direct but comparison, increased studiesquickly that(% LW utilized for rings SilviScan 6 to 8 was to analyze approximately loblolly 50%) pine and breast reached height 65.3% cores for of ring similar 9. In agecomparison, were identified. Cluster For3 had example, similar Isik% LW et al. near [36 the] reported pith (ring mean 2 = SilviScan18.1%, ring density 3 = 21.3%) values, but for it two increased sites each slowly in Georgia with rings aged 4 3 3 15to and9 having 16 years % LW (486 in kg the/m range), North 26 to Carolina 34%. Clusters aged 18 1 andand 192 had years an (542initial kg /%m LW), and consistent South Carolina with the 3 agedother 14 clusters and 15 and years reached (499 kg /40%m ). LW These by densityring 6 and values approximately are similar to 54% the averageLW by ring densities 9. Pictures reported of forthe theradial respective samples clusters for trees in in this Cluster study 0 (Table and 31 are), and shown the trees in Figure were 5 also. older. Despite the di fferences in age, DBH values (185 mm Georgia, 228 mm North Carolina, and 235 mm South Carolina) were similar owing to the improved growth rates of loblolly pine in southern Brazil. While it is not possible to compare ring densities, percent latewood (% LW) may provide useful comparative data, and, as noted by Jordan et al. [35], % LW is of critical importance in determining ring density and subsequently, whole-tree density. Jordan et al. [35] used an SG of 0.48 to separate earlywood (EW) from LW, while we used a density of 480 kg/m3 [37]. Hence, the point within a ring at which the commencement of LW production may be slightly different, but the practical significance of the difference will be small as EW changes rapidly to LW in loblolly pine [37,38]. For Cluster 0 trees, average % LW was initially low (ring 1 = 17.5%, ring 2 = 24.8%) but increased quickly (% LW for rings 6 to 8 was approximately 50%) and reached 65.3% for ring 9. In comparison, Cluster 3 had similar % LW near the pith (ring 2 = 18.1%, ring 3 = 21.3%), but it increased slowly with rings 4 to 9 having % LW in the range 26 to 34%. Clusters 1 and 2 had an initial % LW consistent with the other clusters and reached 40% LW by ring 6 and approximately 54% LW by ring 9. Pictures of the radial samples for trees in Cluster 0 and 3 are shown in Figure5. Forests 2020, 11, x FOR PEER REVIEW 8 of 13 Forests 2020, 11, 686 8 of 13

(a)

(b)

FigureFigure 5.5.Radial Radial stripsstrips forfor ((aa)) ClusterCluster 00 andand ( (bb)) Cluster Cluster 3 3 trees. trees.

