This change map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. The map indicates land cover changes between the years 2016 and 2019. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/d93aecdd-2cfc-41fe-accc-d636b8ad4f6a https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/1da8a111-847d-41ee-91bb-3e9b9f5d278f To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster: - Modefilter (3x3) to eliminate isolated pixels and edge effects - Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and thus a change is likely. Gain ranges from 0 to 1, all changes < 0.5 are omitted. - Change areas < 1ha are removed The resulting map has the following nomenclature: 0: No Change 1: Change from low vegetation to forest 2: Change from water to forest 3: Change from built-up to forest 4: Change from bare soil to forest 5: Change from agriculture to forest 6: Change from forest to low vegetation 7: Change from water to low vegetation 8: Change from built-up to low vegetation 9: Change from bare soil to low vegetation 10: Change from agriculture to low vegetation 11: Change from forest to water 12: Change from low vegetation to water 13: Change from built-up to water 14: Change from bare soil to water 15: Change from agriculture to water 16: Change from forest to built-up 17: Change from low vegetation to built-up 18: Change from water to built-up 19: Change from bare soil to built-up 20: Change from agriculture to built-up 21: Change from forest to bare soil 22: Change from low vegetation to bare soil 23: Change from water to bare soil 24: Change from built-up to bare soil 25: Change from agriculture to bare soil 26: Change from forest to agriculture 27: Change from low vegetation to agriculture 28: Change from water to agriculture 29: Change from built-up to agriculture 30: Change from bare soil to agriculture - Contains modified Copernicus Sentinel data (2016/2019), processed by mundialis Incora report with details on methods and results: pending

Die SedDB verbindet die für die Binnenwasserstraßen vorliegenden Feststofftransportdaten (Geschiebe und Schwebstoff aus Vollprofilmessungen) mit den sedimentologischen Daten der Gewässersohle (Kornverteilung, Schichtaufbau, Sohlbeschreibung etc.) in einer Datenhaltung und macht sie für morphologische Anwendungen (quantitative Fragestellungen), für ökologische und qualitativ-gewässerkundliche Fragestellungen verfügbar.

Projekt EO2Heaven, Daten über die Luftverschmutzung und der zu erwartenden Atemwegs- und Herz-Kreislauferkrankungen in Sachsen, basierend auf den Berechnungen des TU Dresden EO2Heaven Teams. Fallstudie 1 (Umwelteffekte auf Atemwegs- und Herz-Kreislauf-Erkrankungen). GEOSS AIP 5 - 2012.

This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2016), processed by mundialis Processing was performed for blocks of federal states and individual maps were mosaicked afterwards. For each class 100,000 pixels from the potential training areas were extracted as training data. Incora report with details on methods and results: pending

This collection contains tropospheric NO2 columns for Germany and surrounding areas derived from Sentinel-5P/TROPOMI Level-1B data. The Sentinel-5P tropospheric NO2 data is generated by DLR and provided in the framework of the mFUND-Project "S-VELD". The tropospheric NO2 data are vertical column densities with the unit "µmol/m2". Sentinel-5P observes Germany once per day at ~12:00 UTC. These daily observations are gridded onto a regular UTM grid. The day and measurement time are included in the netCDF data file. Only tropospheric NO2 data for cloud-free Sentinel-5P measurements are provided (cloud fraction < ~0.2). Sentinel-5P cloud fraction data is included in this collection as well.

This collection contains monthly mean surface NO2 concentrations for Germany derived from Sentinel-5P/TROPOMI data. The Sentinel-5P NO2 data is generated by DLR and provided in the framework of the mFUND-Project "S-VELD". The surface NO2 data are concentrations with the unit "μg/m3". Sentinel-5P observes Germany once per day at ~12:00 UTC and only cloud-free measurements (cloud fraction less than ~0.2) are used. The Sentinel-5P surface NO2 data within each month are averaged and gridded onto a regular UTM grid. The number of measurements used in the calculation of the averaged value are included in this collection as well.