Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common gar...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Remote sensing (Basel, Switzerland) Switzerland), 2019-12, Vol.11 (24), p.2884
Hauptverfasser: Deepak, Maya, Keski-Saari, Sarita, Fauch, Laure, Granlund, Lars, Oksanen, Elina, Keinänen, Markku
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 24
container_start_page 2884
container_title Remote sensing (Basel, Switzerland)
container_volume 11
creator Deepak, Maya
Keski-Saari, Sarita
Fauch, Laure
Granlund, Lars
Oksanen, Elina
Keinänen, Markku
description The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.
doi_str_mv 10.3390/rs11242884
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2550295935</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2550295935</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-aedbd99c632799d1c42fd5817a36a1fcd214da264d281d2d05cf2e13430f968b3</originalsourceid><addsrcrecordid>eNpNUEtLxDAYDKLgsu7FXxDwJlTzfUna5iKsxRcUBFfPIZsHdqltTbpC_72VFXQOM3MYZmAIOQd2xbli1zEBoMCyFEdkgazATKDC43_-lKxS2rEZnINiYkFuam8CrUzXDxOtzeRjousQvB3pZpg5mpa--NDO1nTW06ajm6b98pHeNtG-n5GTYNrkV7-6JG_3d6_VY1Y_PzxV6zqzqGDMjHdbp5TNORZKObACg5MlFIbnBoJ1CMIZzIXDEhw6Jm1AD1xwFlRebvmSXBx6h9h_7n0a9a7fx26e1CglQyUVl3Pq8pCysU8p-qCH2HyYOGlg-uci_XcR_waNMVb9</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2550295935</pqid></control><display><type>article</type><title>Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Deepak, Maya ; Keski-Saari, Sarita ; Fauch, Laure ; Granlund, Lars ; Oksanen, Elina ; Keinänen, Markku</creator><creatorcontrib>Deepak, Maya ; Keski-Saari, Sarita ; Fauch, Laure ; Granlund, Lars ; Oksanen, Elina ; Keinänen, Markku</creatorcontrib><description>The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs11242884</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Birch trees ; Canopies ; Chlorophyll ; Discriminant analysis ; Herbivores ; Leaf area ; Leaves ; Principal components analysis ; Reflectance ; Remote sensing ; Spectral reflectance ; Trees ; Wavelengths</subject><ispartof>Remote sensing (Basel, Switzerland), 2019-12, Vol.11 (24), p.2884</ispartof><rights>2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-aedbd99c632799d1c42fd5817a36a1fcd214da264d281d2d05cf2e13430f968b3</citedby><cites>FETCH-LOGICAL-c291t-aedbd99c632799d1c42fd5817a36a1fcd214da264d281d2d05cf2e13430f968b3</cites><orcidid>0000-0002-1866-736X ; 0000-0003-0458-5520</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids></links><search><creatorcontrib>Deepak, Maya</creatorcontrib><creatorcontrib>Keski-Saari, Sarita</creatorcontrib><creatorcontrib>Fauch, Laure</creatorcontrib><creatorcontrib>Granlund, Lars</creatorcontrib><creatorcontrib>Oksanen, Elina</creatorcontrib><creatorcontrib>Keinänen, Markku</creatorcontrib><title>Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch</title><title>Remote sensing (Basel, Switzerland)</title><description>The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.</description><subject>Birch trees</subject><subject>Canopies</subject><subject>Chlorophyll</subject><subject>Discriminant analysis</subject><subject>Herbivores</subject><subject>Leaf area</subject><subject>Leaves</subject><subject>Principal components analysis</subject><subject>Reflectance</subject><subject>Remote sensing</subject><subject>Spectral