a3c5b57f-8d87-42d2-a3b2-8f5a230964ae Dataset German Aerospace Center (DLR) geoservice@dlr.de 2025-02-14T13:08:04 ISO 19115-1:2014/19139 2003/Cor.1:2006 2 102120 10 86697 10 false EPSG:4326 Forest Structure - Sentinel-1/2, GEDI - Germany, Yearly 2023-11-16T12:48:00 https://geoservice.dlr.de/catalogue/srv/metadata/a3c5b57f-8d87-42d2-a3b2-8f5a230964ae The product shows forest structure information on canopy height, total canopy cover and Above-ground biomass density (AGBD) in Germany as annual products in 10 m spatial resolution. The products were generated using a machine learning modelling approach that combines complementary spaceborne remote sensing sensors, namely GEDI (Global Ecosystem Dynamics Investigation; NASA; full-waveform LiDAR), Sentinel-1 (Synthetic-Aperture-Radar; ESA, C-band) and Sentinel-2 (Multispectral Instrument; ESA; VIS-NIR-SWIR). Sample estimates on forest structure from GEDI were modelled in 10 m spatial resolution as annual products based on spatio-temporal composites from Sentinel-1 and -2. The derived products are the first consistent data sets on canopy height, total canopy cover and AGBD for Germany which enable a quantitative assessment of recent forest structure dynamics, e.g. in the context of repeated drought events since 2018. The full description of the method and results can be found in the publication of Kacic et al. (2023). This map shows the forest structure in Germany based on information on canopy height, total canopy cover and Above-ground biomass density (AGBD). German Aerospace Center (DLR) patrick.kacic@dlr.de German Aerospace Center (DLR) geoservice@dlr.de https://geoservice.dlr.de/catalogue/srv/api/records/a3c5b57f-8d87-42d2-a3b2-8f5a230964ae/attachments/fs_germany_ql.jpg large_thumbnail https://geoservice.dlr.de/catalogue/srv/api/records/a3c5b57f-8d87-42d2-a3b2-8f5a230964ae/attachments/fs_germany_ql_s.jpg thumbnail Land cover GEMET - INSPIRE themes, version 1.0 2008-06-01 geonetwork.thesaurus.local.theme.inspire-theme National Spatial scope 2024-05-23 geonetwork.thesaurus.external.place.SpatialScope.en DLR EOC Forest Forest structure Drought Machine Learning Model Germany Yearly Sentinel-1 Sentinel-2 GEDI 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. Es gelten keine Zugriffsbeschränkungen 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 Namensnennung - 4.0 International (CC BY 4.0)", "url": "http://dcat-ap.de/def/licenses/cc-by/4.0", "quelle": "Copyright DLR (year of production)"} 20000 imageryBaseMapsEarthCover 5.87 15.04 47.27 55.06 2017-04-01T00:00:00 GeoTIFF https://geoservice.dlr.de/web/datasets/forest_structure WWW:LINK-1.0-http--link EOC Geoservice Dataset EOC Geoservice Dataset https://geoservice.dlr.de/eoc/land/wms? OGC:WMS FOREST_STRUCTURE_DE_AGBD_P1Y Forest Structure Germany - Above-Ground Biomass Density - Yearly https://geoservice.dlr.de/eoc/land/wms? OGC:WMS FOREST_STRUCTURE_DE_COVER_P1Y Forest Structure Germany - Canopy Cover - Yearly https://geoservice.dlr.de/eoc/land/wms? OGC:WMS FOREST_STRUCTURE_DE_RH95_P1Y Forest Structure Germany - Canopy Height - Yearly https://geoservice.dlr.de/eoc/land/wms?SERVICE=WMS&REQUEST=GetCapabilities OGC:WMS-http-get-capabilities https://download.geoservice.dlr.de/FSG/files/ WWW:LINK-1.0-http--link EOC Download Service EOC Download Service (Forest Structure Germany) 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 Sentinel-1 and Sentinel-2 (less than 60 % cloud coverage) data for the months April to September available through the Google Earth Engine (GEE) were used. In addition, GEDI L2A, L2B, and L4A data from 2019 to 2022 for the months June to August were used. At the date of data processing GEDI data for 2022 was only available for the month June. Processing: The Sentinel-1 GRD dataset available from GEE was processed ARD using the methodology explained in Mullissa et al. 2021 (https://doi.org/10.3390/rs13101954). Sentinel-2 L2A surface reflectance was processed using sen2cor in GEE. Quality assurance: With regard to the modelling accuracy, a statistical comparison to independent samples of GEDI was calculated. According to the mean statistics for all years, the canopy height models reach a coefficient of variation (R2) of 64.6 % and a Mean-Absolute-Error (MAE) of 4.4 m. The mean accuracy of total canopy cover models for all years amounts to 67.0 % (R2) and 12.5 % (MAE). The Above-ground biomass density (AGBD) models have a accuracy of 58.8 % in R2 and 41.0 Mg/ha in MAE.