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 |
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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 |
format | Article |
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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.</description><identifier>ISSN: 0931-1890</identifier><identifier>EISSN: 1432-2285</identifier><identifier>DOI: 10.1007/s00468-023-02397-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>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</subject><ispartof>Trees (Berlin, West), 2023-06, Vol.37 (3), p.981-991</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c337t-28041867dbbf2f2c8678325b2acccc2188b743a25d9ef8670ec43520b4df6da93</cites><orcidid>0000-0001-6363-9475 ; 0000-0001-8473-0462 ; 0000-0003-2015-0551</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00468-023-02397-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00468-023-02397-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://hal.inrae.fr/hal-04477277$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Chambi-Legoas, Roger</creatorcontrib><creatorcontrib>Tomazello-Filho, Mario</creatorcontrib><creatorcontrib>Vidal, Cristiane</creatorcontrib><creatorcontrib>Chaix, Gilles</creatorcontrib><title>Wood density prediction using near-infrared hyperspectral imaging for early selection of Eucalyptus grandis trees</title><title>Trees (Berlin, West)</title><addtitle>Trees</addtitle><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.</description><subject>Accuracy</subject><subject>Age</subject><subject>Agriculture</subject><subject>Annual variations</subject><subject>Biomechanics</subject><subject>Biomedical and Life Sciences</subject><subject>climate</subject><subject>Correlation</subject><subject>Cross-sections</subject><subject>Density</subject><subject>early selection</subject><subject>Eucalyptus</subject><subject>Eucalyptus grandis</subject><subject>Forestry</subject><subject>Growth rings</subject><subject>Hardwoods</subject><subject>Heterogeneity</subject><subject>Hyperspectral imaging</subject><subject>I.R. radiation</subject><subject>Infrared imaging</subject><subject>least squares</subject><subject>Life Sciences</subject><subject>Near infrared radiation</subject><subject>Original Article</subject><subject>Performance prediction</subject><subject>Plant Anatomy/Development</subject><subject>Plant Pathology</subject><subject>Plant Physiology</subject><subject>Plant Sciences</subject><subject>prediction</subject><subject>Regression models</subject><subject>Shrinkage</subject><subject>Throughfall</subject><subject>Trees</subject><subject>Water availability</subject><subject>Water regimes</subject><subject>Wood</subject><subject>wood density</subject><issn>0931-1890</issn><issn>1432-2285</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kUGL1TAUhYMo-Bz9A64CbnTR8eYmbdLlMIyO8MCN4jKkafImQyfpJK3Qf2-eFQUXE7gk3Pudww2HkLcMLhmA_FgARKcaQH6uXjb4jByY4NggqvY5OUDPWcNUDy_Jq1LuAYB3DA_k8UdKIx1dLGHZ6JzdGOwSUqRrCfFEozO5CdFnUyf0bptdLrOzSzYTDQ_mdGZ8yrRi00aLm9yuTp7erNZM27yshZ6yiWModMnOldfkhTdTcW_-3Bfk-6ebb9e3zfHr5y_XV8fGci6XBhUIpjo5DoNHj7Y-Fcd2QGPrQabUIAU32I6983UIzgreIgxi9N1oen5BPuy-d2bSc67b5k0nE_Tt1VGfeyCElCjlT1bZ9zs75_S4urLoh1CsmyYTXVqL5iCgugOKir77D71Pa471JxoVY7ITSrWVwp2yOZWSnf-7AQN9Tkzviemalv6dmMYq4ruoVDieXP5n_YTqFwmxmak</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Chambi-Legoas, Roger</creator><creator>Tomazello-Filho, Mario</creator><creator>Vidal, Cristiane</creator><creator>Chaix, Gilles</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><general>Springer Verlag</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7X2</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M0K</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>SOI</scope><scope>7S9</scope><scope>L.6</scope><scope>1XC</scope><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></search><sort><creationdate>20230601</creationdate><title>Wood density prediction using near-infrared hyperspectral imaging for early selection of Eucalyptus grandis trees</title><author>Chambi-Legoas, Roger ; Tomazello-Filho, Mario ; Vidal, Cristiane ; Chaix, Gilles</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-28041867dbbf2f2c8678325b2acccc2188b743a25d9ef8670ec43520b4df6da93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Age</topic><topic>Agriculture</topic><topic>Annual variations</topic><topic>Biomechanics</topic><topic>Biomedical and Life Sciences</topic><topic>climate</topic><topic>Correlation</topic><topic>Cross-sections</topic><topic>Density</topic><topic>early selection</topic><topic>Eucalyptus</topic><topic>Eucalyptus grandis</topic><topic>Forestry</topic><topic>Growth rings</topic><topic>Hardwoods</topic><topic>Heterogeneity</topic><topic>Hyperspectral imaging</topic><topic>I.R. radiation</topic><topic>Infrared imaging</topic><topic>least squares</topic><topic>Life Sciences</topic><topic>Near infrared radiation</topic><topic>Original Article</topic><topic>Performance prediction</topic><topic>Plant Anatomy/Development</topic><topic>Plant Pathology</topic><topic>Plant Physiology</topic><topic>Plant Sciences</topic><topic>prediction</topic><topic>Regression models</topic><topic>Shrinkage</topic><topic>Throughfall</topic><topic>Trees</topic><topic>Water availability</topic><topic>Water regimes</topic><topic>Wood</topic><topic>wood density</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chambi-Legoas, Roger</creatorcontrib><creatorcontrib>Tomazello-Filho, Mario</creatorcontrib><creatorcontrib>Vidal, Cristiane</creatorcontrib><creatorcontrib>Chaix, Gilles</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Biological Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environment Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Trees (Berlin, West)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chambi-Legoas, Roger</au><au>Tomazello-Filho, Mario</au><au>Vidal, Cristiane</au><au>Chaix, Gilles</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Wood density prediction using near-infrared hyperspectral imaging for early selection of Eucalyptus grandis trees</atitle><jtitle>Trees (Berlin, West)</jtitle><stitle>Trees</stitle><date>2023-06-01</date><risdate>2023</risdate><volume>37</volume><issue>3</issue><spage>981</spage><epage>991</epage><pages>981-991</pages><issn>0931-1890</issn><eissn>1432-2285</eissn><abstract>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.</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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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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