reflectance</subject><subject>Trees</subject><subject>Wavelengths</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpNUEtLxDAYDKLgsu7FXxDwJlTzfUna5iKsxRcUBFfPIZsHdqltTbpC_72VFXQOM3MYZmAIOQd2xbli1zEBoMCyFEdkgazATKDC43_-lKxS2rEZnINiYkFuam8CrUzXDxOtzeRjousQvB3pZpg5mpa--NDO1nTW06ajm6b98pHeNtG-n5GTYNrkV7-6JG_3d6_VY1Y_PzxV6zqzqGDMjHdbp5TNORZKObACg5MlFIbnBoJ1CMIZzIXDEhw6Jm1AD1xwFlRebvmSXBx6h9h_7n0a9a7fx26e1CglQyUVl3Pq8pCysU8p-qCH2HyYOGlg-uci_XcR_waNMVb9</recordid><startdate>20191204</startdate><enddate>20191204</enddate><creator>Deepak, Maya</creator><creator>Keski-Saari, Sarita</creator><creator>Fauch, Laure</creator><creator>Granlund, Lars</creator><creator>Oksanen, Elina</creator><creator>Keinänen, Markku</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-1866-736X</orcidid><orcidid>https://orcid.org/0000-0003-0458-5520</orcidid></search><sort><creationdate>20191204</creationdate><title>Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch</title><author>Deepak, Maya ; Keski-Saari, Sarita ; Fauch, Laure ; Granlund, Lars ; Oksanen, Elina ; Keinänen, Markku</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-aedbd99c632799d1c42fd5817a36a1fcd214da264d281d2d05cf2e13430f968b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Birch trees</topic><topic>Canopies</topic><topic>Chlorophyll</topic><topic>Discriminant analysis</topic><topic>Herbivores</topic><topic>Leaf area</topic><topic>Leaves</topic><topic>Principal components analysis</topic><topic>Reflectance</topic><topic>Remote sensing</topic><topic>Spectral reflectance</topic><topic>Trees</topic><topic>Wavelengths</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Deepak, Maya</creatorcontrib><creatorcontrib>Keski-Saari, Sarita</creatorcontrib><creatorcontrib>Fauch, Laure</creatorcontrib><creatorcontrib>Granlund, Lars</creatorcontrib><creatorcontrib>Oksanen, Elina</creatorcontrib><creatorcontrib>Keinänen, Markku</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>Publicly Available Content 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>Engineering Collection</collection><jtitle>Remote sensing (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Deepak, Maya</au><au>Keski-Saari, Sarita</au><au>Fauch, Laure</au><au>Granlund, Lars</au><au>Oksanen, Elina</au><au>Keinänen, Markku</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2019-12-04</date><risdate>2019</risdate><volume>11</volume><issue>24</issue><spage>2884</spage><pages>2884-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs11242884</doi><orcidid>https://orcid.org/0000-0002-1866-736X</orcidid><orcidid>https://orcid.org/0000-0003-0458-5520</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2072-4292
ispartof Remote sensing (Basel, Switzerland), 2019-12, Vol.11 (24), p.2884
issn 2072-4292
2072-4292
language eng
recordid cdi_proquest_journals_2550295935
source MDPI - Multidisciplinary Digital Publishing Institute; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Birch trees
Canopies
Chlorophyll
Discriminant analysis
Herbivores
Leaf area
Leaves
Principal components analysis
Reflectance
Remote sensing
Spectral reflectance
Trees
Wavelengths
title Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T18%3A22%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Leaf%20Canopy%20Layers%20Affect%20Spectral%20Reflectance%20in%20Silver%20Birch&rft.jtitle=Remote%20sensing%20(Basel,%20Switzerland)&rft.au=Deepak,%20Maya&rft.date=2019-12-04&rft.volume=11&rft.issue=24&rft.spage=2884&rft.pages=2884-&rft.issn=2072-4292&rft.eissn=2072-4292&rft_id=info:doi/10.3390/rs11242884&rft_dat=%3Cproquest_cross%3E2550295935%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2550295935&rft_id=info:pmid/&rfr_iscdi=true