<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20190219</CreaDate>
<CreaTime>19233600</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>Map</resTitle>
</idCitation>
<idAbs/>
<searchKeys>
<keyword>Ethiopia</keyword>
<keyword>land cover</keyword>
<keyword>modelling</keyword>
<keyword>land scape</keyword>
<keyword>vegetation</keyword>
<keyword>forest</keyword>
</searchKeys>
<idPurp>The main methodological approach implemented to map the complex landscapes of Ethiopia at the required scales for the WLRC land cover layer was the majority and minority concept of landscape segregation that translated into the HICU-based mapping (Homogenous Image Classification Units). The employment of such an ‘exclusion-based’ approach (e.g. sub-setting of the Landsat imagery and gradually reducing the minorities/majorities) can be considered as a breakthrough in deriving important land cover information in heterogeneous landscapes, such as the rainfed agricultural area of Ethiopia. This approach made it possible to distinguish cultivated land from other land use or land cover classes</idPurp>
<idCredit/>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">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</Data>
</Thumbnail>
</Binary>
</metadata>
