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.
<span><strong>Definitionen:</strong> Hydrodynamik beschreibt die Bewegung von Fluiden und die dabei wirkenden Kräfte. Hydrodynamische Kennwerte sind zeitintegrierte, beschreibende Parameter dieser Prozesse. So tragen bspw. die grundlegenden Tidekenngrößen des Tidehochwassers, des Tideniedrigwassers sowie der damit eng verbundenen Werte für Tidestieg, Tidefall und Tidehub dazu bei, die Dynamik der Tide herauszuarbeiten.</span> <span><strong>Datenerzeugung:</strong> Aus numerischen Simulationsdaten wurden physikalische Größen wie beispielsweise Wasserstand oder Strömungsgeschwindigkeit in festen zeitlichen Intervallen unter Berücksichtigung erreichbarer Genauigkeiten berechnet. Diese Simulationsdaten wurden mit Datenanalysemethoden zu hydrodynamischen Kennwerten wie beispielsweise dem Tidehub zusammengefasst. Es wurden harmonische Analysen des Wasserstandes durchgeführt und Tidekennwerte des Wasserstands bzw. statistische Langzeitkennwerte von Wasserstand, Strömungsgeschwindigkeit, Salzgehalt, Wassertemperatur und Schwebstoffgehalt berechnet. </span> <span><strong>Produkte:</strong> Hydrodynamische Kennwerte aus dem Projekt TrilaWatt basieren auf der Analyse der numerischen Simulation von Tide, Seegang, Salzgehalt, Temperatur und Schwebstoffkonzentration im Bereich des trilateralen Wattenmeers (Niederlande -nl, Deutschland -de, Dänemark -dk) und der Deutschen Bucht als Jahresmittel für das Jahr 2016. Die Daten werden als regelmäßiges 20 m Raster im GeoTIFF-Format bereitgestellt. Kennwerte werden nur für Berechnungszellen bereitgestellt, die im Analysezeitraum immer überflutet waren. In den Datenäquivalenten (*_no_filter) wurde diese Maskierung nicht angewendet. Nicht-gefilterte Datenäquivalente (no_filter) sind, falls physikalisch sinnvoll, ebenfalls erstellt worden. Bei nicht-gefilterten Datenprodukten ist zu beachten, dass die Anzahl der den Mittelwerten zugrundeliegenden Werte vor allem im Flachwasserbereich durch intertidales Trockenfallen geringer ist und damit die Mittelwertbildung beeinträchtigt ist. Die Anzahl an validen Datenpunkten bzw. Tiden pro Jahr (Anzahl gültiger Datenpunkte bzw. Anzahl Tidehochwasser) wird als Rasterdatei zur Einordnung nicht-gefilterter Produkte mitgeliefert.</span> <span><strong>Produktliste:</strong> - Tidehub und Tidehoch- und Tideniedrigwasser: 5-, 50- und 95% Quantil <br> - Laufzeitverschiebung zur Referenzposition „Leuchtturm Alte Weser“ von Tidehoch- und Tideniedrigwasser: Jahresmittelwerte <br> - Tidemittelwasser: 50% Quantil <br> - M2-Partialtide: Amplitude und Phase <br> - Tidehochwasser und validen Datenpunkte: Anzahl pro Jahr<br> - Wasserstand: 1-, 50- und 99% Quantil, Mittelwert, Minimum, Maximum <br> - Strömungsgeschwindigkeit: tiefengemittelter Mittelwert, 99- und 99,9% Quantil des Betrags <br> - Strömungsgeschwindigkeit: tiefengemittelter Betrag und x- und y-Komponente des Residuums <br> - Strömungsgeschwindigkeit: tiefengemittelter mittlerer, kubierter Betrag <br> - Bodenschubspannung: 99% Quantil, Mittelwert<br> - Salzgehalt, Temperatur und Schwebstoffkonzentration: tiefengemitteltes 1- und 99% Quantil und Mittelwert (Schwebstoffkonzentration als Summe aus drei Fraktionen mit einer Sinkgeschwindigkeit ws = 0,25, 1,5 und 7 mm/s) <br> - Signifikante Wellenhöhe des Seegangs: 50-, 95- und 99% Quantil, (Jahres-) Mittelwert