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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>ISPRS</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprsarchives-XXXIX-B8-199-2012</article-id>
<title-group>
<article-title>HYDROPERIOD CLASSIFICATION OF CERVANTES COOLIMBA COASTAL WETLANDS USING LANDSAT TIME SERIES IMAGERY</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>van Dongen</surname>
<given-names>R. J.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Behn</surname>
<given-names>G. A.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Coote</surname>
<given-names>M.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Shanahan</surname>
<given-names>A.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Setiawan</surname>
<given-names>H.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Dept, of Environment and Conservation, Perth, Australia</addr-line>
</aff>
<pub-date pub-type="epub">
<day>28</day>
<month>07</month>
<year>2012</year>
</pub-date>
<volume>XXXIX-B8</volume>
<fpage>199</fpage>
<lpage>202</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2012 R. J. van Dongen et al.</copyright-statement>
<copyright-year>2012</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
</license>
</permissions>
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<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XXXIX-B8/199/2012/isprs-archives-XXXIX-B8-199-2012.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XXXIX-B8/199/2012/isprs-archives-XXXIX-B8-199-2012.pdf</self-uri>
<abstract>
<p>Geomorphic classification of wetlands relies on information regarding landform and hydroperiod. Additional attributes of wetland
size, vegetation cover and salinity can be collected for lower order classification. Hydroperiod is important in determining features
that characterize the ecological function of the wetland. This study examines how Landsat time series imagery was used to describe
the hydroperiod of wetlands along the coastal zone between Cervantes to Coolimba in Western Australia. Inundation extent maps
were derived using 17, Landsat Band 5 images captured between 1988 and 2011. The number of times a wetland basin was more
than 10 % inundated was then calculated. This inundation frequency dataset was then the basis for hydroperiod classification.
Wetlands were classified as being permanently inundated if they had more than 10% inundation in 12 or more of the 17 images
available (approximately 70%). A Landsat image captured 2 weeks prior to field work was used to determine Band 5 thresholds to
identify areas of inundation and assess classification accuracy. Field measurements of surface water cover, depth, secchi depth,
vegetation cover and salinity were taken across 16 plots located close to inundation boundaries. The accuracy of the inundation maps
was highly dependent on the degree of vegetation cover. Mapping accuracy was heavily influenced by vegetation cover and achieved
an overall accuracy of 87.5%. The resulting hydroperiod datasets provides an accurate record of inundation frequency which can be
used to aid classification of wetlands and also allows changes to inundation frequency over time to be assessed.</p>
</abstract>
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