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.
Per- und Polyfluoralkylsubstanzen (PFAS) sind eine komplexe Gruppe von künstlich hergestellten Chemikalien mit einzigartigen wasser- und ölabweisenden Eigenschaften. Sie werden seit Jahrzehnten für die Herstellung zahlreicher Verbraucherprodukte verwendet, z. B. für antihaftbeschichtete Kochgeschirre, atmungsaktive Textilien oder Lebensmittelverpackungen. Die Aufnahme über Lebensmittel und Trinkwasser ist der Hauptexpositionsweg des Menschen. Aufgrund der beobachteten Assoziationen zwischen der Konzentration von PFAS im Blut und den Blutfettwerten (besonders LDL-Cholesterin) wird vermutet, dass PFAS eine Rolle für das Risiko von Herz-Kreislauf- Erkrankungen spielen könnten. Auch der Zusammenhang mit dem Risiko von Typ 2 Diabetes wird diskutiert. Der Bekanntheitsgrad von PFAS in der Öffentlichkeit und ihre Untersuchung in wissenschaftlichen Studien hat erst in den letzten Jahren zugenommen. Aus diesem Grund gibt es bis heute nur sehr wenige Studien, die den Zusammenhang zwischen PFAS und der Inzidenz von Herz-Kreislauf-Erkrankungen und Typ 2 Diabetes untersucht haben. Daher hat diese Studie zum Ziel, die Zusammenhänge zwischen den Baseline-Konzentrationen von PFOS/PFOA und anderen perfluorierten Verbindungen im Blut und dem Risiko für Entstehung eines Herzinfarkts, Schlaganfalls und / oder einer Herzinsuffizienz und Typ 2 Diabetes während der Nachbeobachtung in einer Fall-Kohortenstudie der European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Studie zu untersuchen. Zudem sollen Assoziationen im Querschnitt zwischen Konzentrationen von PFOS, PFOA und anderen perfluorierten Verbindungen im Blut und Biomarkern des Lipidmetablismus (Gesamtcholesterin, LDL-Cholesterin, HDL-Cholesterin, Triglyceride), des Glucosemetabolismus (Glucose und HbA1c), des Leberstoffwechsels (GGT, GPT), der Harnsäure und des hsCRP in der repräsentativen Subkohorte untersucht werden. Zudem sollen auch die Zusammenhänge zwischen PFAS und bestimmten Lebensmitteln oder Lebensmittelgruppen (z.B. Fleisch, Fisch) zu untersuchen werden.
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.
Wirkung von Schwefeldioxid auf Nettophotosynthese und Atmung von Flechten; Kartierung von Flechten im Stadtgebiet; Schwefel-Analysen in Flechten.
Um die lückenhaft über große Flächen und oft unberechenbar verteilten Meeresressourcen zu nutzen, fliegen Albatrosse und Sturmvögel oft Hunderte von Kilometern pro Tag und füttern ihre Küken selten. In marinen Ökosystemen unter starkem anthropogenem Einfluss wird die Verfügbarkeit von Beute oft durch die Anwesenheit der Fischereifahrzeuge verändert, die große Mengen an Abfällen wie Innereien von verarbeitetem Fisch, Nichtzielarten und zu kleine Fische verwerfen. Dadurch erzeugen sie nicht nur eine vorhersehbare und reichliche Nahrungsquelle für Seevögel, sondern Fischerei-Abfälle erschließen Seevögeln auch den Zugriff auf demersale Organismen wie Bodenfische als neuartige Nahrungsquelle. In vielen fischreich genutzten Meeresgebieten stellen Abfälle daher einen großen Anteil der Nahrung von Seevögeln. Dies kann erhebliche Auswirkungen auf die Ernährungsökologie der Seevögel haben. Das Ziel der geplanten Studie ist es, unser Verständnis von Verhaltensanpassungen als Reaktion auf Änderungen in der Verfügbarkeit von Beute zu vertiefen. Wir schlagen dazu eine Fallstudie an Sturmtauchern Calonectris diomedea im Mittelmeer vor, einer Art, die sowohl natürliche Beute als auch Fischereiabfälle als Nahrung nutzt. Um das Ausmaß und die Auswirkungen der Nahrungsquellen zu bewerten, werden wir eine Kombination aus GPS-Tracking, Messungen der Stoffwechselrate mit 2 Methoden (Beschleunigungsdaten und Schwerwassermethode) und nicht-invasive genetische Nahrungsbestimmung verwenden. Wir werden untersuchen, ob die Nutzung der Fischereiabfälle durch die Sturmtaucher als Reaktion auf geringe Verfügbarkeit von ihrer natürlichen Beute auftritt oder ob diese Art sich an die neue Nahrungsquelle angepasst hat, und sie unabhängig von der Verfügbarkeit ihrer natürlichen Beute regelmäßig nutzt. Darüber hinaus werden wir erfahren und neue Brutpaare vergleichen, um zu bewerten, wie die Qualität von Alttieren dieses Verhalten beeinflusst, sowie die Energiebilanz der natürlichen Beute und von Fischereiabfällen vergleichen.
