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WMS SL Sentinel-2 CIR - Sentinel-2 CIR 2017

Sentinel-2 Falschfarbenbild (ColoredInfraRed), Kombination der Spektralkanäle B8 (rot), B4 (grün) und B3 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert die Sentinel-2 Falschfarbenbilder(CIR) des Jahr 2017.

Sentinel-2 Mosaike rgb und cir Hessen

Sentinel-2 cir- und rgb-Mosaike in möglichst wolkenfreier Monatsfolge für 2021-06, 2021-07, 2021-09, 2021-12, 2022-02, 2022-03, 2022-06, 2022-08, 2022-09, 2023-02, 2023-04, 2023-06, 2024-01, 2024-08, 2024-09, 2025-02, 2025-03, 2025-04, 2025-05, 2025-06, 2025-08, 2025-12 in 10 m Bodenauflösung.

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

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.

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.

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.

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 September 2024. It is similar to the product developed by Thonfeld et l. (2022) but was fully reprocessed and updated to reveal the most recent forest disturbance dynamics. 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 Johann-Heinrich-von-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.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2015

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2015.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2018

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2018.

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