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Tree Species - Sentinel-2 - Germany, 2016

The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2016 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. The input features explicitly incorporate phenological information to capture seasonal vegetation dynamics relevant for species discrimination. A total of over 100,000 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 product was created by majority-voting on annual XGBoost Sentinel-2 tree species classifications (2016–2024) and filtering with forest structure data. If no clear majority vote was achieved, the class uncertain was assigned. The Tree Species Germany 2016 product achieves an overall F1-score of 0.95. For the dominant species pine, spruce, beech, and oak, class-wise F1-scores range from 0.92 to 0.99, while F1-scores for other widespread species such as birch, alder, larch, Douglas fir, fir, and other deciduous species range from 0.85 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.

Forest Canopy Cover Loss (FCCL) - Germany - Monthly, Administrative Level

This vector dataset is based on a 10 m resolution raster dataset that shows forest canopy cover loss (FCCL) in Germany at a monthly resolution from September 2017 to October 2025. Results at pixel level were aggregated at municipality, district, and federal state level. For the results at administrative level we differentiate between deciduous and coniferous forests. We use the stocked area map 2018 (Langner et al. 2022, https://doi.org/10.3220/DATA20221205151218 ) as a reference forest mask. We differentiate between deciduous and coniferous forests by intersecting the stocked area map with a tree species map (Blickensdoerfer et al. 2024). Pixels of the classes birch, beech, oak, alder, deciduous trees with long lifespan and deciduous trees with short lifespan were classified as deciduous forest and pixels of the classes Douglas fir, spruce, pine, larch and fir as coniferous forest. The coverage of the two datasets is not identical, which is why a few areas of the forest reference map remained unclassified. These were filled with the dominant leaf type map of the Copernicus Land Monitoring Service (CLMS 2025). Therefore, the vector data at administrative level contains information about unclassified forest areas and the total forest area as the sum of deciduous, coniferous, and unclassified forests. The FCCL confidence at pixel level is lowest at the end of the time series because the number of repeated threshold exceedance is used as a criterion to record forest canopy cover losses. Therefore, we excluded August 2024 through October 2025 from the annual and overall statistics and summarized the respective FCCL as additional attribute. The dataset provides an update of the assessment in Thonfeld et al. (2026).

SWIM Water Extent - Sentinel-1/2 - Daily

SWIM Water Extent is a global surface water product at 10 m pixel spacing based on Sentinel-1/2 data. The collection contains binary layers indicating open surface water for each Sentinel-1/2 scene. Clouds and cloud shadows are removed using ukis-csmask (see: https://github.com/dlr-eoc/ukis-csmask ) and are represented as NoData. The water extent extraction is based on convolutional neural networks (CNN). For further information, please see the following publications: https://doi.org/10.1016/j.rse.2019.05.022 and https://doi.org/10.3390/rs11192330

Tree Species - Sentinel-1/2 - Germany, 2022

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.

IceLines - Sentinel-1 - Antarctica

IceLines (Ice Shelf and Glacier Front Time Series) is an automated calving front monitoring service providing monthly ice shelf front time series of major Antarctic ice shelves. The provided time series allows to discover the dynamics of ice shelf front changes and calving events. The front positions are automatically derived from Sentinel-1 data based on a deep neuronal network called HED-U-Net. The time series covers the timespan 2014 to today (partly limited due to Sentinel-1 data availability). Incorrectly extracted fronts are truncated which might lead to gaps in the time series especially between December to March due to strong surface melt. Annual averages are calculated based on the extracted monthly fronts (excluding the summer months) and provide more robust results due to temporal aggregation

GrassLands - First Cut - Yearly, 10m

The timing of the first mowing event per year strongly impacts grassland functions and ecology, such as the provision of habitats and species composition. As grasslands in Germany are managed on small-scale units and grass grows back quickly, satellite information with high spatial and temporal resolution is necessary to capture grassland mowing dynamics. Based on Sentinel-2 data time series, mowing events are detected throughout Germany and the date of the first mowing event per year is extracted. The grassland mowing detection approach operates per pixel, including preprocessing of the Enhanced Vegetation Index (EVI) time series and a calibrated rule-based grassland mowing detection which is specified in more detail in Reinermann et al. 2022, 2023.

GrassLands - Mowing Frequency - Yearly, 10m

Grassland mowing dynamics (i.e. the timing and frequency of mowing events) have a strong impact on grassland functions and yields. As grasslands in Germany are managed on small-scale units and grass grows back quickly, satellite information with high spatial and temporal resolution is necessary to capture grassland mowing dynamics. Based on Sentinel-2 data time series, mowing events are detected throughout Germany and annual maps of the grassland mowing frequency generated. The grassland mowing detection approach operates per pixel, including preprocessing of the Enhanced Vegetation Index (EVI) time series and a calibrated rule-based grassland mowing detection which is specified in more detail in Reinermann et al. 2022, 2023.

WMS ST Sentinel_S2R10_2020_KW17

Die Daten beinhalten aufbereitete Fernerkundungsdaten der Sentinel-2 Satelliten des europäischen Erdbeobachtungsprogramms „Copernicus“. Sentinel2-Aufnahme (S2) mit 10 m Auflösung (R10) vom 21.04.2020, aufbereitet durch das Landesamt für Vermessung und Geoinformation Sachsen-Anhalt und Bereitstellung als WMS in 3 Darstellungsvarianten (RGB, CIR, NDVI) für das Gebiet Sachsen-Anhalt.

WMS ST Sentinel_S2R10_2021_KW32

Die Daten beinhalten aufbereitete Fernerkundungsdaten der Sentinel-2 Satelliten des europäischen Erdbeobachtungsprogramms „Copernicus“. Sentinel2-Aufnahme (S2) mit 10 m Auflösung (R10) vom 08.09.2021, aufbereitet durch das Landesamt für Vermessung und Geoinformation Sachsen-Anhalt und Bereitstellung als WMS in 3 Darstellungsvarianten (RGB, CIR, NDVI) für das Gebiet Sachsen-Anhalt.

Forest Canopy Cover Loss (FCCL) - Germany - Monthly, 10m

This raster dataset shows forest canopy cover loss (FCCL) in Germany at a monthly resolution from September 2017 to October 2025. It is similar to the product developed by Thonfeld et al. (2022) but was fully reprocessed and updated to reveal the most recent forest disturbance dynamics (Thonfeld et al. 2026). The combination of Sentinel-2A/B and Landsat-8/9 data allows for a high temporal resolution while the pixel size of the product is 10 m. The results are clipped to the stocked area 2018 mapped by the Thünen-Institute (Langner et al. 2022, https://doi.org/10.3220/DATA20221205151218). The dataset contains predominantly larger canopy openings resulting from different drivers but also larger clusters of standing deadwood. FCCL can result from abiotic (e.g. wind, fire, drought, hail) drivers, biotic (e.g. insects, funghi) drivers or a combination of both as well as from sanitary and salvage logging and planned harvest. The first version with canopy cover losses from January 2018 - April 2021 (Thonfeld et al. 2022) can be accessed here: https://geoservice.dlr.de/web/datasets/tccl.

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