Wood density prediction using near-infrared hyperspectral imaging for early selection of Eucalyptus grandis trees

Key message Near-infrared hyperspectral imaging allows to build suitable wood density maps for 6-year-old Eucalyptus grandis trees. Robust age–age correlations from wood density maps suggest feasible early tree selection for wood density. Wood is a heterogenous material whose properties vary over ti...

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Veröffentlicht in:Trees (Berlin, West) West), 2023-06, Vol.37 (3), p.981-991
Hauptverfasser: Chambi-Legoas, Roger, Tomazello-Filho, Mario, Vidal, Cristiane, Chaix, Gilles
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creator Chambi-Legoas, Roger
Tomazello-Filho, Mario
Vidal, Cristiane
Chaix, Gilles
description Key message Near-infrared hyperspectral imaging allows to build suitable wood density maps for 6-year-old Eucalyptus grandis trees. Robust age–age correlations from wood density maps suggest feasible early tree selection for wood density. Wood is a heterogenous material whose properties vary over time, making it difficult to predict the wood properties at a given age of trees in the future. The site and climate are also factors affecting wood heterogeneity. To improve the accuracy of early selection of trees in drier sites, it is thus important to study inter-annual variations in wood density in conditions of contrasting water availability. We tested the use of near-infrared hyperspectral imaging (NIR-HSI) to assess inter-annual wood density and predict wood density at a future age to evaluate the accuracy of early selection of Eucalyptus grandis trees for wood density and to see if a drier site influences early selection. We sampled 38 six-year-old trees growing under two different water regimes: (i) 37% throughfall reduction (–W), to simulate a dry site, and (ii) undisturbed throughfall (+ W). NIR-HSI images were used to build high-resolution wood density maps of the whole cross section. After the annual growth rings were delimited, the average wood density at each age and in growth ring was extracted to evaluate juvenile–mature correlations in the wood. The NIR-HSI images calibrated with a locally weighted partial least square regression (LWPLSR) model, using raw spectra, performed well in predicting the wood density of the whole cross section. Correlations for wood density between ages 1–3 and 5–6 were strong ( r  = 0.85 to 0.94), while correlations between rings 1–3 and 4–5 were moderate to strong ( r  = 0.51 to 0.87). In − W plots, juvenile–mature correlations were slightly lower than in + W plots. Our results suggest that early E. grandis selection for wood density is feasible to predict wood density at 6 years of age.
doi_str_mv 10.1007/s00468-023-02397-2
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NIR-HSI images were used to build high-resolution wood density maps of the whole cross section. After the annual growth rings were delimited, the average wood density at each age and in growth ring was extracted to evaluate juvenile–mature correlations in the wood. The NIR-HSI images calibrated with a locally weighted partial least square regression (LWPLSR) model, using raw spectra, performed well in predicting the wood density of the whole cross section. Correlations for wood density between ages 1–3 and 5–6 were strong ( r  = 0.85 to 0.94), while correlations between rings 1–3 and 4–5 were moderate to strong ( r  = 0.51 to 0.87). In − W plots, juvenile–mature correlations were slightly lower than in + W plots. 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NIR-HSI images were used to build high-resolution wood density maps of the whole cross section. After the annual growth rings were delimited, the average wood density at each age and in growth ring was extracted to evaluate juvenile–mature correlations in the wood. The NIR-HSI images calibrated with a locally weighted partial least square regression (LWPLSR) model, using raw spectra, performed well in predicting the wood density of the whole cross section. Correlations for wood density between ages 1–3 and 5–6 were strong ( r  = 0.85 to 0.94), while correlations between rings 1–3 and 4–5 were moderate to strong ( r  = 0.51 to 0.87). In − W plots, juvenile–mature correlations were slightly lower than in + W plots. 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Robust age–age correlations from wood density maps suggest feasible early tree selection for wood density. Wood is a heterogenous material whose properties vary over time, making it difficult to predict the wood properties at a given age of trees in the future. The site and climate are also factors affecting wood heterogeneity. To improve the accuracy of early selection of trees in drier sites, it is thus important to study inter-annual variations in wood density in conditions of contrasting water availability. We tested the use of near-infrared hyperspectral imaging (NIR-HSI) to assess inter-annual wood density and predict wood density at a future age to evaluate the accuracy of early selection of Eucalyptus grandis trees for wood density and to see if a drier site influences early selection. We sampled 38 six-year-old trees growing under two different water regimes: (i) 37% throughfall reduction (–W), to simulate a dry site, and (ii) undisturbed throughfall (+ W). NIR-HSI images were used to build high-resolution wood density maps of the whole cross section. After the annual growth rings were delimited, the average wood density at each age and in growth ring was extracted to evaluate juvenile–mature correlations in the wood. The NIR-HSI images calibrated with a locally weighted partial least square regression (LWPLSR) model, using raw spectra, performed well in predicting the wood density of the whole cross section. Correlations for wood density between ages 1–3 and 5–6 were strong ( r  = 0.85 to 0.94), while correlations between rings 1–3 and 4–5 were moderate to strong ( r  = 0.51 to 0.87). In − W plots, juvenile–mature correlations were slightly lower than in + W plots. Our results suggest that early E. grandis selection for wood density is feasible to predict wood density at 6 years of age.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00468-023-02397-2</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6363-9475</orcidid><orcidid>https://orcid.org/0000-0001-8473-0462</orcidid><orcidid>https://orcid.org/0000-0003-2015-0551</orcidid></addata></record>
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ispartof Trees (Berlin, West), 2023-06, Vol.37 (3), p.981-991
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source SpringerLink Journals - AutoHoldings
subjects Accuracy
Age
Agriculture
Annual variations
Biomechanics
Biomedical and Life Sciences
climate
Correlation
Cross-sections
Density
early selection
Eucalyptus
Eucalyptus grandis
Forestry
Growth rings
Hardwoods
Heterogeneity
Hyperspectral imaging
I.R. radiation
Infrared imaging
least squares
Life Sciences
Near infrared radiation
Original Article
Performance prediction
Plant Anatomy/Development
Plant Pathology
Plant Physiology
Plant Sciences
prediction
Regression models
Shrinkage
Throughfall
Trees
Water availability
Water regimes
Wood
wood density
title Wood density prediction using near-infrared hyperspectral imaging for early selection of Eucalyptus grandis trees
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