<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20230323</CreaDate>
<CreaTime>13083000</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
<MapLyrSync>TRUE</MapLyrSync>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>HRL_GrasslandProbabilityIndex_2015</resTitle>
</idCitation>
<idAbs>The Grass Vegetation Probability Index describes the reliability of the multi-seasonal optical
grassland classification for the reference year 2015 (EO data from plus/minus 1 year). It is a
measure for the reliability of the grassland class assignment and indicates to which degree
grassland could be separated from other vegetated land cover types (see Figure 2).
The probability index is directly related to the EO data situation: while dense time series of
meaningful scenes will lead to high classification reliabilities, poor data situations will provide low
probability index rates. An optimum data scenario is used to calibrate the index to the range of 1-
100%.</idAbs>
<searchKeys>
<keyword>High</keyword>
<keyword>Resolution</keyword>
<keyword>Layer</keyword>
<keyword>HRL</keyword>
<keyword>Grassland</keyword>
<keyword>Vegetation</keyword>
<keyword>Probability</keyword>
<keyword>Index</keyword>
<keyword>GRAVPI</keyword>
<keyword>Copernicus</keyword>
<keyword>Copernicus</keyword>
<keyword>Land</keyword>
</searchKeys>
<idPurp>https://sdi.eea.europa.eu/catalogue/srv/api/records/845c7684-703b-475a-b7f3-d7bd719b15f8</idPurp>
<idCredit>European Environment Agency (EEA)</idCredit>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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==</Data>
</Thumbnail>
</Binary>
</metadata>
