Can wetland plant functional groups be spectrally discriminated?
Plant functional traits (PFTs) underpin ecosystem processes and therefore ecosystem service provision. If PFTs are possible to detect and discriminate spectrally, then it may be possible to use remote sensing applications to map ecosystem processes or services within and across landscapes. As a firs...
Gespeichert in:
Veröffentlicht in: | Remote sensing of environment 2018-06, Vol.210, p.25-34 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 34 |
---|---|
container_issue | |
container_start_page | 25 |
container_title | Remote sensing of environment |
container_volume | 210 |
creator | Rebelo, Alanna J. Somers, Ben Esler, Karen J. Meire, Patrick |
description | Plant functional traits (PFTs) underpin ecosystem processes and therefore ecosystem service provision. If PFTs are possible to detect and discriminate spectrally, then it may be possible to use remote sensing applications to map ecosystem processes or services within and across landscapes. As a first step towards this application, we explored whether functional groups of 22 dominant South African wetland species were spectrally separable based on their PFTs. We measured 23 biochemical and morphological PFTs in combination with spectra from 350 to 2349 nm using a handheld radiometer. First, we evaluated the possibility of accurately predicting morphological and biochemical PFTs from reflectance spectra using three approaches: spectrum averaging, redundancy analysis (RDA), and partial least squares regression (PLSR). Second, we established whether functional groups and species were spectrally distinguishable. We found seven PFTs to be important in at least two of the three approaches: four morphological and three biochemicals. Morphological traits that were important were leaf area (PLSR: r2 = 0.40, regression: r2 = 0.41), specific leaf area (r2 = 0.67), leaf mass (r2 = 0.43, r2 = 0.38), and leaf length/width ratio (r2 = 0.62). Biochemical traits that play a role in the structural composition of vegetation, like lignin content (r2 = 0.98, r2 = 0.54), concentration (r2 = 0.45) and cellulose content (r2 = 0.57, r2 = 0.49), were found to be important by at least two of the analyses. Three other traits were important in at least one of the analyses: total biomass (r2 = 0.56), leaf C/N ratio (r2 = 0.99), and cellulose concentration (r2 = 0.76). Redundancy analysis suggests that there is a large percentage (52%) of the spectrum not explained by the PFTs measured in this study. However, spectral discrimination of functional groups, and even species, appears promising, mostly in the ultraviolet A part of the spectrum. This has interesting applications for mapping PFTs using remote sensing techniques, and therefore for estimating related ecosystem processes and services.
[Display omitted]
•Four morphological and three biochemical traits were consistently related to spectra.•Lignin, C/N ratio and cellulose were most strongly related to spectra in an analysis.•Species and functional groups were most spectrally distinct in the UV-A region.•Potentially wetland ecosystem services could be mapped using these relationships. |
doi_str_mv | 10.1016/j.rse.2018.02.031 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2086369096</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0034425718300439</els_id><sourcerecordid>2086369096</sourcerecordid><originalsourceid>FETCH-LOGICAL-c407t-b5dd1d2ef2e5fe81c2a975976915862f7f6073d6e9e337a26285576a78aa06d43</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhoMouK7-AG8Fz62TtElaPKgsfsGCFz2HbDKRlNrWpFX235ulnr3MXN53eOYh5JJCQYGK67YIEQsGtC6AFVDSI7KitWxykFAdkxVAWeUV4_KUnMXYAlBeS7oidxvdZz84dbq32ZjmlLm5N5Mfet1lH2GYx5jtMIsjminorttn1kcT_Kfv9YT29pycON1FvPjba_L--PC2ec63r08vm_ttbiqQU77j1lLL0DHkDmtqmG4kb6RoEohgTjoBsrQCGyxLqZlgNedSaFlrDcJW5ZpcLXfHMHzNGCfVDnNIkFExqEUpGmhEStElZcIQY0CnxoSqw15RUAdRqlVJlDqIUsBUEpU6N0sHE_63x6Ci8dgbtD6kp5Ud_D_tX_myb_8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2086369096</pqid></control><display><type>article</type><title>Can wetland plant functional groups be spectrally discriminated?</title><source>Elsevier ScienceDirect Journals</source><creator>Rebelo, Alanna J. ; Somers, Ben ; Esler, Karen J. ; Meire, Patrick</creator><creatorcontrib>Rebelo, Alanna J. ; Somers, Ben ; Esler, Karen J. ; Meire, Patrick</creatorcontrib><description>Plant functional traits (PFTs) underpin ecosystem processes and therefore ecosystem service provision. If PFTs are possible to detect and discriminate spectrally, then it may be possible to use remote sensing applications to map ecosystem processes or services within and across landscapes. As a first step towards this application, we explored whether functional groups of 22 dominant South African wetland species were spectrally separable based on their PFTs. We measured 23 biochemical and morphological PFTs in combination with spectra from 350 to 2349 nm using a handheld radiometer. First, we evaluated the possibility of accurately predicting morphological and biochemical PFTs from reflectance spectra using three approaches: spectrum averaging, redundancy analysis (RDA), and partial