4f5d5933-250e-493a-9fea-cd1b7c967e6a Dataset German Aerospace Center (DLR) geoservice@dlr.de 2026-01-08T08:38:20 ISO 19115-1:2014/19139 2 65270 10 88222 10 false EPSG:3035 - ETRS89-extended / LAEA Europe Tree Species - Sentinel-1/2 - Germany, 2022 2025-07-10T00:00:00 https://geoservice.dlr.de/catalogue/srv/metadata/4f5d5933-250e-493a-9fea-cd1b7c967e6a DOI: 10.15489/smh8w3j8i962 The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2022 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data, radar data from Sentinel-1, and a digital elevation model. The input features explicitly incorporate phenological information to capture seasonal vegetation dynamics relevant for species discrimination. A total of over 80,000 training and test samples were compiled from publicly accessible sources, including urban tree inventories, Google Earth Pro, Google Street View, and field observations. The final classification was generated using an XGBoost machine learning algorithm. The Tree Species Germany product achieves an overall F1-score of 0.89. For the dominant species pine, spruce, beech, and oak, class-wise F1-scores range from 0.76 to 0.98, while F1-scores for other widespread species such as birch, alder, larch, Douglas fir, and fir range from 0.88 to 0.96. The product provides a consistent, high-resolution, and up-to-date representation of tree species distribution across Germany. Its transferable, cost-efficient, and repeatable methodology enables reliable large-scale forest monitoring and offers a valuable basis for assessing spatial patterns and temporal changes in forest composition in the context of ongoing climatic and environmental dynamics. The Tree Species Germany product represents the distribution of dominant tree species across Germany for the year 2022 at a spatial resolution of 10 meters. German Aerospace Center (DLR) Marco.Wegler@dlr.de German Aerospace Center (DLR) geoservice@dlr.de https://geoservice.dlr.de/catalogue/srv/api/records/4f5d5933-250e-493a-9fea-cd1b7c967e6a/attachments/tree_species_ql.jpg large_thumbnail https://geoservice.dlr.de/catalogue/srv/api/records/4f5d5933-250e-493a-9fea-cd1b7c967e6a/attachments/tree_species_ql_s.jpg thumbnail Land cover GEMET - INSPIRE themes, version 1.0 2008-06-01 geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme National Spatial scope 2024-05-23 geonetwork.thesaurus.external.place.SpatialScope.en DLR EOC forest tree species machine learning time-series Sentinel-2 Sentinel-1 Germany opendata Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use. There are no limitations on public access to spatial data sets and services. Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0/ Terms of use: License, https://creativecommons.org/licenses/by/4.0/ {"id": "cc-by-4.0", "name": "Creative-Commons - Attribution 4.0 International (CC BY 4.0)", "url": "http://dcat-ap.de/def/licenses/cc-by/4.0", "quelle": "Copyright DLR (2025)"} 20000 biota 6.0926 15.5865 47.1996 55.0773 2021-03-01T00:00:00 2023-10-31T23:59:59 GeoTIFF https://download.geoservice.dlr.de/TREE_SPECIES_DE/files/ WWW:LINK-1.0-http--link HTTP download Tree Species Germany https://geoservice.dlr.de/eoc/land/wms?SERVICE=WMS&REQUEST=GetCapabilities OGC:WMS-http-get-capabilities https://geoservice.dlr.de/eoc/land/wms? OGC:WMS TREE_SPECIES_DE_2022 Tree Species - Sentinel-1/2 - Germany, 2022 https://doi.org/10.1080/01431161.2025.2530236 WWW:LINK-1.0-http--link Scientific publication https://geoservice.dlr.de/web/datasets/treespecies_de_2022 WWW:LINK-1.0-http--link EOC Geoservice Dataset Conformity_001 INSPIRE Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 See the referenced specification. true Input data: Sentinel-2, -1, Copernicus GLO-30 DEM ML Random Forest and XGBoost Accuracy information: F1-Score of 0.89