Description: Spatial distribution of selected demersal fish species in the German Bight. Data source: Data from environmental impact assessments (EIA) under the permit procedures of the Federal Maritime and Hydrographic Agency (BSH) in the North Sea EEZ and research data of the Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research; time period: 2000 to 2014, mainly spring and autumn data (EIA data), but also summer and winder data (AWI data) Sampling standards: Data of EIAs follow the standard investigation concept StUK 1-3 (BSH 2007), AWI data mainly collected in accordance with the ICES sampling standard (Rumohr 1999). Sampling gears: EIA-data with 7-8 m (partly 6 m) beam trawl (haul: 15 min), AWI-data with a 2-3 m beam trawl (haul: 5-15 min); trawling speed 3-4 kn, codend mesh size 10 mm Sampling: 1 haul per station and sampling date, data were recorded on board or subsamples were stored frozen for further analysis, analysis of abundance and biomass (kg wet weight) per species Data analysis: science information system of demersal fish data, examination of quality and plausibility, data harmonisation, product computation by AWI Product description: Grid: 10x10 km²; available selectable parameter: number of stations, minimum, maximum, mean, median and standard deviation of density (km-²) per species; classification method: natural jenks (Jenks Caspall algorithm); Note: The products contain a different classification of species density! Note: Please regard different value ranges! Note: Data refer exclusively to demersal fish species spectrum! Cited literature Rumohr, H. (1999). "Soft bottom macrofauna: Collection, treatment, and quality assurance of samples." ICES Techniques in Environmental Sciences, No. 27: 1-19. BSH (2007): Standard Investigation of the Impacts of Offshore Wind Turbines on the Marine Environment (StUK 3), Hamburg.

Description: Spatial distribution of selected macrozoobenthic species in the German Bight. Data source: Data from environmental impact assessments (EIA) under the permit procedures of the Federal Maritime and Hydrographic Agency (BSH) in the North Sea EEZ and research data of the Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research; time period: 1997 to 2011, mainly spring and autumn data (EIA data), but also summer and winter data (AWI data) Sampling standards: Data of EIAs follow the standard investigation concept StUK 1-3 (BSH 2007), AWI data collected in accordance with the ICES sampling standard (Rumohr 1999). Sampling gears: mainly van Veen grabs (0.1 square metre, 30-95 kg), few stations by box corer (0.1 square metre, 160 kg), Nephrops norvegicus and Goneplax rhomboides sampled by beam trawl and dredge (1-3 m width) Sampling: 1-3 replicates per station, fixation in 4 % buffered formalin seawater solution, dredge and beam-trawl data recorded on board or subsamples frozen stored, abundance and biomass (g wet weight) per species Data analysis: science information system of benthic invertebrate data, examination of quality and plausibility, data harmonisation, product computation by AWI Product description: Grid: 5x5 km² for grab data, 10x10 square kilometre for data on N. norvegicus and G. rhomboides from beam trawl and dredge hauls; available selectable parameter: number of stations, minimum, maximum, mean, median and standard deviation of density (m-2) per species; classification method: natural jenks (Jenks Caspall algorithm), Note: The products contain a different classification of species density! Note: Please regard different value ranges! Rumohr, H. (1999). "Soft bottom macrofauna: Collection, treatment, and quality assurance of samples." ICES Techniques in Environmental Sciences, No. 27: 1-19. BSH (2007): Standard "Investigation of the Impacts of Offshore Wind Turbines on the Marine Environment (StUK 3)", Hamburg. For more information, please visit: https://gdi.bsh.de/en/data/Benthos-Density_Information_Benthos_Dichte_DE.pdf

This product consists of global gap free Leaf area index (LAI) time series, based on MERIS full resolution Level 1B data. It is produced as a series of 10-day composites in geographic projection at 300m spatial resolution. The processing chain comprises geometric correction, radiometric correction and pixel identification, LAI calculation with the BEAM MERIS vegetation processor, re-projection to a global grid, and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing we applied time series analysis to fill data gaps and filter outliers using the technique of harmonic analysis in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (less than 10 degrees), topography and intermittent data reception. We applied our technique for the whole period of observation (Jul 2002 - Mar 2012). Validation, was performed using VALERI and BigFoot data.

This product is a shape file of all detected forest patches in the Paraguayan Chaco that are larger than 10 hectars fort he years 2000, 2010, and 2020. Every forest patch contains information on its perimeter, size, shape, and core area. By looking at all forest patches together, an impression can be gained of the fragmentation of the forest in the Paraguayan Chaco. Proximity is a measure of fragmentation. Areas of large and close by forest patches show high proximity values while isolated patches or patchest hat are only surrounded by small forest patches, have a small proximity. The Core area index quantifies the share of core area in the entire forest patch area. Thereby, corea area is the area of a forest patch with at least 500m distance to the edge of the forest. The Shape index is calculated from perimeter and area of a patch. The fragementation of a forest often has the effect that the ratio between area and perimeter is affected. The edge lengths become longer while the surface area becomes smaller.

The product contains information of tree canopy cover loss in Germany per district (Landkreis) between January 2018 and April 2021 at monthly temporal resolution. The information is aggregated at from the 10 m spatial resolution Sentinel-2 and Landsat-based raster product (Tree Canopy Cover Loss Monthly - Landsat-8/Sentinel-2 - Germany, 2018-2021). The method used to derive this product as well as the mapping results are described in detail in Thonfeld et al. (2022). The map depicts areas of natural disturbances (windthrow, fire, droughts, insect infestation) as well as sanitation and salvage logging, and regular forest harvest without explicitly differentiating these drivers. The vector files contain information about tree canopy cover loss area per forest type (deciduous, coniferous, both) and per year (2018, 2019, 2020, January-April 2021, and January 2018-April 2021) in absolute numbers and in percentages. In addition, the vector files contain the district area and the total forest area per district.

The dataset is based on an analysis combining Sentinel-1 (SAR), -2 (Multispectral) and GEDI (Global Ecosystem Dynamics Investigation, LiDAR) data to model vegetation structure information. The derived products show high-spatial resolution maps (10 m) of total canopy cover (cover density in %), Foliage height diversity (Fhd) index in meter, Plant area index (Pai) in meter and canopy height (rh95) in meter.

In this service, information on underwater obstacles is presented, which is available in the "Deutsches Unterwasserhindernisauskunftssystem" (DUWHAS) of the BSH. The data are displayed according to the symbolisation of the international nautical charts (INT 1).