least squares regression (PLSR). Second, we established whether functional groups and species were spectrally distinguishable. We found seven PFTs to be important in at least two of the three approaches: four morphological and three biochemicals. Morphological traits that were important were leaf area (PLSR: r2 = 0.40, regression: r2 = 0.41), specific leaf area (r2 = 0.67), leaf mass (r2 = 0.43, r2 = 0.38), and leaf length/width ratio (r2 = 0.62). Biochemical traits that play a role in the structural composition of vegetation, like lignin content (r2 = 0.98, r2 = 0.54), concentration (r2 = 0.45) and cellulose content (r2 = 0.57, r2 = 0.49), were found to be important by at least two of the analyses. Three other traits were important in at least one of the analyses: total biomass (r2 = 0.56), leaf C/N ratio (r2 = 0.99), and cellulose concentration (r2 = 0.76). Redundancy analysis suggests that there is a large percentage (52%) of the spectrum not explained by the PFTs measured in this study. However, spectral discrimination of functional groups, and even species, appears promising, mostly in the ultraviolet A part of the spectrum. This has interesting applications for mapping PFTs using remote sensing techniques, and therefore for estimating related ecosystem processes and services.
[Display omitted]
•Four morphological and three biochemical traits were consistently related to spectra.•Lignin, C/N ratio and cellulose were most strongly related to spectra in an analysis.•Species and functional groups were most spectrally distinct in the UV-A region.•Potentially wetland ecosystem services could be mapped using these relationships.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2018.02.031</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Biochemical plant traits ; Biochemistry ; Carbon/nitrogen ratio ; Cellulose ; Ecosystem service mapping ; Ecosystems ; Flowers & plants ; Functional groups ; Hyperspectral ; Landscape ; Leaf area ; Lignin ; Partial least squares regression (PLSR) ; Redundancy ; Redundancy analysis (RDA) ; Reflectance ; Regression analysis ; Remote sensing ; Remote sensing techniques ; Sensing techniques ; South African palmiet wetlands ; Species ; Spectra ; Spectral separability ; Spectroscopy ; Wetlands</subject><ispartof>Remote sensing of environment, 2018-06, Vol.210, p.25-34</ispartof><rights>2018 Elsevier Inc.</rights><rights>Copyright Elsevier BV Jun 1, 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c407t-b5dd1d2ef2e5fe81c2a975976915862f7f6073d6e9e337a26285576a78aa06d43</citedby><cites>FETCH-LOGICAL-c407t-b5dd1d2ef2e5fe81c2a975976915862f7f6073d6e9e337a26285576a78aa06d43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0034425718300439$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Rebelo, Alanna J.</creatorcontrib><creatorcontrib>Somers, Ben</creatorcontrib><creatorcontrib>Esler, Karen J.</creatorcontrib><creatorcontrib>Meire, Patrick</creatorcontrib><title>Can wetland plant functional groups be spectrally discriminated?</title><title>Remote sensing of environment</title><description>Plant functional traits (PFTs) underpin ecosystem processes and therefore ecosystem service provision. If PFTs are possible to detect and discriminate spectrally, then it may be possible to use remote sensing applications to map ecosystem processes or services within and across landscapes. As a first step towards this application, we explored whether functional groups of 22 dominant South African wetland species were spectrally separable based on their PFTs. We measured 23 biochemical and morphological PFTs in combination with spectra from 350 to 2349 nm using a handheld radiometer. First, we evaluated the possibility of accurately predicting morphological and biochemical PFTs from reflectance spectra using three approaches: spectrum averaging, redundancy analysis (RDA), and partial least squares regression (PLSR). Second, we established whether functional groups and species were spectrally distinguishable. We found seven PFTs to be important in at least two of the three approaches: four morphological and three biochemicals. Morphological traits that were important were leaf area (PLSR: r2 = 0.40, regression: r2 = 0.41), specific leaf area (r2 = 0.67), leaf mass (r2 = 0.43, r2 = 0.38), and leaf length/width ratio (r2 = 0.62). Biochemical traits that play a role in the structural composition of vegetation, like lignin content (r2 = 0.98, r2 = 0.54), concentration (r2 = 0.45) and cellulose content (r2 = 0.57, r2 = 0.49), were found to be important by at least two of the analyses. Three other traits were important in at least one of the analyses: total biomass (r2 = 0.56), leaf C/N ratio (r2 = 0.99), and cellulose concentration (r2 = 0.76). Redundancy analysis suggests that there is a large percentage (52%) of the spectrum not explained by the PFTs measured in this study. However, spectral discrimination of functional groups, and even species, appears promising, mostly in the ultraviolet A part of the spectrum. This has interesting applications for mapping PFTs using remote sensing techniques, and therefore for estimating related ecosystem processes and services.
