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. An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results: overall accuracy: 88.4% class: user's accuracy / producer's accuracy (number of reference points n) forest: 96.7% / 94.3% (1410) low vegetation: 70.6% / 84.0% (844) water: 98.5% / 94.2% (69) built-up: 98.2% / 89.8% (983) bare soil: 19.7% / 58.5% (41) agriculture: 91.7% / 85.3% (1653) Incora report with details on methods and results: pending
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 (2019), 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. An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results: overall accuracy: 91.9% class: user's accuracy / producer's accuracy (number of reference points n) forest: 98.1% / 95.9% (1410) low vegetation: 76.4% / 91.5% (844) water: 98.4% / 92.8% (69) built-up: 99.2% / 97.4% (983) bare soil: 35.1% / 95.1% (41) agriculture: 95.9% / 85.3% (1653) Incora report with details on methods and results: pending
This landcover map was produced with a classification method developed in 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 (2020), 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. An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results: overall accuracy: 88.4% class: user's accuracy / producer's accuracy (number of reference points n) forest: 95.0% / 93.8% (1410) low vegetation: 73.4% / 86.5% (844) water: 98.5% / 92.8% (69) built-up: 98.9% / 95.8% (983) bare soil: 23.9% / 82.9% (41) agriculture: 94.6% / 83.2% (1653) Incora report with details on methods and results: pending
This landcover map was produced with a classification method developed in 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. Even though the project is concluded, the annual land cover classification product is continuously generated. 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 (2020), 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. An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results: overall accuracy: 83.5% class: user's accuracy / producer's accuracy (number of reference points n) forest: 90.6% / 91.9% (1410) low vegetation: 69.2% / 82.8% (844) water: 97.0% / 94.2% (69) built-up: 96.5% / 97.4% (983) bare soil: 8.5% / 68.3% (41) agriculture: 96.6% / 68.4% (1653) Compared to the previous years, the overall accuracy and accuracies of some classes is reduced. 2021 was a rather cloudy year in Germany, which means that the detection of agricultural areas is hampered as it is based on the variance of the NDVI throughout the year. With fewer cloud-free images available, the NDVI variance is not fully covered and as no adaptations have been applied to the algorithm, agricultural fields may get classified as low vegetation or bare soil more often. Another reason for lower classification accuracy is the significant damage that occured to forest areas due to storm and bark beetle. The validation dataset was generated based on aerial imagery from the years 2018/2019 which and is slowly becoming obsolete. An up-to-date validation dataset will be applied. Incora report with details on methods and results: pending
Die Europäische Kommission stellte am 12. Dezember 2016 die Anwendung „Global Surface Water Explorer“ offiziell vor. Es handelt sich um eine allgemein zugängliche Online-Anwendung mit interaktiven Karten, die helfen soll, europäische und internationale Strategien zur Bekämpfung des Klimawandels und zur Wasserwirtschaft zu verbessern. Auf den von der Gemeinsamen Forschungsstelle der Kommission und Google Earth Engine entwickelten Karten können Veränderungen der Oberflächengewässer der Erde in den letzten 32 Jahren abgelesen werden. Auf den Karten lässt sich eine Zunahme von Oberflächengewässern in ganz Europa verzeichnen, die auf die Errichtung von Staudämmen und Veränderungen in der Bewirtschaftung und Speicherung dieses Wassers zurückgeht. Allerdings sind die Vorkommen in einigen Teilen Asiens erheblich zurückgegangen. Über 70 % der Nettoverluste sind in Kasachstan, Usbekistan, Iran, Afghanistan und Irak zu verzeichnen. Weltweit sind fast 90 000 km² ganz verschwunden und über 72 000 km² sind nur für einige Monate im Jahr vorhanden. Die Karten sind für alle Nutzer kostenlos über die Google Earth Engine-Plattform zugänglich. Dieses Projekt stellt auch einen Beitrag zum Copernicus Global Land Service dar, dem weltweiten Landüberwachungsdienst des Copernicus-Programms, das einen kostenlosen und freien Zugang zum gesamten Datensatz bietet. Die Copernicus-Satelliten Sentinel-1 und Sentinel-2 werden zudem zusätzliche Radar- und optische Satellitenbilder aufnehmen, durch die die Detailtreue und Genauigkeit der in dem Global Surface Water Explorer enthaltenen Informationen zukünftig weiter verbessert werden können.
