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imageryBaseMapsEarthCover

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  • Land use in the Greater Region according to Corine Land Cover 2006 - Source: EEA - Corine Land Cover 2006 European seamless vector database: version 15 (08/2011)

  • This map provides a presentation of the Upper Rhine territory through some figures.

  • Land use in the Greater Region according to Corine Land Cover 201 - Source: European Environment Agency - Corine Land Cover (CLC) 2018 v20

  • This map shows the rate of urbanisation at the municipal level in the Upper Rhine area.

  • - Change in soil sealing 2(imperviousness) 2009-2015 - Territorial entities: cantons (LOR and LUX), Kreise (RLP and SL), arrondissements (WAL) - Thematic data sources: Imperviousness Density (IMD)2009 & 2015, European Environment Agency. Calculations: LISER 2018 - Geodata sources: © GeoBasis-DE / BKG 2017; IGN France 2017; NGI-Belgium 2017; ACT Luxembourg 2017. Harmonization: SIG-GR / GIS-GR 2018

  • This map shows the geology of the Upper Rhine area.

  • This map shows the land use in the Upper Rhine area.

  • Forest types in the Greater Region 2019 - Source: INTERREG VA project Regiowood II (https://www.regiowood2.info/en) - Data sources and processing: - France/Lorraine: SPOT4-5 images acquired in 2005, RapidEye images acquired in 2010 and 2011. Data processing: ICube-SERTIT University of Strasbourg (https://sertit.unistra.fr) - Belgium: aerial image coverage from 2009, 2012 and 2016, LiDAR coverage 2014. Data processing: Gembloux Agro-Bio Tech University of Liège (https://www.gembloux.ulg.ac.be/gestion-des-ressources-forestieres) - Germany and Luxembourg: cadastral data, Landsat 8 from 2014. Data processing: Umweltfernerkundung & Geoinformatik University of Trier (https://www.fernerkundung.uni-trier.de/) - Data for the entire Greater Region from 2016: Sentinel-2 A/B. Although the classification is based on different methodologies in different regions, the final result is a consistent cross-border map. The accuracy of the classification is 88%. Please note that the date of the "Aerial Imagery" background map data may differ from the Regiowood data depending on the sub-entity of the Greater Region.

  • Forest types in the Greater Region 2016 - Source: INTERREG VA project Regiowood II (https://www.regiowood2.info/en) - Data sources and processing: - France/Lorraine: SPOT4-5 images acquired in 2005, RapidEye images acquired in 2010 and 2011. Data processing: ICube-SERTIT University of Strasbourg (https://sertit.unistra.fr) - Belgium: aerial image coverage from 2009, 2012 and 2016, LiDAR coverage 2014. Data processing: Gembloux Agro-Bio Tech University of Liège (https://www.gembloux.ulg.ac.be/gestion-des-ressources-forestieres) - Germany and Luxembourg: cadastral data, Landsat 8 from 2014. Data processing: Umweltfernerkundung & Geoinformatik University of Trier (https://www.fernerkundung.uni-trier.de/) - Data for the entire Greater Region from 2016: Sentinel-2 A/B. Although the classification is based on different methodologies in different regions, the final result is a consistent cross-border map. The accuracy of the classification is 88%. Please note that the date of the "Aerial Imagery" background map data may differ from the Regiowood data depending on the sub-entity of the Greater Region.

  • This map is a game. You have to find the names of the missing cities on the document. Good luck!