Geoservice News Latest Announcements

The IceLines dataset provides information on Antarctic ice shelf front dynamics. This allows conclusions on iceberg calving mechanisms and to observe changes in the Antarctic coastline. On the basis of Sentinel-1 data, the fronts of the largest ice shelves of Antarctica are continuously mapped with a deep learning approach (HED-Unet) [1] [2]. The generated dataset includes front positions for 36 ice shelves with a daily, monthly, seasonal and annual temporal resolution since the launch of Sentinel-1 in 2014. IceLines is updated on a monthly basis and can be explored via the EOC GeoService Webapp [3] or directly downloaded from the EOC Download Service [4]. Further information on the product is available on a separate DLR website [5].

The product shows monthly tree canopy cover loss information between January 2018 and April 2021 for Germany. The dataset is based on image composites of the disturbance index (DI) derived from Sentinel-2 and Landsat-8 time series. In addition, different maps of tree canopy cover loss in Germany per district (Landkreis) are available depicting the losses for different forest types in absolute numbers and in percentages.

The datasets are now available in the EOC Geoservice Webapp [1] and can also be downloaded from the EOC Download Service [2]. Further information on the product is available on the DLR news website [3].

The Traffic Area Map is a dataset providing information about the inventory of different traffic areas for the city of Brunswick, Germany. The map is based on aerial image sequences acquired with the DLR 3K camera system during six flight campaigns at different times of the day and year in 2019 and 2020. The dataset is now available in the EOC Geoservice Webapp [1] and can also be downloaded from the EOC Download Service [2]. Further information on the UrMo Digital project is available on a separate DLR website [3].

The new version of the Global SnowPack displays snow parameters like duration and extent globally. The yearly products show the snow cover duration for the entire hydrological year, early snow cover duration and the late snow cover duration. [1]. The daily products show the snow cover extent, which is processed in near real time [2]. Additionally, the GSP mean has been released, which is updated each year and consists of the arithmetic mean for the entire time series [3].

The datasets are now available in the EOC Geoservice Webapp and can also be downloaded from the EOC Download Service [4]. Further information on the product is available on a separate DLR website [5].

New daily, monthly and yearly burnt area products have been released in the EOC Geoservice. The daily product [1] is based on a fully automated approach and provides the extents of burned areas two hours after the according scenes have been acquired by the Copernicus Sentinel-3 satellite. Additionally, attribute information about the severity of the fire as well as the exact detection time is included in the data. It is also possible to accurately track the evolution of each burnt area. The product is iteratively and automatically updated over a period of 10 days as new satellite data becomes available. This enables the continuous improvement of the accuracy of the derived extents by minimizing the influence of disturbing factors such as cloud cover. In addition to a daily product, the results are also available in summarized form as monthly [2] and yearly composites [3]. While the information of the daily product is always kept for the last 50 days, the archived monthly products are available as early as April 2016, and the first yearly product as early as 2017. This time series allows deriving large-scale developments in terms of size of affected areas and severity. In total, approximately 100,000 satellite scenes from the Ocean and Land Colour Instrument (OLCI) sensor on the Copernicus Sentinel-3 A/B satellites have been used for processing through the end of 2021. Via Web Map and Web Feature Services (WMS/WFS), the data can be easily integrated into existing projects and applications. The methodology for deriving fire areas has been scientifically published [4], and an analysis of large fires on the Australian East Coast in 2019/20 based on the methodology is also available [5].

A new map-based search interface for atmosphere-corrected (MAJA) Sentinel-2 data over Germany now facilitates the selection of scenes for download. New features like a time slider and easy to handle view options for quicklooks bring additional comfort while browsing for images. The new map interface can be accessed within the EOC Download Service [1]. In addition, the interface prior in place, which allowed to browse for scenes through a structure of folders remains available too [2].
The MAJA atmospheric-correction processor, developed by DLR in cooperation with CNES, calculates "Bottom of Atmosphere" reflectance for Sentinel-2 images based on image stack analysis. Further information on this dataset is available at this site [3].

The Global WaterPack is a dataset containing information about the frequency of open surface water cover on a global scale. The dataset is characterized by its high temporal resolution of one day and a spatial resolution of 250 meter. The Global WaterPack Yearly layers (2003 to 2020) represent how often a location (pixel) was detected as open surface water during the corresponding year. The dataset can be visualized in the EOC Geoservice Webapp [1]. Further information on the product is available on a separate DLR website [2].

The World Settlement Footprint (WSF) 2019 and Evolution are global maps of human settlements which have been generated by means of an advanced classification technique that jointly exploits multi decadal open and free optical and radar imagery. While the World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery, the World Settlement Footprint Evolution depicts the global settlement extent on a yearly basis from 1985 to 2015 at a spatial resolution of 30 meters. The dataset can be visualized in the EOC Geoservice Webapp [1] [2] and can also be downloaded from the EOC Download Service [3] [4].

The TanDEM-X PolarDEM 90m of Antarctica is a gap-free and edited version of the TanDEM-X digital elevation model (DEM) provided in Antarctic Polar Stereographic projection (EPSG:3031) with a pixel spacing of 90 meters. The editing process comprises a smoothing of noisy areas, a gap-filling with new TanDEM-X acquisitions and a replacement of frozen and partially open sea-ice areas with geoidal undulations. The majority of the data were acquired between April 2013 to September 2014. The TanDEM-X PolarDEM is a project developed by the German Remote Sensing Data Center (DFD) at the German Aerospace Center (DLR) within the activities of the TanDEM-X mission. It is a framework for the provision of derivatives of the global Digital Elevation Model (DEM) of the TanDEM-X mission for Polar Regions [1]. The dataset can be visualized in the EOC Geoservice Webapp [2] and after registration, also be downloaded from the EOC Download Service [3].

The TimeScan Landsat 2015 layer [1, 2] is a higher-processing level baseline product providing a harmonized representation of the spectral and temporal properties of the land surface. The global TimeScan Landsat 2015 dataset was derived from over 450,000 Landsat-8 images (~500TB) collected from 2013 to 2015. The product condenses the information content of the original images to a 20th of their original size. The TimeScan Landsat 2015 layer can be analyzed in the form of a single, global, cloud-free dataset based on statistical ranges of 6 indices for such aspects as the state of vegetation, water cover, or human settlement (built-up areas). For each of the recorded indices the statistics provide the minimal, maximal and mean values along with the standard deviation and the mean slope determined for the entire period from 2013 to 2015. The TimeScan procedure is designed to help end users exploit information from masses of data that until now were too unwieldy for them to handle.