<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20221001</CreaDate>
<CreaTime>10554600</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
<MapLyrSync>TRUE</MapLyrSync>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>MSPA_forest_2015</resTitle>
</idCitation>
<idAbs>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;The &lt;/SPAN&gt;&lt;SPAN&gt;map &lt;/SPAN&gt;&lt;SPAN&gt;provides information about morphological spatial pattern of EU forest in 201&lt;/SPAN&gt;&lt;SPAN&gt;5&lt;/SPAN&gt;&lt;SPAN&gt;. The input data is the Forest Area 201&lt;/SPAN&gt;&lt;SPAN&gt;5&lt;/SPAN&gt;&lt;SPAN&gt; based on Copernicus HRL Forest products at 100m spatial resolution. This binary forest mask is divided into seven morphological spatial pattern classes: Core, Islet, Perforation, Edge, Loop, Bridge, and Branch (Forest), while the Background (Non-Forest) is divided into three classes: Core-Opening (Background inside Core), Border-Opening (Background along Foreground boundary) and Background outside of Foreground.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;The Morphological Spatial Pattern Analysis (MSPA) methodology was developed by JRC. Details on the methodology, are described in MSPA-Manual, which is included in the GuidosToolbox software; Soille &amp;amp; Vogt, 2008. In short, MSPA is a customized sequence of mathematical morphological operators targeted at the description of the geometry and connectivity of the image components.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</idAbs>
<searchKeys>
<keyword>Forest</keyword>
<keyword>geodata</keyword>
<keyword>land</keyword>
<keyword>cover</keyword>
</searchKeys>
<idPurp>https://sdi.eea.europa.eu/catalogue/srv/api/records/11984527-7724-4c1a-b175-285b88534d94</idPurp>
<idCredit>© EEA</idCredit>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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==
</Data>
</Thumbnail>
</Binary>
</metadata>
