<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20191213</CreaDate>
<CreaTime>15351300</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idAbs>This land cover dataset is part of the Connecticut's Changing Landscape project of the Center for Land Use Education and Research (CLEAR) at the University of Connecticut. It includes 12 classes of land cover derived from Landsat satellite imagery. Visit the website for more information and metadata. http://clear.uconn.edu/projects/landscape/
</idAbs>
<searchKeys>
<keyword>UConn</keyword>
<keyword>University of Connecticut</keyword>
<keyword>landcover</keyword>
<keyword>Land Cover</keyword>
<keyword>CLEAR</keyword>
<keyword>CT</keyword>
<keyword>Connecticut</keyword>
</searchKeys>
<idPurp>Land cover derived from 1985 satellite imagery for CT</idPurp>
<idCredit>University of Connecticut, Center for Land Use Education and Research (CLEAR)</idCredit>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
<idCitation>
<resTitle>ByTown</resTitle>
</idCitation>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">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</Data>
</Thumbnail>
</Binary>
</metadata>
