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.

Forest types in the Greater Region 2021 (Lorraine: 2020) - Source: INTERREG VA project Regiowood II (https://www.regiowood2.info/en) - Data: Sentinel-2 from summer 2020 (Grand-Est), spring 2021 (Rhineland-Palatinate, Saarland, Luxembourg) and early summer 2021 (Wallonia). 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%. - Classifications methods: - France:: SPOT4-5 images acquired in 2005 from the vegetation layer of the BD Topo (IGN). 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) - Rhineland-Palatinate and Saarland: use of cadastral data (ATKIS). Data processing: Umweltfernerkundung & Geoinformatik University of Trier (https://www.uni-trier.de/universitaet/fachbereiche-faecher/fachbereich-vi/faecher/erdbeobachtung-und-klimaprozesse/umweltfernerkundung-und-geoinformatik) - Luxembourg: use of the dataset "arrondissements forestiers" from ANF. Data processing: Umweltfernerkundung & Geoinformatik University of Trier (https://www.uni-trier.de/universitaet/fachbereiche-faecher/fachbereich-vi/faecher/erdbeobachtung-und-klimaprozesse/umweltfernerkundung-und-geoinformatik) 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.