Ozeanerwärmung, -versauerung und die Umweltverschmutzung, nehmen zunehmend Einfluss auf die arktische und antarktische Umwelt. Antarktische, stenothermen Fische haben sich evolutionär an die dortigen stabilen Umweltbedingungen angepasst, welche z.B. genetische und funktionellen Veränderungen beinhalten. Diese könnten u.a. die Anpassungsmöglichkeiten antarktischer Fische gegenüber Umweltveränderungen beeinträchtigen. Vergleichsweise dazu leben arktische, gadoide Fische in einem Gebiet mir größeren Umweltschwankungen. In Anbetracht desen wird sich die Klimaveränderung wahrscheinlich unterschiedlich auf Arktische und Antarktische Fische auswirken.Das Herz-Kreislaufsystems stenothermer Fischarten ist prinzipiell nur geringfügig auf Umweltveränderungen zu reagieren. Hierbei stellt die Herzfunktion einen Schlüsselfaktor dar. Studien deuten des Weiteren auf negative und interagierende Einflüsse von Ozeanerwärmung- und versauerung auf Embryos und Larvalen polarer Fischarten hin. Die Exposition der Fische gegenüber mehreren, kombinierten Umweltstressoren kann zudem zu Verschiebungen im Energiehaushalt führen. Diese können eine verringerte Energieverfügbarkeit für andere, lebensnotwendige Funktionen zur Folge haben.Der Antrag befasst sich mit der Frage, wie sich die Umweltstressoren anthropogene Umweltverschmutzung, Klimaerwärmung und Ozeanversauerung auf den Energiestoffwechsel verschiedener Lebensstadien arktischer und antarktischer Fische auswirkt. Die Kernfragen lauten:Beeinträchtigt das Zusammenspiel multipler Stressoren den Schadstoffstoffwechsel polarer Fische? Verursachen multiple Stressoren eine Verschiebung im Energiehaushalt arktischer und antarktischer Fische? Wie beeinflussen Schadstoffe die aerobe und Herzfunktion der verschiedenen Entwicklungsstadien polarer Fische?Was für negative Folgen könnten aus ökologischer Sicht für arktische Gadoiden und antarktische Notothenioiden draus resultieren?Der Antrag soll ein grundsätzliches Verständnis für molekulare, mitochondriale, zellulare und Stoffwechselprozesse schaffen, welche der Anfälligkeit polarer Fische gegenüber Umweltstressoren zugrundeliegen. Als Maß für evolutionäre Anpassungsfähigkeit sollen die Akklimationskapazitäten der verschiedenen Lebensstadien polarer Fische untersucht werden.Für einen Breitengraden-Vergleich von Toleranzen gegenüber Umweltfaktoren konzentriert sich der Antrag auf ökologisch und biologisch vergleichbare stenotherme Arten. Somit wird eine Datengrundlage geschaffen, um die evolutionär verschiedenen aber gleichermaßen stenothermen arktische und antarktische Fische vergleichen zu können.Die in diesem Antrag eruierte physiologische Empflindlichkeit polarer Fische gegenüber Klimawandel sollen abschließend dazu dienen, die zukünftigen Risiken menschengemachter Umweltrisiken für diese Tiere abgeschätzen zu können. Schließlich wird das Projekt eine Grundlage für Management- und Schutzmaßnahmen polarer Ökosysteme gegenüber fortschreitendem globalen Wandel bilden.
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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