[Display omitted]
•Four morphological and three biochemical traits were consistently related to spectra.•Lignin, C/N ratio and cellulose were most strongly related to spectra in an analysis.•Species and functional groups were most spectrally distinct in the UV-A region.•Potentially wetland ecosystem services could be mapped using these relationships.</description><subject>Biochemical plant traits</subject><subject>Biochemistry</subject><subject>Carbon/nitrogen ratio</subject><subject>Cellulose</subject><subject>Ecosystem service mapping</subject><subject>Ecosystems</subject><subject>Flowers & plants</subject><subject>Functional groups</subject><subject>Hyperspectral</subject><subject>Landscape</subject><subject>Leaf area</subject><subject>Lignin</subject><subject>Partial least squares regression (PLSR)</subject><subject>Redundancy</subject><subject>Redundancy analysis (RDA)</subject><subject>Reflectance</subject><subject>Regression analysis</subject><subject>Remote sensing</subject><subject>Remote sensing techniques</subject><subject>Sensing techniques</subject><subject>South African palmiet wetlands</subject><subject>Species</subject><subject>Spectra</subject><subject>Spectral separability</subject><subject>Spectroscopy</subject><subject>Wetlands</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-AG8Fz62TtElaPKgsfsGCFz2HbDKRlNrWpFX235ulnr3MXN53eOYh5JJCQYGK67YIEQsGtC6AFVDSI7KitWxykFAdkxVAWeUV4_KUnMXYAlBeS7oidxvdZz84dbq32ZjmlLm5N5Mfet1lH2GYx5jtMIsjminorttn1kcT_Kfv9YT29pycON1FvPjba_L--PC2ec63r08vm_ttbiqQU77j1lLL0DHkDmtqmG4kb6RoEohgTjoBsrQCGyxLqZlgNedSaFlrDcJW5ZpcLXfHMHzNGCfVDnNIkFExqEUpGmhEStElZcIQY0CnxoSqw15RUAdRqlVJlDqIUsBUEpU6N0sHE_63x6Ci8dgbtD6kp5Ud_D_tX_myb_8</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Rebelo, Alanna J.</creator><creator>Somers, Ben</creator><creator>Esler, Karen J.</creator><creator>Meire, Patrick</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope></search><sort><creationdate>20180601</creationdate><title>Can wetland plant functional groups be spectrally discriminated?</title><author>Rebelo, Alanna J. ; Somers, Ben ; Esler, Karen J. ; Meire, Patrick</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-b5dd1d2ef2e5fe81c2a975976915862f7f6073d6e9e337a26285576a78aa06d43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Biochemical plant traits</topic><topic>Biochemistry</topic><topic>Carbon/nitrogen ratio</topic><topic>Cellulose</topic><topic>Ecosystem service mapping</topic><topic>Ecosystems</topic><topic>Flowers & plants</topic><topic>Functional groups</topic><topic>Hyperspectral</topic><topic>Landscape</topic><topic>Leaf area</topic><topic>Lignin</topic><topic>Partial least squares regression (PLSR)</topic><topic>Redundancy</topic><topic>Redundancy analysis (RDA)</topic><topic>Reflectance</topic><topic>Regression analysis</topic><topic>Remote sensing</topic><topic>Remote sensing techniques</topic><topic>Sensing techniques</topic><topic>South African palmiet wetlands</topic><topic>Species</topic><topic>Spectra</topic><topic>Spectral separability</topic><topic>Spectroscopy</topic><topic>Wetlands</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rebelo, Alanna J.</creatorcontrib><creatorcontrib>Somers, Ben</creatorcontrib><creatorcontrib>Esler, Karen J.</creatorcontrib><creatorcontrib>Meire, Patrick</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</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>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rebelo, Alanna J.</au><au>Somers, Ben</au><au>Esler, Karen J.</au><au>Meire, Patrick</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can wetland plant functional groups be spectrally discriminated?