European Digital Elevation Model for European Base Layer within BESTMAP (EU_DEM v1.1; Copernicus Land Monitoring Service 2016).
Das Projekt "Evolution of Copernicus Land Services based on Sentinel data (ECoLaSS)" wird vom Umweltbundesamt gefördert und von GAF AG durchgeführt. The Copernicus programme, coordinated and managed by the European Commission, delivers environmental information (largely based on Earth Observation satellite data) in the form of Copernicus Services, addressing six thematic areas: Land, Marine, Atmosphere, Climate Change, Emergency Management and Security. The new Sentinel satellites, recently extended through the successful launch of Sentinel-3, will deliver an unprecedented volume of EO data in high spatial, radiometric and temporal resolution, providing a huge potential for monitoring applications within the Land Monitoring Service - at continental and global scale. The synergistic use of Sentinel-1/2/3 opens up the possibility for new applications, such as the use of time series in the area of Land Monitoring. The ECoLaSS project (Evolution of Copernicus Land Services based on Sentinel data) aims to develop methods and algorithms for pre-operational prototypes improving and developing future specific Copernicus Land services. These prototypes, representing new or improved Copernicus Land Cover and Land Use products, will be demonstrated by means of test/demonstrations sites distributed over Europe and Africa, representing multiple bio-geographic regions and biomes. Prototypes will be designed with high spatial and thematic accuracy, in a timely manner for a pan-European operational Roll-out with the potential for global applications. ECoLaSS will promote the innovation potential of new land monitoring services and applications and might thus contribute to a growing 'Copernicus Economy' by boosting (new) Copernicus CORE Land Services and value-added applications (Downstream Services). It is expected, that such new services will bring new opportunities with a wide range of dedicated applications to the market from 2020 onwards and thus significantly contribute to a positive evolution of the Copernicus Land services.
Das Projekt "Unterstützung der nationalen Copernicus Fachkoordinatoren für den Landdienst und Verankerung von Copernicus im Umweltbundesamt (COPUBA)" wird vom Umweltbundesamt gefördert und von Umweltbundesamt durchgeführt. Ziel des Vorhabens ist die Unterstützung der Aktivitäten des nationalen Copernicus Fachkoordinators für den Landdienst (einschl. der In Situ Komponente) z. B. durch die Verbesserung des Informationsflusses zwischen Nutzern und Dienstleistern, sowie europäischer und nationaler Ebene; Sicherstellung und Einbeziehung von deutschen Nutzeranforderungen auf der europäischen und nationalen Ebene; Szenarienentwicklung zu neuen nationalen Diensten; Organisation und Durchführung von Workshops, Informations- und Schulungsveranstaltungen und Öffentlichkeitsarbeit. Ferner soll durch eigene Pilotvorhaben eine Verankerung von Copernicus im Umweltbundesamt (UBA) erreicht werden. Es soll eine Einbindung der Copernicus Daten und Dienste in die UBA-eigene IT-Infrastruktur bzw. in die Geschäftsprozesse erreicht werden. Bereits laufende oder geplante Projekte wie Corine Land Cover, Satellitenfernerkundung (SFE) in der Antarktis oder die Ermittlung von Indikatoren der deutschen Anpassungsstrategie an den Klimawandel mit Hilfe der SFE sollen unterstützt und begleitet werden. Die Unterstützung des nationalen Fachkoordinators für den Copernicus Landdienst erfolgt z. B. durch Teilnahme an den Sitzungen der nationalen Fachkoordinatoren, Mitarbeit bei der Öffentlichkeitsarbeit, der Informationsvermittlung von der europäischen bis zur kommunalen Ebene sowie der Vorbereitung und Durchführung von Workshops zum Thema Landdienst. Die Verankerung von Copernicus Daten und Diensten im Umweltbundesamt erfolgt durch interne Informations- und Schulungsveranstaltungen und die fachliche Unterstützung bei Forschungsvorhaben mit SFE-Hintergrund und durch die Initiierung und Begleitung eigener Pilotprojekte.
