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).
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
This data set represents the yearly, accumulated results of the final (10-day) version of the fire perimeters from the "Burnt Area Daily NRT Incremental Product - Europe, Sentinel-3" dataset. The burn perimeters are spatially and temporally correlated, so that interrelated detections from consecutive observations are combined into a single feature. A perimeter is interpreted as belonging to a given event if a spatial overlap exists within a time frame of 15 days. Besides the geometry, attribute information is also combined while considering the size of the perimeter as a weighting factor. Each feature contains information about the final fire perimeter, Date/Time of the first detection, and the averaged burn severity