</atitle><jtitle>Remote sensing of environment</jtitle><date>2018-06-01</date><risdate>2018</risdate><volume>210</volume><spage>25</spage><epage>34</epage><pages>25-34</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Plant functional traits (PFTs) underpin ecosystem processes and therefore ecosystem service provision. If PFTs are possible to detect and discriminate spectrally, then it may be possible to use remote sensing applications to map ecosystem processes or services within and across landscapes. As a first step towards this application, we explored whether functional groups of 22 dominant South African wetland species were spectrally separable based on their PFTs. We measured 23 biochemical and morphological PFTs in combination with spectra from 350 to 2349 nm using a handheld radiometer. First, we evaluated the possibility of accurately predicting morphological and biochemical PFTs from reflectance spectra using three approaches: spectrum averaging, redundancy analysis (RDA), and partial least squares regression (PLSR). Second, we established whether functional groups and species were spectrally distinguishable. We found seven PFTs to be important in at least two of the three approaches: four morphological and three biochemicals. Morphological traits that were important were leaf area (PLSR: r2 = 0.40, regression: r2 = 0.41), specific leaf area (r2 = 0.67), leaf mass (r2 = 0.43, r2 = 0.38), and leaf length/width ratio (r2 = 0.62). Biochemical traits that play a role in the structural composition of vegetation, like lignin content (r2 = 0.98, r2 = 0.54), concentration (r2 = 0.45) and cellulose content (r2 = 0.57, r2 = 0.49), were found to be important by at least two of the analyses. Three other traits were important in at least one of the analyses: total biomass (r2 = 0.56), leaf C/N ratio (r2 = 0.99), and cellulose concentration (r2 = 0.76). Redundancy analysis suggests that there is a large percentage (52%) of the spectrum not explained by the PFTs measured in this study. However, spectral discrimination of functional groups, and even species, appears promising, mostly in the ultraviolet A part of the spectrum. This has interesting applications for mapping PFTs using remote sensing techniques, and therefore for estimating related ecosystem processes and services.
[Display omitted]
•Four morphological and three biochemical traits were consistently related to spectra.•Lignin, C/N ratio and cellulose were most strongly related to spectra in an analysis.•Species and functional groups were most spectrally distinct in the UV-A region.•Potentially wetland ecosystem services could be mapped using these relationships.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2018.02.031</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0034-4257 |
ispartof | Remote sensing of environment, 2018-06, Vol.210, p.25-34 |
issn | 0034-4257 1879-0704 |
language | eng |
recordid | cdi_proquest_journals_2086369096 |
source | Elsevier ScienceDirect Journals |
subjects | Biochemical plant traits Biochemistry Carbon/nitrogen ratio Cellulose Ecosystem service mapping Ecosystems Flowers & plants Functional groups Hyperspectral Landscape Leaf area Lignin Partial least squares regression (PLSR) Redundancy Redundancy analysis (RDA) Reflectance Regression analysis Remote sensing Remote sensing techniques Sensing techniques South African palmiet wetlands Species Spectra Spectral separability Spectroscopy Wetlands |
title | Can wetland plant functional groups be spectrally discriminated? |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T21%3A51%3A33IST&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=Can%20wetland%20plant%20functional%20groups%20be%20spectrally%20discriminated?&rft.jtitle=Remote%20sensing%20of%20environment&rft.au=Rebelo,%20Alanna%20J.&rft.date=2018-06-01&rft.volume=210&rft.spage=25&rft.epage=34&rft.pages=25-34&rft.issn=0034-4257&rft.eissn=1879-0704&rft_id=info:doi/10.1016/j.rse.2018.02.031&rft_dat=%3Cproquest_cross%3E2086369096%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=2086369096&rft_id=info:pmid/&rft_els_id=S0034425718300439&rfr_iscdi=true |