The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
Diese DOP Daten wurden mit der Qualitätstufe TrueDOP erzeugt. Die Luftbilder werden auf Grundlage des bildbasierten Digitalen Oberflächenmodells (bDOM) entzerrt. Auf diese Weise wird nicht nur das Gelände, sondern auch jedes Objekt oberhalb des Geländes entzerrt und lagerichtig dargestellt. Verkippungen insbesondere bei der Darstellung der Gebäude werden eliminiert. Sichttote Bereiche im Orthophoto werden erheblich reduziert und der Informationsgehalt gesteigert.
Für die Verwendung im Antragsverfahren des Integrierten Verwaltungs- und Kontrollsystems (InVeKoS) werden digitale Datenebenen benötigt, die mit der erforderlichen Genauigkeit die aktuelle Situation der landwirtschaftlichen Nutzung darstellen. Das System zur Identifizierung landwirtschaftlicher Parzellen (LPIS) nach Artikel 68 der Verordnung (EU) 2021/2116 ist ein Bestandteil des Integrierten Verwaltungs- und Kontrollsystems (InVeKoS), wie in Artikel 66 der genannten Verordnung definiert. Es wird auf Ebene der Referenzparzellen angewandt. (VO 2022/1172 Art. 2 Abs. 1). Das Thüringer Flächenreferenzsystem (digitale Grundkarte Landwirtschaft, kurz DGK-Lw) basiert in Thüringen auf der Referenzparzelle Feldblock (FB) gemäß § 5 Nr. 1 GAPInVeKoS-Verordnung. Der Feldblock ist danach eine von dauerhaften Grenzen umgebene zusammenhängende landwirtschaftliche Fläche eines oder mehrerer Betriebsinhaber. Der Feldblock beinhaltet somit die Informationen über die geographische Lage der Außengrenzen der landwirtschaftlichen Nutzfläche. Referenzparzellen sind deutschlandweit eindeutig durchnummeriert (Feldblockident - FBI). Sie besitzen weiterhin eine Feldblockgröße (maximal förderfähige Fläche) und eine Bodennutzungskategorie. Folgende Feldblocktypen gibt es: - Landwirtschaftliche Nutzfläche (LF) - Landschaftselemente (LE) - Sondernutzungsflächen (SF) - Forstflächen (FF) Die Einteilung der Feldblöcke erfolgt getrennt nach den Hauptbodennutzungen Ackerland (AL), Grünland (GL), Dauerkulturen (DK), einschließlich darauf befindlicher Agroforstsysteme sowie nach den BNK für keine „landwirtschaftliche Fläche“ entsprechend § 11 Abs. 1 Nr. 3 Buchst. a, b, c und d GAPDZV (NW, EF und PK) und Sonstige. Landschaftselemente (LE) werden entsprechend der Verordnung (EU) 2022/1172 Art. 2 Abs. 7 unter definierten Bedingungen als Teil der förderfähigen landwirtschaftlichen Fläche betrachtet. In Thüringen werden diese dauerhaften Konditionalitäts-LE als separater Feldblock (FB) ausgewiesen und sind somit Teil des Thüringer Flächenreferenzsystems (Feldblockreferenz). Sie müssen einen eindeutigen Bezug zu einem LF-FB (Landwirtschaftliche Nutzfläche) besitzen, d.h. sie liegen innerhalb eine Acker-, Dauergrünland- oder Dauerkulturfläche oder grenzen in Randlage direkt an diese. Für die Herstellung der DGK-Lw werden (amtliche) Orthofotos der Thüringer Kataster- und Vermessungsverwaltung (TLBG) sowie Orthofotos aus Eigenbefliegungen des TLLLR interpretiert. Die Herkunft dieser Bilddaten beträgt jährlich jeweils 50 % der Landesfläche, so dass in jedem Jahr für die gesamte Thüringer Landesfläche aktuelle Bilddaten vorliegen.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
