MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES
Identifieur interne : 001264 ( Istex/Corpus ); précédent : 001263; suivant : 001265MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES
Auteurs : C. J. LegleiterSource :
- River Research and Applications [ 1535-1459 ] ; 2013-07.
Abstract
Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel‐bed river, using data acquired through a task‐oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image‐derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio‐based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (R2 = 0.68) and an observed (field‐surveyed) versus predicted (image‐derived) R2 of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment because of geo‐referencing errors and a coarser spatial resolution. On the larger river, NAIP‐derived bathymetry was more accurate, with an observed versus predicted R2 value of 0.64 for a compressed county mosaic easily accessible via the Internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected because of limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions. Copyright © 2012 John Wiley & Sons, Ltd.
Url:
DOI: 10.1002/rra.2560
Links to Exploration step
ISTEX:EF423DDCCF01807C4ADEC782CE55F61F4A24B494Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES</title><author><name sortKey="Legleiter, C J" sort="Legleiter, C J" uniqKey="Legleiter C" first="C. J." last="Legleiter">C. J. Legleiter</name><affiliation><mods:affiliation>Department of Geography, University of Wyoming, Wyoming, Laramie, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>Correspondence to: Dr. C. J. Legleiter, Department of Geography, University of Wyoming, 1000 E. University Ave., Dept 3371, Laramie, WY 82071, USA.E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: Carl.Legleiter@uwyo.edu</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:EF423DDCCF01807C4ADEC782CE55F61F4A24B494</idno><date when="2013" year="2013">2013</date><idno type="doi">10.1002/rra.2560</idno><idno type="url">https://api.istex.fr/document/EF423DDCCF01807C4ADEC782CE55F61F4A24B494/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001264</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001264</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES</title><author><name sortKey="Legleiter, C J" sort="Legleiter, C J" uniqKey="Legleiter C" first="C. J." last="Legleiter">C. J. Legleiter</name><affiliation><mods:affiliation>Department of Geography, University of Wyoming, Wyoming, Laramie, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>Correspondence to: Dr. C. J. Legleiter, Department of Geography, University of Wyoming, 1000 E. University Ave., Dept 3371, Laramie, WY 82071, USA.E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: Carl.Legleiter@uwyo.edu</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">River Research and Applications</title><title level="j" type="abbrev">River Res. Applic.</title><idno type="ISSN">1535-1459</idno><idno type="eISSN">1535-1467</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><date type="published" when="2013-07">2013-07</date><biblScope unit="volume">29</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="760">760</biblScope><biblScope unit="page" to="780">780</biblScope></imprint><idno type="ISSN">1535-1459</idno></series><idno type="istex">EF423DDCCF01807C4ADEC782CE55F61F4A24B494</idno><idno type="DOI">10.1002/rra.2560</idno><idno type="ArticleID">RRA2560</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1535-1459</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel‐bed river, using data acquired through a task‐oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image‐derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio‐based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (R2 = 0.68) and an observed (field‐surveyed) versus predicted (image‐derived) R2 of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment because of geo‐referencing errors and a coarser spatial resolution. On the larger river, NAIP‐derived bathymetry was more accurate, with an observed versus predicted R2 value of 0.64 for a compressed county mosaic easily accessible via the Internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected because of limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions. Copyright © 2012 John Wiley & Sons, Ltd.