Das Projekt "Sentinels Synergy for Agriculture (SENSAGRI)" wird vom Umweltbundesamt gefördert und von Universidad Valencia durchgeführt. In the emerging Copernicus Earth monitoring era, Europe provides Earth Observation (EO) data from Sentinel-1 (S1) and Sentinel-2 (S2) on a free and open data policy basis. In response of the EO Work programme 'EO-3-2016: Evaluation of Copernicus Services', Sentinels Synergy for Agriculture (SENSAGRI) aims to exploit the unprecedented capacity of S1 and S2 to develop an innovative portfolio of prototypes agricultural monitoring services. When used alone either optical or radar sensors allow the mapping of crop types. However more robust, accurate, frequently updated and comprehensive crop maps are expected from the seldom exploited synergy of both types of measurements. The same holds when dealing with crop status, health and stresses. Experimental studies have demonstrated that fusion of optical and radar data opens up prospects for enhanced monitoring capabilities. SENSAGRI will exploit the synergy of optical and radar measurements to develop three prototype services capable of near real time operations: (1) surface soil moisture (SSM), (2) green and brown leaf area index (LAI) and (3) crop type mapping. These prototypes shall provide a baseline for advanced services that can boost the competitiveness of the European agro-industrial sector. SENSAGRI proposes four advanced proof-of-concept services: (i) yield/biomass, (ii) tillage change, (iii) irrigation and (iv) advanced crop maps. The algorithms will be developed and validated in four European agricultural test areas in Spain, France, Italy and Poland, which are representative of the European crop diversity, and their usefulness demonstrated in at least two non-European countries. In order to refine the specifications of the products and to iteratively assess the services, actors of the agricultural sector will be involved using a Living Lab approach. The combination of user-centered approach and of state-of-the-art algorithms will establish a sound foundation for deciding of a new Copernicus land service.
Zur Erzeugung eines flächendeckenden Landbedeckungsdatensatzes für das DWD Stadtklimamodell MUKLIMO_3 wurden die Copernicus Urban Atlas Daten durch Corine Landcover Daten ergänzt. Die Originalklassen der beiden Eingangsdatensätze wurden beibehalten und sind auf den Internetseiten des Copernicus Land Monitoring Service dokumentiert (https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012, https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012). Der Datensatz wurde im Rahmen des Bundesministeriums für Verkehr und digitale Infrastruktur (BMVI) geförderten Copernicus Projekts „Nutzung des GMES Urban Atlas für die Stadtklimamodellierung“ (GUAMO) unter dem Förderkennzeichen 50 EW 1610 erstellt (Laufzeit August 2016 – September 2018). Der Datensatz ist als Polygon-Feature (Feature-Class) einer ESRI Geodatenbank in folgendem Koordinatensystem verfügbar: ETRS_1989_LAEA WKID: Authority: 3035 (EPSG) Projection: Lambert_Azimuthal_Equal_Area False_Easting: 4321000,0 False_Northing: 3210000,0 Central_Meridian: 10,0 Latitude_Of_Origin: 52,0 Linear Unit: Meter (1,0) Geographic Coordinate System: GCS_ETRS_1989 Angular Unit: Degree (0,0174532925199433) Prime Meridian: Greenwich (0,0) Datum: D_ETRS_1989 Spheroid: GRS_1980 Semimajor Axis: 6378137,0 Semiminor Axis: 6356752,314140356 Inverse Flattening: 298,257222101
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