und Maximalwert <br> - Mittlere Wellenperiode: Jahresmittelwert bei maximaler signifikanter Wellenhöhe<br> - Seegangsrichtung: x- und y-Komponenten des Residuums </span> <span><strong>English:</strong> This web service contains annual averages and quantiles of tidal characteristics, annual averages and quantiles of hydrographic parameters (e.g., depth-averaged salinity, suspended sediments, or sea water temperature), and tidal constituents from harmonic analyses that were estimated from numerical simulations of the year 2016. Data are distributed on regular 20 m grids as GeoTIFFs. </span> <span><strong>Download:</strong> A download is located under references (in German: "Verweise und Downloads"). </span>
Definitionen: In den Geowissenschaften beschreibt eine Topographie die Erdoberfläche. In aquatischen Systemen wird der Begriff oft synonym zum Begriff “Bathymetrie” für die Höhenlage der Gewässersohle verwendet. Im Forschungsprojekt TrilaWatt bezeichnen topographische Daten die subtidale, intertidale und supratidale Höhenverteilung im Bereich der 12 Seemeilen-Zone des Wattenmeers. Datenerzeugung: Die Basis der Datenerzeugung bilden topographische Modelle aus einer umfangreichen Datenbasis von See- und Landvermessungen verschiedenster Datentypen. Diese werden mit einem datengetriebenem Simulationsmodell über räumlich-zeitliche Interpolationsverfahren zusammengelegt. Als Kompromisse zwischen der ständige morphodynamische Aktivität im Wattenmeer und der deutlich geringeren Messfrequenz werden in TrilaWatt topographische Modelle als Jahrestopographien erstellt. Produkt: Für den Zeitraum von 2015 bis einschließlich 2021 wird ein gerastertes topographisches Modell in der 12 Seemeilen Zone des Wattenmeers mit einer gerasterten Auflösung von 10 m in Raum und Zeit zum jeweiligen Gültigkeitszeitraum des 01.07. interpoliert. Das Datenprodukt wird im GeoTIFF Format bereitgestellt. Zur Einschätzung der Unschärfe des topographischen Datensatzes werden zu jedem Datenprodukt Datenquellenkarten veröffentlicht. Weiterhin werden prototypische Topographien für die Jahre 1996-2014 (NL) sowie für 2022 (NL und DE) bereitgestellt. Weitere Produkte: Min-Z/Max-Z, Morphologischer Raum und Morphologischer Drive (2015-2021). Zitat für diesen Datensatz (DOI) - Zeitraum 2015-2021: Milbradt, P., Pineda Leiva, D. F. (2024): TrilaWatt: Topographie (2015-2021) [Dataset]. Bundesanstalt für Wasserbau. https://doi.org/10.48437/366eab-3640c8 Zitat für diesen Datensatz (DOI) - Zeitraum 1996-2014, 2022: Milbradt, P., Pineda Leiva, D. F. (2025): TrilaWatt: Topographie (1996-2014, 2022) [Data set]. Bundesanstalt für Wasserbau. https://doi.org/10.48437/4baaf0-aeaf58 English: Topography describes the study of the forms and features of land surfaces. Topographic data in aquatic systems is often also referred to as bathymetry. TrilaWatt topography data merged a large number of observational data to annual topographies using a data-driven interpolation model. Data are distributed in 10m grids as GeoTIFF files within the 12 nautical mile zone of the Wadden Sea's coast line. Additional products: Min-Z/Max-Z, Bed Elevation Range and morphological Drive (2015-2021). Download A download is located under references (in German: "Verweise und Downloads").
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.
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