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>C. J. Legleiter</name><affiliations><json:string>Department of Geography, University of Wyoming, Wyoming, Laramie, USA</json:string><json:string>Correspondence to: Dr. C. J. Legleiter, Department of Geography, University of Wyoming, 1000 E. University Ave., Dept 3371, Laramie, WY 82071, USA.E‐mail:</json:string><json:string>E-mail: Carl.Legleiter@uwyo.edu</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>remote sensing</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>depth retrieval</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>bathymetry</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>multispectral</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>river morphology</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>in‐stream habitat</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>monitoring</value></json:item></subject><articleId><json:string>RRA2560</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel‐bed river, using data acquired through a task‐oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image‐derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio‐based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (R2 = 0.68) and an observed (field‐surveyed) versus predicted (image‐derived) R2 of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment because of geo‐referencing errors and a coarser spatial resolution. On the larger river, NAIP‐derived bathymetry was more accurate, with an observed versus predicted R2 value of 0.64 for a compressed county mosaic easily accessible via the Internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected because of limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions. Copyright © 2012 John Wiley & Sons, Ltd.</abstract><qualityIndicators><score>8.5</score><pdfVersion>1.4</pdfVersion><pdfPageSize>595.276 x 790.866 pts</pdfPageSize><refBibsNative>true</refBibsNative><abstractCharCount>1780</abstractCharCount><pdfWordCount>11990</pdfWordCount><pdfCharCount>76833</pdfCharCount><pdfPageCount>21</pdfPageCount><abstractWordCount>255</abstractWordCount></qualityIndicators><title>MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES</title><genre><json:string>article</json:string></genre><host><volume>29</volume><publisherId><json:string>RRA</json:string></publisherId><pages><total>21</total><last>780</last><first>760</first></pages><issn><json:string>1535-1459</json:string></issn><issue>6</issue><subject><json:item><value>Research Article</value></json:item></subject><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><eissn><json:string>1535-1467</json:string></eissn><title>River Research and Applications</title><doi><json:string>10.1002/(ISSN)1535-1467</json:string></doi></host><categories><wos><json:string>science</json:string><json:string>water resources</json:string><json:string>environmental sciences</json:string></wos><scienceMetrix><json:string>natural sciences</json:string><json:string>biology</json:string><json:string>marine biology & hydrobiology</json:string></scienceMetrix></categories><publicationDate>2013</publicationDate><copyrightDate>2013</copyrightDate><doi><json:string>10.1002/rra.2560</json:string></doi><id>EF423DDCCF01807C4ADEC782CE55F61F4A24B494</id><score>0.03951158</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/EF423DDCCF01807C4ADEC782CE55F61F4A24B494/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/EF423DDCCF01807C4ADEC782CE55F61F4A24B494/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/EF423DDCCF01807C4ADEC782CE55F61F4A24B494/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Blackwell Publishing Ltd</publisher><availability><p>Copyright © 2013 John Wiley & Sons, Ltd.Copyright © 2012 John Wiley & Sons, Ltd.</p></availability><date>2012-01-05</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES</title><author xml:id="author-1"><persName><forename type="first">C. J.</forename><surname>Legleiter</surname></persName><email>Carl.Legleiter@uwyo.edu</email><affiliation>Department of Geography, University of Wyoming, Wyoming, Laramie, USA</affiliation><affiliation>Correspondence to: Dr. C. J. Legleiter, Department of Geography, University of Wyoming, 1000 E. University Ave., Dept 3371, Laramie, WY 82071, USA.E‐mail:</affiliation></author></analytic><monogr><title level="j">River Research and Applications</title><title level="j" type="abbrev">River Res. Applic.</title><idno type="pISSN">1535-1459</idno><idno type="eISSN">1535-1467</idno><idno type="DOI">10.1002/(ISSN)1535-1467</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><date type="published" when="2013-07"></date><biblScope unit="volume">29</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="760">760</biblScope><biblScope unit="page" to="780">780</biblScope></imprint></monogr><idno type="istex">EF423DDCCF01807C4ADEC782CE55F61F4A24B494</idno><idno type="DOI">10.1002/rra.2560</idno><idno type="ArticleID">RRA2560</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2012-01-05</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel‐bed river, using data acquired through a task‐oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image‐derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio‐based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (R2 = 0.68) and an observed (field‐surveyed) versus predicted (image‐derived) R2 of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment because of geo‐referencing errors and a coarser spatial resolution. On the larger river, NAIP‐derived bathymetry was more accurate, with an observed versus predicted R2 value of 0.64 for a compressed county mosaic easily accessible via the Internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected because of limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions. Copyright © 2012 John Wiley & Sons, Ltd.