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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Devendra Dahal</dc:contributor>
  <dc:contributor>Claudia Young</dc:contributor>
  <dc:contributor>Gyanesh Chander</dc:contributor>
  <dc:contributor>Shuguang Liu</dc:contributor>
  <dc:creator>Shengli Huang</dc:creator>
  <dc:date>2011</dc:date>
  <dc:description>&lt;p&gt;&lt;span&gt;Spatiotemporal variations of wetland water in the Prairie Pothole Region are controlled by many factors; two of them are temperature and precipitation that form the basis of the Palmer Drought Severity Index (PDSI). Taking the 196&lt;/span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;&lt;span&gt;km&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;Cottonwood Lake area in North Dakota as our pilot study site, we integrated PDSI, Landsat images, and aerial photography records to simulate monthly water surface. First, we developed a new Wetland Water Area Index (WWAI) from PDSI to predict water surface area. Second, we developed a water allocation model to simulate the spatial distribution of water bodies at a resolution of 30&lt;/span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;&lt;span&gt;m. Third, we used an additional procedure to model the small wetlands (less than 0.8&lt;/span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;&lt;span&gt;ha) that could not be detected by Landsat. Our results showed that i) WWAI was highly correlated with water area with an R&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;of 0.90, resulting in a simple regression prediction of monthly water area to capture the intra- and inter-annual water change from 1910 to 2009; ii) the spatial distribution of water bodies modeled from our approach agreed well with the water locations visually identified from the aerial photography records; and iii) the R&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;between our modeled water bodies (including both large and small wetlands) and those from aerial photography records could be up to 0.83 with a mean average error of 0.64&lt;/span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;&lt;span&gt;km&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;within the study area where the modeled wetland water areas ranged from about 2 to 14&lt;/span&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;&lt;span&gt;km&lt;/span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;span&gt;. These results indicate that our approach holds great potential to simulate major changes in wetland water surface for ecosystem service; however, our products could capture neither the short-term water change caused by intensive rainstorm events nor the wetland change caused by human activities.&lt;/span&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1016/j.rse.2011.08.002</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Elsevier</dc:publisher>
  <dc:title>Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>