3a5f8f67-aede-4325-aee6-83330ea046f1 Dataset German Aerospace Center (DLR) geoservice@dlr.de 2025-02-03T08:34:48 ISO 19115-1:2014/19139 2 71084 10 90002 10 false EPSG:3035 - ETRS89-extended / LAEA Europe Tree Canopy Cover Loss Monthly - Landsat-8/Sentinel-2 - Germany, 2018-2021 2022-08-11T00:00:00 https://geoservice.dlr.de/catalogue/srv/metadata/3a5f8f67-aede-4325-aee6-83330ea046f1 DOI: 10.15489/z0g96v5kwg14 The product shows tree canopy cover loss in Germany between January 2018 and April 2021 at monthly temporal and 10 m spatial resolution. The basic principle behind this map is to compute monthly composites of the disturbance index (DI, Healey et al. 2005), a spectral index sensitive to forest disturbance, from all available Sentinel-2 and Landsat-8 data with less than 80 % cloud cover. These monthly composites are then compared to a median composite of the DI for 2017, which serves as a reference. After applying a threshold to the difference image, the time series of detected losses is checked for consistency. Only losses recorded continuously in all observations of a pixel until the end of the time series are considered. The dataset does not differentiate between the drivers of the losses. It depicts areas of natural disturbances (windthrow, fire, droughts, insect infestation) as well as sanitation and salvage logging, and regular forest harvest. The full description of the method and results can be found in Thonfeld et al. (2022). This map shows the tree canopy cover losses in Germany for the period of January 2018 until April 2021. German Aerospace Center (DLR) Frank.Thonfeld@dlr.de German Aerospace Center (DLR) geoservice@dlr.de https://geoservice.dlr.de/catalogue/srv/api/records/3a5f8f67-aede-4325-aee6-83330ea046f1/attachments/tccl_ger_p1m_ql.png large_thumbnail https://geoservice.dlr.de/catalogue/srv/api/records/3a5f8f67-aede-4325-aee6-83330ea046f1/attachments/tccl_ger_p1m_ql_s.png 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 opendata Forest Canopy Cover Loss Drought Disturbance Index Sentinel-2 Landsat-8 Germany 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 (2022)"} 20000 biota 5.99 15.02 47.30 54.99 2018-01-01T00:00:00 2021-04-30T23:59:59 GeoTIFF https://geoservice.dlr.de/web/datasets/tccl/ WWW:LINK-1.0-http--link EOC Geoservice Dataset EOC Geoservice Dataset https://geoservice.dlr.de/eoc/land/wms? OGC:WMS TREE_CANOPY_COVER_LOSS_P1M WMS Access: Tree Canopy Cover Loss Germany 2018-2021 https://download.geoservice.dlr.de/TCCL/files/ WWW:LINK-1.0-http--link HTTP download Tree Canopy Cover Loss Monthly - Germany, 2018-2021 https://www.dlr.de/content/de/artikel/news/2022/01/20220221_sorge-um-den-deutschen-wald.html WWW:LINK-1.0-http--link Sor­ge um den deut­schen Wald DLR News - Webpage 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 All Sentinel-2 and Landsat-8 data since 2017 available through the Google Earth Engine (GEE) with less than 80 % cloud cover were used. Tree Canopy Cover Loss - Germany 2018-2021 - Processing Processing: The Sentinel-2 level 2 surface reflectance dataset available from GEE was processed using sen2cor and the Landsat-8 level 2 dataset is composed of surface reflectance data processed using the land surface reflectance code (LaSRC). Quality Assurance: With regard to the spatial accuracy, our assessment based on pixel counts revealed an overall accuracy of 92.8%. The sample-based accuracy assessment revealed an overall accuracy of 98% ± 1% and an area estimate of 643,735 ha ± 120,726 ha canopy cover loss. This map is a modified version of the paper, where we removed low-confidence losses, particularly during early 2018 and the 2020/21 winter.