<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20190122</CreaDate>
<CreaTime>19312500</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>Map</resTitle>
</idCitation>
<idAbs/>
<searchKeys>
<keyword>Ethiopia</keyword>
<keyword>land use</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 use 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. Extensive fieldwork was neededd to distinguish different land use within a homogenous land cover class.</idPurp>
<idCredit/>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">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</Data>
</Thumbnail>
</Binary>
</metadata>