FigureFigure2 2in in Jordan Jordan et et al. al. [ [35]35] indicatesindicates thatthat %% LWLW increases increases rapidly rapidly with with age age in in the the SE SE USA, USA, with with regionalregional averagesaverages approachingapproaching 40%40% byby ageage 55 andand atat leastleast 45%45% byby ageage 99 (at(at thisthis ageage treestrees fromfrom stands stands fromfrom the the south south Atlantic Atlantic coastal coastal plain plain had had 52 52 toto 53%53% LW).LW). This This data data indicatesindicates that that %% LWLW (and (and note note this this isis on on a a regionalregional basis basis andand representsrepresents thethe averageaverage ofof manymany treestrees andand sites)sites) forfor treestrees growngrown inin thethe USAUSA southsouth would would be be well well above above the the values values reported reported here for here Cluster for Cluster 3, similar 3, similar to those to reported those reported for Clusters for 1Clusters and 2 and 1 and lower 2 and than lower those than for thethose Cluster for the 0 trees.Cluster 0 trees. HartHart [ 7 [7],], in in his his comparison comparison of loblolly of loblolly pine grownpine grown in Santa in Catarina Santa Catarina and the USA and South,the US observedA South, thatobserved the ratio that of the EW ratio to LW of wasEW to a majorLW was diff aerence major between difference the between two regions. the two On regions. a volume On basis, a volume trees frombasis Santa, trees Catarina from Santa at age Catarina 17 were at approximatelyage 17 were approximately 21 to 22% latewood, 21 to 22% while latewood, the trees while from thethe USAtrees southfrom (agedthe US 18–21A south years) (aged had 18 a– much21 years) higher had proportion a much higher (54 to 55%).proportion When (54 making to 55%). comparisons When making with Hartcomparisons [7], it is important with Hart to [7] recognize, it is important that the families to recognize from the that breeding the families trial sampled from the for breedingthis study trial are notsampled typical for of this what study is planted are not in typical industrial of what plantations. is planted The in familiesindustrial also plantati representons. The a wide families range also of variabilityrepresent ina wide terms range of wood of variability properties in and terms physiology. of wood This properties is apparent and uponphysiology. a detailed This examination is apparent ofupon the a wood detailed properties examination of the of di thefferent wood clusters. properties The of variation the different in % clusters. LW provides The variation an explanation in % LW forprovides the separation an explanation of trees for into the di sefferentparation clusters, of trees but into for treesdifferent of di clustersfferent, families but for totrees produce of different such largefamilies diff erencesto produce in % such LW large while differences growing at in the % sameLW while location growing indicates at the that same the location trees responded indicates very that dithefferently trees respond to the growinged very differently conditions theyto the experienced. growing conditions they experienced. TheThe averageaverage ring MFA of of Cluster Cluster 3 3was was consistently consistently lower lower than than the the other other clusters clusters (Figure (Figure 6). The6). TheMFA MFA of ring of ring 1 was 1 was 30° 30, ◦ and, and similar similar to to the the other other clusters, clusters, but but by by ring ring 6 6,, it had had decreased decreased to to approximatelyapproximately 16 16°◦ and and was was 5 5 to to 8 8◦° lowerlower than than Clusters Clusters 0 0 to to 2. 2. By By ring ring 9,9, ClustersClusters 0 0 to to 2, 2, which which demonstrateddemonstrated similarsimilar radialradial trendstrends ofof decreasingdecreasing MFA,MFA, hadhad similarsimilar anglesangles (18(18◦°)) andand closeclose toto thatthat ofof ClusterCluster 33 (16(16◦°).). ThreeThree samplessamples (one(one eacheach inin ringsrings 33 toto 5)5) withwith MFAsMFAs closeclose toto or or above above 4040°◦ were were muchmuch higherhigher thanthan thethe otherother valuesvalues observedobserved forfor thesethese rings,rings, andand likelylikely indicateindicate thethe presencepresence ofof compressioncompression woodwood in in one one tree. tree.

Forests 2020, 11, 686 9 of 13 Forests 2020, 11, x FOR PEER REVIEW 9 of 13

Figure 6. MicrofibrilMicroﬁbril angle (MFA) radial variation for trees representing the average wood property values of each cluster.

MFA variation for for loblolly loblolly pine pine grown grown in in the the SE SE USA USA is not is not as asextensively extensively studied studied as density as density (or SG)(or SG)owing owing to the to thecost cost of ofanalysis. analysis. Jordan Jordan et etal. al. [39] [39 and] and Jordan Jordan et et al. al. [40] [40 ]reported reported MFA MFA within within-tree-tree variation forfor aa smallsmall numbernumber of of trees trees from from five five physiographic physiographic regions regions (the (the upper upper coastal coastal plain plain was wa nots notrepresented). represented). Jordan Jordan et al. et [40al.] [40] reported reported average average MFAs MFAs near near the piththe ofpith approximately of approximately 35◦ for 35 trees° for treesfrom from the north the north Atlantic Atlantic coastal coastal plain plain and Piedmont, and Piedmont, whereas whereas MFA forMFA the for other the regionsother regions was closer was closerto 30◦ .to For 30° all. For regions all regions in the in SE the USA, SE USA MFA, MFA decreased decreased rapidly rapidly in the in first the fewfirst yearsfew years of growth, of growth and, andat age at age 10, trees10, trees from from the the north north Atlantic Atlantic coastal coastal plain plain and and Piedmont Piedmont had had the the highesthighest averageaverage brea breastst height MFAsMFAs (approximately(approximately 23 23◦),°), whereas whereas MFAs MFAs for for the the remaining remaining three three regions regions (hilly, (hilly, gulf, andgulf south, and southAtlantic Atlantic coastal coastal plains) plains) were allwere similar all similar (16 to 17(16◦ ).to 17°). Radial variation in stiffness stiffness (Figure7 7)) waswas similarsimilar acrossacross clusters.clusters. StiStiffnessffness of rings 1 to 3 for all clusters was very low (5 GPa or less) but increased rapidly and by ring 7 7,, averaged 10 GPa. Trees Trees in Cluster 0 had the highest ring ring 7 7 stiffness stiffness with differences differences between it and the other clusters becomi becomingng more pronounced pronounced as as the the trees trees grew grew older. older. By By Ring Ring 9, 9,average average stiffness stiffness for for the the Cluster Cluster 0 trees 0 trees was was 15 GPa15 GPa compared compared to toapproxima approximatelytely 12.5 12.5 GPa GPa for for the the other other 3 3clusters. clusters. The The marked marked increase increase in in stiffness stiffness for Cluster 0 0 can be related to the higher density of this cluster (Figure 44)) andand aa decreasingdecreasing trendtrend inin MFA particularly afterafter ringring 55 whenwhen itsits averageaverage ring ring MFA MFA became, became, and and stayed, stayed, lower lower than than the the MFA MFA of ofClusters Clusters 1 and 1 and 2 (Figure 2 (Figure6). The 6). comparativelyThe comparatively high high sti ffness stiffness of trees of intrees Cluster in Cluster 3 up until 3 up ring until 6 relatesring 6 relatesdirectly directly to the lower to the initial lower MFAs initial of thisMFAs cluster. of this After cluster. ring 6,After differences ring 6, indifferences MFA became in MFA progressively became progressivelysmaller with age, smaller and whilewith age Cluster, and 3w treeshile Cluster still had 3 the trees lowest still ringhad the MFAs lowest (Figure ring6), MFAs their lower(Figure ring 6), theirdensities lower (Figure ring densities4) resulted (Figure in sti 4)ffness resulted increasing in stiffness at a slower increasing rate at than a slower for Clusters rate than 0 to for 2. Clusters It is also 0apparent to 2. It is in also Figure apparent7 that ain small Figure number 7 that ofa small trees havenumber quite of hightrees ringhave sti quiteffness high values ring for stiffness rings8 values and 9, forwith rings stiff ness8 and values 9, with being stiffness close values to 20 GPa.being close to 20 GPa.