</p></abstract><textClass><keywords scheme="keyword"><list><head>keywords</head><item><term>remote sensing</term></item><item><term>depth retrieval</term></item><item><term>bathymetry</term></item><item><term>multispectral</term></item><item><term>river morphology</term></item><item><term>in‐stream habitat</term></item><item><term>monitoring</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>article-category</head><item><term>Research Article</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2011-07-29">Received</change><change when="2011-12-22">Registration</change><change when="2012-01-05">Created</change><change when="2013-07">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/EF423DDCCF01807C4ADEC782CE55F61F4A24B494/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component type="serialArticle" version="2.0" xml:lang="en" xml:id="rra2560"><header><publicationMeta level="product"><doi>10.1002/(ISSN)1535-1467</doi><issn type="print">1535-1459</issn><issn type="electronic">1535-1467</issn><idGroup><id type="product" value="RRA"></id></idGroup><titleGroup><title type="main" sort="RIVER RESEARCH AND APPLICATIONS">River Research and Applications</title><title type="short">River Res. Applic.</title></titleGroup></publicationMeta><publicationMeta level="part" position="60"><doi>10.1002/rra.v29.6</doi><copyright ownership="publisher">Copyright © 2013 John Wiley & Sons, Ltd.</copyright><numberingGroup><numbering type="journalVolume" number="29">29</numbering><numbering type="journalIssue">6</numbering></numberingGroup><coverDate startDate="2013-07">July 2013</coverDate></publicationMeta><publicationMeta level="unit" position="70" type="article" status="forIssue"><doi>10.1002/rra.2560</doi><idGroup><id type="unit" value="RRA2560"></id></idGroup><countGroup><count type="pageTotal" number="21"></count></countGroup><titleGroup><title type="articleCategory">Research Article</title><title type="tocHeading1">Research Articles</title></titleGroup><copyright ownership="publisher">Copyright © 2012 John Wiley & Sons, Ltd.</copyright><eventGroup><event type="manuscriptReceived" date="2011-07-29"></event><event type="manuscriptRevised" date="2011-12-02"></event><event type="manuscriptAccepted" date="2011-12-22"></event><event type="xmlCreated" agent="SPi Global" date="2012-01-05"></event><event type="publishedOnlineEarlyUnpaginated" date="2012-01-17"></event><event type="firstOnline" date="2012-01-17"></event><event type="publishedOnlineFinalForm" date="2013-07-05"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-02-08"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.6.4 mode:FullText" date="2015-10-03"></event></eventGroup><numberingGroup><numbering type="pageFirst">760</numbering><numbering type="pageLast">780</numbering></numberingGroup><correspondenceTo><lineatedText><line>Correspondence to: Dr. C. J. Legleiter, Department of Geography, University of Wyoming, 1000 E. University Ave., Dept 3371, Laramie, WY 82071, USA.</line><line>E‐mail: <email>Carl.Legleiter@uwyo.edu</email></line></lineatedText></correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:RRA.RRA2560.pdf"></link></linkGroup></publicationMeta><contentMeta><titleGroup><title type="main">MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES</title><title type="short">MAPPING RIVER DEPTH FROM AERIAL IMAGE DATA</title><title type="shortAuthors">C. J. LEGLEITER</title></titleGroup><creators><creator creatorRole="author" xml:id="rra2560-cr-0001" affiliationRef="#rra2560-aff-0001" corresponding="yes"><personName><givenNames>C. J.