Das Bundesamt für Kartographie und Geodäsie (BKG) hat ein Verfahren zur bedarfsbezogenen Bereitstellung von Fernerkundungsdaten entwickelt. Für die fernerkundliche Produktion werden derzeit optische Satellitenbilddaten in der Bundesverwaltung bevorzugt eingesetzt. Hierfür ist das sogenannte Mosaik-Verfahren eine wichtige Methode. Das Verfahren wurde mit den von der EU finanzierten Sentinel-2- Daten der europäischen Erdbeobachtungsinitiative Copernicus aufgebaut und kann für jeden optischen Datensatz (inkl. Luftbilder) angewendet werden. Wir stellen mit dieser Methode einen Service zur Verfügung, über den sich optische Fernerkundungsdaten für jedes Gebiet auf der Erde fachlich und bedarfsorientiert aufbereiten lassen. Über den Dienst Web Map Service (WMS) Sen2Europe wird den Bedarfsträgern ermöglicht, vorprozessierte und aufbereitete Fernerkundungsinformationen aus dem Erdbeobachtungsprogramm Copernicus (Sentinel-2, L1C-L2A), für Europa, in bestehende eigene Fachverfahren mit einzubinden. Die Bilddaten des Jahres 2018 wurden jeweils zu einem Mosaik zusammengefügt, welches eine Bodenauflösung von 10m hat. Ein Komposit aus drei Bändern (Sentinel-2 Bänder: 2, 3, 4 (R, G, B)) sowie eine Information zu den Eingangsbilddaten werden angeboten. Die Bilddaten wurden einem radiometrischen Farbausgleichsverfahren unterzogen um ein einheitliches Erscheinungsbild zu erlangen. Für jedes Mosaik beträgt die Wolkenbedeckung über das gesamte Gebiet weniger als 3%. Über den Request-Parameter TIME kann hier gezielt ein spezifischer Jahresstand angezeigt werden (z.B. Time=2018) (ohne TIME-Parameter wird der neueste Stand angezeigt). Die nachfolgende URL des freien Webdienstes können Sie direkt, z.B. in Ihrem Geo-Informationssystem (GIS), verwenden:https://sgx.geodatenzentrum.de/wms_sen2europe
Das Bundesamt für Kartographie und Geodäsie (BKG) hat ein Verfahren zur bedarfsbezogenen Bereitstellung von Fernerkundungsdaten entwickelt. Für die fernerkundliche Produktion werden derzeit optische Satellitenbilddaten in der Bundesverwaltung bevorzugt eingesetzt. Hierfür ist das sogenannte Mosaik-Verfahren eine wichtige Methode. Das Verfahren wurde mit den von der EU finanzierten Sentinel-2- Daten der europäischen Erdbeobachtungsinitiative Copernicus aufgebaut und kann für jeden optischen Datensatz (inkl. Luftbilder) angewendet werden. Wir stellen mit dieser Methode einen Service zur Verfügung, über den sich optische Fernerkundungsdaten für jedes Gebiet auf der Erde fachlich und bedarfsorientiert aufbereiten lassen. Über den Dienst Web Map Service (WMS) Sen2Europe wird den Bedarfsträgern ermöglicht, vorprozessierte und aufbereitete Fernerkundungsinformationen aus dem Erdbeobachtungsprogramm Copernicus (Sentinel-2, L1C-L2A), für Europa, in bestehende eigene Fachverfahren mit einzubinden. Die Bilddaten des Jahres 2021 wurden jeweils zu einem Mosaik zusammengefügt, welches eine Bodenauflösung von 10m hat. Ein Komposit aus drei Bändern (Sentinel-2 Bänder: 2, 3, 4 (R, G, B)) sowie eine Information zu den Eingangsbilddaten werden angeboten. Die Bilddaten wurden einem radiometrischen Farbausgleichsverfahren unterzogen um ein einheitliches Erscheinungsbild zu erlangen. Für jedes Mosaik beträgt die Wolkenbedeckung über das gesamte Gebiet weniger als 3%. Über den Request-Parameter TIME kann hier gezielt ein spezifischer Jahresstand angezeigt werden (z.B. Time=2018) (ohne TIME-Parameter wird der neueste Stand angezeigt). Die nachfolgende URL des freien Webdienstes können Sie direkt, z.B. in Ihrem Geo-Informationssystem (GIS), verwenden:https://sgx.geodatenzentrum.de/wms_sen2europe
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