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Found 143 results.

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.

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 SL Sentinel-2 CIR - Sentinel-2 CIR 2023

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 2023.

WMS SL Sentinel-2 NDVI - Sentinel-2 NDVI 2018

Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2018.

WMS SL Sentinel-2 NDVI - Sentinel-2 NDVI 2020

Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert den Sentinel-2 Normierter Differenzierter Vegetationsindex (NDVI) des Jahr 2020.

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.

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

SoilSuite – Sentinel-2 – Europe, 5 year composite (2018-2022)

The SoilSuite contains a collection of different image data products that provide information about the spectral and statistical properties of European soils and other bare surfaces such as rocks. It is created using DLR's Soil Composite Mapping Processor (ScMAP), which utilises the Sentinel-2 data archive. SCMaP is a specialised processing chain for detecting and analysing bare soils/surfaces on a large (continental) scale. Bare surface and soil pixels are selected using a combined NDVI and NBR index (PVIR2) that optimises the exclusion of photosynthetically active and non-active vegetation. The index is calculated and applied for each individual pixel. All SoilSuite products are calculated based on the available Sentinel-2 scenes recorded between January 2018 and December 2022 in Europe. The data package excludes all scenes with a cloud cover of > 80 % and a sun elevation of < 20°. The spectral composite products are calculated from the mean value after extensive removal of clouds, haze and snow effects at both scene and pixel level. The spectral data products are available at a pixel size of 20 m and contain 10 Sentinel-2 bands (B02, B03, B04, B05, B06, B07, B08, B08a, B11, B12). The SoilSuite comprises: (a) “Bare Surface Reflectance Composite – Mean” that provides the spectral properties of soils that vary due to different soil organic carbon (SOC) content, soil moisture and soil minerology. This product is often used for spectral and digital soil mapping approaches, (b) “Bare Surface Reflectance Composite - Standard deviation” informing about the spectral dynamic of bare surfaces and soils, (c) “Bare Surface Reflectance Composite – 95% Confidence” contains information about the reliability of the spectral information due to the number of valid observations per pixel, (d) “Bare Surface Statistics Product” provides the number of bare soil occurrences over the total number of valid observations (Band 1), the number of bare soil occurrences (Band 2) and the total number of valid observations (Band 3), (e) “Mask” is a product that aggregates simple landcover classes that occur during the time period between 2018 - 2022 (Sentinel-2). The three-class Mask contains bare surface occurrences (1), permanent vegetation (2) and other surfaces such as water bodies, urban areas, roads (3). Additionally, the SoilSuite provides (f) “Reflectance Composite – Mean” that represents the mean reflectance of all valid Sentinel-2 observations between 2018 – 2022 including vegetation, bare and other surfaces, and (g) “Reflectance Composite – Standard deviation”, which contains the standard deviation per band for all valid Sentinel-2 observations between 2018 – 2022.

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 2023

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 2023.

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