</givenNames><familyName>Legleiter</familyName></personName></creator></creators><affiliationGroup><affiliation countryCode="US" type="organization" xml:id="rra2560-aff-0001"><orgDiv>Department of Geography</orgDiv><orgName>University of Wyoming</orgName><address><city>Laramie</city><countryPart>Wyoming</countryPart><country>USA</country></address></affiliation></affiliationGroup><keywordGroup type="author"><keyword xml:id="rra2560-kwd-0001">remote sensing</keyword><keyword xml:id="rra2560-kwd-0002">depth retrieval</keyword><keyword xml:id="rra2560-kwd-0003">bathymetry</keyword><keyword xml:id="rra2560-kwd-0004">multispectral</keyword><keyword xml:id="rra2560-kwd-0005">river morphology</keyword><keyword xml:id="rra2560-kwd-0006">in‐stream habitat</keyword><keyword xml:id="rra2560-kwd-0007">monitoring</keyword></keywordGroup><abstractGroup><abstract type="main"><title type="main">ABSTRACT</title><p>Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel‐bed river, using data acquired through a task‐oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image‐derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio‐based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (<i>R</i><sup>2</sup> = 0.68) and an observed (field‐surveyed) versus predicted (image‐derived) <i>R</i><sup>2</sup> of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment because of geo‐referencing errors and a coarser spatial resolution. On the larger river, NAIP‐derived bathymetry was more accurate, with an observed versus predicted <i>R</i><sup>2</sup> value of 0.64 for a compressed county mosaic easily accessible via the Internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected because of limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions. Copyright © 2012 John Wiley & Sons, Ltd.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>MAPPING RIVER DEPTH FROM AERIAL IMAGE DATA</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES</title></titleInfo><name type="personal"><namePart type="given">C. J.</namePart><namePart type="family">Legleiter</namePart><affiliation>Department of Geography, University of Wyoming, Wyoming, Laramie, USA</affiliation><affiliation>Correspondence to: Dr. C. J. Legleiter, Department of Geography, University of Wyoming, 1000 E. University Ave., Dept 3371, Laramie, WY 82071, USA.E‐mail:</affiliation><affiliation>E-mail: Carl.Legleiter@uwyo.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2013-07</dateIssued><dateCreated encoding="w3cdtf">2012-01-05</dateCreated><dateCaptured encoding="w3cdtf">2011-07-29</dateCaptured><dateValid encoding="w3cdtf">2011-12-22</dateValid><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract>Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel‐bed river, using data acquired through a task‐oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image‐derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio‐based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (R2 = 0.68) and an observed (field‐surveyed) versus predicted (image‐derived) R2 of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment because of geo‐referencing errors and a coarser spatial resolution. On the larger river, NAIP‐derived bathymetry was more accurate, with an observed versus predicted R2 value of 0.64 for a compressed county mosaic easily accessible via the Internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected because of limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions. Copyright © 2012 John Wiley & Sons, Ltd.</abstract><subject><genre>keywords</genre><topic>remote sensing</topic><topic>depth retrieval</topic><topic>bathymetry</topic><topic>multispectral</topic><topic>river morphology</topic><topic>in‐stream habitat</topic><topic>monitoring</topic></subject><relatedItem type="host"><titleInfo><title>River Research and Applications</title></titleInfo><titleInfo type="abbreviated"><title>River Res. Applic.</title></titleInfo><genre type="journal">journal</genre><subject><genre>article-category</genre><topic>Research Article</topic></subject><identifier type="ISSN">1535-1459</identifier><identifier type="eISSN">1535-1467</identifier><identifier type="DOI">10.1002/(ISSN)1535-1467</identifier><identifier type="PublisherID">RRA</identifier><part><date>2013</date><detail type="volume"><caption>vol.</caption><number>29</number></detail><detail type="issue"><caption>no.</caption><number>6</number></detail><extent unit="pages"><start>760</start><end>780</end><total>21</total></extent></part></relatedItem><identifier type="istex">EF423DDCCF01807C4ADEC782CE55F61F4A24B494</identifier><identifier type="DOI">10.1002/rra.2560</identifier><identifier type="ArticleID">RRA2560</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2013 John Wiley & Sons, Ltd.Copyright © 2012 John Wiley & Sons, Ltd.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Agronomie/explor/SisAgriV1/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001264 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 001264 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Agronomie
|area= SisAgriV1
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:EF423DDCCF01807C4ADEC782CE55F61F4A24B494
|texte= MAPPING RIVER DEPTH FROM PUBLICLY AVAILABLE AERIAL IMAGES
}}
| This area was generated with Dilib version V0.6.28. |  |