Forests 2020, 11, 686 10 of 13 Forests 2020, 11, x FOR PEER REVIEW 10 of 13

FigureFigure 7. Sti 7. ﬀStiffnessness radialradial variation variation for trees for representing trees representing the average the wood average property wood values propertyof each cluster. values of

each cluster.Antony et al. [41] reported stiffness values for the same regions examined by Jordan et al. [35]. Corewood stiffness values for static bending samples from a height of 2.4 m ranged from 3.6 GPa Antony(North et Atlantic al. [41 ]coastal reported plain) sti toff 5.6ness GPa values (gulf coastal for the plain), same while regions outerwood examined stiffness byranged Jordan from et al. [35]. Corewood8 GPa stiff (nessNorth values Atlanti forc coastal static plain) bending to 10.9 samples GPa (gulf from coastal a height plain). of The 2.4 stiffness m ranged averages from for 3.6 the GPa (North Atlanticdifferent coastal clusters plain) to(Table 5.6 1) GPa are (gulfcomparable coastal to the plain), corewood while averages outerwood reported sti byff nessAntony ranged et al. [41] from. 8 GPa (North AtlanticHowever coastal, it is important plain) to to note 10.9 that GPa the (gulfcorewood coastal samples plain). examined The by sti Anffnesstony averageset al. [41] generally for the different represented rings two to four. Antony et al. [40] also measured stiffness on short, clear bending clusters (Tablespecimens1) are (static comparable stiffness) versus to the the corewood dynamic stiffness averages used reported here. Jordan by Antony et al. [35] et observed al. [ 41]. that However, it is importantouterwood to note production that the corewoodcommences samplesin loblolly examined pine by year by 13 Antony in the SE et al. USA [41. H]ence generally, the trees represented rings twoexamined to four. in Antony this study et (aged al. [40 10] years) also measuredlargely comprise stiff nesscorewood. on short, clear bending specimens (static stiffness) versusWe have the dynamic shown that sti withinffness a used Brazilian here.-grown Jordan loblolly et al. pine [35 ]progeny observed test, that it is outerwoodpossible to use production NIR-HSI data to identify groups (or clusters) of trees. Our hypothesis was that clusters would differ commences in loblolly pine by year 13 in the SE USA. Hence, the trees examined in this study (aged in terms of their wood properties and SilviScan data for density, MFA, and stiffness when averaged 10 years)for largely trees within comprise clusters, corewood. confirmed wood property differences among clusters. Four clusters were Weidentified, have shown with trees that identified within a in Brazilian-grown Clusters 0 and 3 having loblolly the highest pine and progeny lowest test,average it isdensitie possibles, to use NIR-HSIrespectively. data to identify Clusters groups 1 and 2 had (or clusters)very similar of average trees. wood Our hypothesisproperties, with was densities that clusters closer to th wouldat differ in termsof of Cluster their wood0 than propertiesCluster 3. Radial and trend SilviScans of increasing data for density density, and MFA,stiffness and and sti decreasingffness when MFA averaged observed for the clusters are consistent with trends reported for loblolly pine [7,35,41,42]. The greatest for treesdifferences within clusters, in trends confirmedamongst clusters wood were property observed di forff erencesdensity (clearly among highest clusters. for Cluste Fourr 0 clusters and were identified,lowest with for trees Cluster identified 3) and MFA in (clearly Clusters lowest 0 and for 3Cluster having 3). The the higher highest densities and lowest for Cluster average 0 were densities, respectively.related Clusters to a higher 1 and% LW 2 compared had very to similar the other average clusters, woodwith Cluster properties, 3 having with the lowest. densities closer to that of Cluster 0NIR than-HSI Cluster provides 3. a Radialrapid approach trends for of collecting increasing wood density property and data stiandff,ness when and coupled decreasing with MFA observedcluster for the analysis clusters, potentially are consistent allows screening with trends for desirable reported wood for properties loblolly amongst pine [7, 35families,41,42 in]. tree The greatest improvement programs. Selection will be company-specific, with emphasis likely to be on improving differencessolid in wood trends properti amongstes. Hence clusters, as an were initial observed screening for (based density on NIR (clearly-HSI cluster highest analysis for Cluster and 0 and lowest forsupported Cluster by 3) corresponding and MFA (clearly SilviScan lowest data), trees for in Cluster Cluster 3).0 would The higherbe likely densitiescandidates for further Cluster 0 were related toevaluation a higher and % testing. LW compared Regardless to of the the target other of clusters, the breeding with program, Cluster NIR 3 having-HSI can thefocus lowest. effort on NIR-HSItrees with provides desirable a rapid properties. approach The greater for collecting provision wood of wood property property data data and, would when allow coupled tree with breeders to have the option of selecting fewer families for field experiments, or they could decide cluster analysis, potentially allows screening for desirable wood properties amongst families in tree which families in existing trials would be the most suitable for meeting the objectives of a company improvement programs. Selection will be company-specific, with emphasis likely to be on improving solid wood properties. Hence, as an initial screening (based on NIR-HSI cluster analysis and supported by corresponding SilviScan data), trees in Cluster 0 would be likely candidates for further evaluation and testing. Regardless of the target of the breeding program, NIR-HSI can focus effort on trees with desirable properties. The greater provision of wood property data would allow tree breeders to have the option of selecting fewer families for field experiments, or they could decide which families in existing trials would be the most suitable for meeting the objectives of a company investing in commercial plantations on a large-scale. It also provides a rapid approach for collecting wood property-related Forests 2020, 11, 686 11 of 13 data from increment cores or discs. The same outcome could likely be achieved using a standard NIR spectrometer [11–13] where spectra at a given spatial resolution are collected and then averaged, but the time required to collect data is greatly reduced using a NIR-HSI system.

