Investigating spatial structure in specific tree species in ancient semi‐natural woodland using remote sensing and marked point pattern analysis
Identifieur interne : 000E53 ( Istex/Corpus ); précédent : 000E52; suivant : 000E54Investigating spatial structure in specific tree species in ancient semi‐natural woodland using remote sensing and marked point pattern analysis
Auteurs : Peter M. Atkinson ; Giles M. Foody ; Peter W. Gething ; Ajay Mathur ; Colleen K. KellySource :
- Ecography [ 0906-7590 ] ; 2007-02.
Abstract
Remote sensing classification has the potential to provide important information, such as tree species distribution maps, to ecologists, at a range of spatial and temporal scales. However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi‐natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter‐dependent at a range of spatial scales. Together, the remote sensing classification and simulation approaches provide the basis for exploiting more fully the potential of remote sensing to provide information of value to ecologists.
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DOI: 10.1111/j.0906-7590.2007.04726.x
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Kelly</name></author></analytic><monogr></monogr><series><title level="j">Ecography</title><title level="j" type="abbrev">Ecography</title><idno type="ISSN">0906-7590</idno><idno type="eISSN">1600-0587</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><pubPlace>Copenhagen</pubPlace><date type="published" when="2007-02">2007-02</date><biblScope unit="volume">30</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="88">88</biblScope><biblScope unit="page" to="104">104</biblScope></imprint><idno type="ISSN">0906-7590</idno></series><idno type="istex">7AB6419A82192B02654D78F5C84ADD07D59C2085</idno><idno type="DOI">10.1111/j.0906-7590.2007.04726.x</idno><idno type="ArticleID">ECOG4726</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0906-7590</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Remote sensing classification has the potential to provide important information, such as tree species distribution maps, to ecologists, at a range of spatial and temporal scales. However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi‐natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter‐dependent at a range of spatial scales. Together, the remote sensing classification and simulation approaches provide the basis for exploiting more fully the potential of remote sensing to provide information of value to ecologists.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Peter M. Atkinson</name></json:item><json:item><name>Giles M. Foody</name></json:item><json:item><name>Peter W. Gething</name></json:item><json:item><name>Ajay Mathur</name></json:item><json:item><name>Colleen K. Kelly</name></json:item></author><articleId><json:string>ECOG4726</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Remote sensing classification has the potential to provide important information, such as tree species distribution maps, to ecologists, at a range of spatial and temporal scales. However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi‐natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter‐dependent at a range of spatial scales. Together, the remote sensing classification and simulation approaches provide the basis for exploiting more fully the potential of remote sensing to provide information of value to ecologists.</abstract><qualityIndicators><score>7.94</score><pdfVersion>1.3</pdfVersion><pdfPageSize>539 x 751 pts</pdfPageSize><refBibsNative>true</refBibsNative><abstractCharCount>1664</abstractCharCount><pdfWordCount>9556</pdfWordCount><pdfCharCount>57093</pdfCharCount><pdfPageCount>17</pdfPageCount><abstractWordCount>245</abstractWordCount></qualityIndicators><title>Investigating spatial structure in specific tree species in ancient semi‐natural woodland using remote sensing and marked point pattern analysis</title><refBibs><json:item><host><author></author><title>Anon., . 2006. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.</title></host></json:item><json:item><author><json:item><name>P. M. Atkinson</name></json:item><json:item><name>P. 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However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi‐natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter‐dependent at a range of spatial scales. Together, the remote sensing classification and simulation approaches provide the basis for exploiting more fully the potential of remote sensing to provide information of value to ecologists.</p></abstract></profileDesc><revisionDesc><change when="2007-02">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/7AB6419A82192B02654D78F5C84ADD07D59C2085/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 version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Blackwell Publishing Ltd</publisherName><publisherLoc>Copenhagen</publisherLoc></publisherInfo><doi origin="wiley" registered="yes">10.1111/(ISSN)1600-0587</doi><issn type="print">0906-7590</issn><issn type="electronic">1600-0587</issn><idGroup><id type="product" value="ECOG"></id><id type="publisherDivision" value="ST"></id></idGroup><titleGroup><title type="main" sort="ECOGRAPHY">Ecography</title><title type="short">Ecography</title></titleGroup></publicationMeta><publicationMeta level="part" position="02001"><doi origin="wiley">10.1111/eco.2007.30.issue-1</doi><numberingGroup><numbering type="journalVolume" number="30">30</numbering><numbering type="journalIssue" number="1">1</numbering></numberingGroup><coverDate startDate="2007-02">February 2007</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="0008800" status="forIssue"><doi origin="wiley">10.1111/j.0906-7590.2007.04726.x</doi><idGroup><id type="unit" value="ECOG4726"></id><id type="supplier" value="eco4726"></id></idGroup><countGroup><count type="pageTotal" number="17"></count></countGroup><titleGroup><title type="tocHeading1">Research papers</title></titleGroup><eventGroup><event type="firstOnline" date="2007-11-06"></event><event type="publishedOnlineFinalForm" date="2007-11-06"></event><event type="xmlConverted" agent="Converter:BPG_TO_WML3G version:2.3.4 mode:FullText source:FullText result:FullText" date="2010-03-29"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-01-19"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-10-16"></event></eventGroup><numberingGroup><numbering type="pageFirst" number="88">88</numbering><numbering type="pageLast" number="104">104</numbering></numberingGroup><correspondenceTo>P. M. Atkinson (<email>pma@soton.ac.uk</email>), and P. W. Gething, School of Geography, Univ. of Southampton, Highfield, Southampton, SO17 1BJ, UK. – G. M. Foody, School of Geography, Univ. of Nottingham, Univ. Park, Nottingham, NG7 2RD, UK. – A. Mathur, Punjab Remote Sensing Centre, Ludhiana 141004, Punjab, India. – C. K. Kelly, Dept of Zoology, Univ. of Oxford, South Parks Road, Oxford OX1 3PS, UK.</correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:ECOG.ECOG4726.pdf"></link></linkGroup></publicationMeta><contentMeta><unparsedEditorialHistory>Manuscript Accepted 26 October 2006</unparsedEditorialHistory><countGroup><count type="figureTotal" number="8"></count><count type="tableTotal" number="7"></count><count type="formulaTotal" number="17"></count><count type="referenceTotal" number="60"></count></countGroup><titleGroup><title type="main">Investigating spatial structure in specific tree species in ancient semi‐natural woodland using remote sensing and marked point pattern analysis</title></titleGroup><creators><creator creatorRole="author" xml:id="cr1"><personName><givenNames>Peter M.</givenNames><familyName>Atkinson</familyName></personName></creator><creator creatorRole="author" xml:id="cr2"><personName><givenNames>Giles M.</givenNames><familyName>Foody</familyName></personName></creator><creator creatorRole="author" xml:id="cr3"><personName><givenNames>Peter W.</givenNames><familyName>Gething</familyName></personName></creator><creator creatorRole="author" xml:id="cr4"><personName><givenNames>Ajay</givenNames><familyName>Mathur</familyName></personName></creator><creator creatorRole="author" xml:id="cr5"><personName><givenNames>Colleen K.</givenNames><familyName>Kelly</familyName></personName></creator></creators><abstractGroup><abstract type="main" xml:lang="en"><p>Remote sensing classification has the potential to provide important information, such as tree species distribution maps, to ecologists, at a range of spatial and temporal scales. However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore <i>Acer pseudoplatanus</i> and ash <i>Fraxinus excelsior</i> from a 400 ha ancient semi‐natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter‐dependent at a range of spatial scales. 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However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi‐natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter‐dependent at a range of spatial scales. 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