4. Conclusions Near-infrared hyperspectral images (NIR-HSI) were collected from 52 radial strips obtained from a 10-year-old loblolly pine progeny trial located in Brazil. The NIR data were subject to a hierarchical complete linkage with square Euclidean distance cluster analysis. Four clusters (0, 1, 2, 3) were identified, and we demonstrated that clusters differed in terms of average air-dry density, microfibril angle (MFA), and stiffness. Average ring data were used to compare clusters, and Cluster 0 trees had the highest average ring densities, whereas those in Cluster 3, the lowest. Cluster 3 trees also had the lowest ring MFAs.

Author Contributions: Conceptualization, A.H., J.L.M.M., and L.S.; methodology, A.H., J.L.M.M., and L.S.; sample collection and preparation, J.G.P. and R.T.; software, L.S., J.L.M.M., and J.D.; formal analysis, L.S., J.L.M.M., and J.D.; resources, J.L.M.M. and A.H.; data curation, J.L.M.M.; writing—original draft preparation, L.S. and J.L.M.M.; writing—review and editing, L.S., J.L.M.M., and J.D.; visualization, J.L.M.M. and J.D.; supervision, J.L.M.M.; project administration, J.L.M.M.; funding acquisition, J.L.M.M. and A.H. All authors have read and agreed to the published version of the manuscript. Funding: National Council for Scientific and Technological Development-CNPq for granting support for the execution of the research project. FINEP for the grant to develop the project “Use of biotechnological tools to improve the genetic quality of Pinus taeda and alternatives species”. Acknowledgments: Battistella Florestal for the establishment, maintenance, and data and sample collection from the progeny test located in the Rio Negrinho municipality of Santa Catarina, Brazil. Oregon State University, College of Forestry for Hosting J.M., and Marja Haagsma and Gerald Page (Oregon State University) for their MATLAB expertise. Conflicts of Interest: The authors declare no conflict of interest.

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