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.
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.
Es ist die Hypothese aufgestellt worden, dass neben nicht abgebauten Pflanzenresten die organische Substanz des Bodens grob aus zwei Kompartimenten besteht. Bestimmt durch den Ton- und Feinschluffanteil entwickelte sich ein inerter C-Pool während der Genese von Böden. Dieser an die mineralischen Feinanteile gebundene Kohlenstoff nimmt nur über einen langen Zeitraum am Kohlenstoffumsatz von Böden teil. In Abhängigkeit von der landwirtschaftlichen Praxis entwickelt sich während des durch die metabolische Aktivität von Bodentieren und Mikroorganismen verursachten Abbaus von Pflanzenresten und organischen Düngern ein zweiter, labiler C-Pool. Dieser ist im wesentlich verantwortlich für die Nährstoffflüsse in Böden. Das Ziel des geplanten Forschungsprojektes ist es, in Laborexperimenten die Verteilung von frisch zugeführten 14C aus markiertem Weizenstroh zwischen inertem und labilem C-Pool über den Zeitraum eines Jahres zu verfolgen. Zusätzlich wird die Mineralisierung des Pflanzenmaterials zu 14CO2, die Bildung wasserlöslicher 14C-Metabolite und die anabolische Verwertung des markierten Kohlenstoffs durch die mikrobielle Biomasse des Bodens verfolgt. Nach einer physikalischen Fraktionierung der mineralisch-organischen Bodensubstanz in einzelne Größenfraktionen soll deren Gehalt an 14C/12C organischer Substanz über die Zeit bestimmt werden. In einem Inkubationsexperiment werden die isolierten Größenfraktionen mit der autochthonen Bodenflora beimpft werden, und die dabei durch die Aktivität der Mikroorganismen freigesetzten 14CO2 Mengen sind ein Indikator für die Stabilität der organischen Substanz in den einzelnen Fraktionen. Für diese Untersuchungen werden Proben eines landwirtschaftlichen Bodens ausgesucht, der für viele Jahrzehnte verschiedener Düngungspraxis (null, mineralisch, organisch) unterlag. Durch dieses Forschungsprojekt werden Informationen über die kausalen Zusammenhänge von Bodenprozessen bei der Bildung und Speicherung der organischen Substanz im Boden erwartet.
Feststellen der Belastung von Lebensmitteln tierischen Ursprungs im Rahmen der Bakteriologischen Fleischbeschauung und an Tieren, speziell wildlebenden Tieren mit Pestiziden (Insektizide, Fungizide, Herbizide etc.). Aufgabe: Pruefen des Akkumulierens, des Recycling; Ausarbeiten von speziellen Untersuchungsmethoden, Erstellen von Untersuchungsplaenen, Untersuchung der Herkunft von Pestiziden, deren Uebergehen auf Lebensmittel und und Tiere und deren Metabolisierung.
In diesem Projekt soll das genomische Potenzial und wichtige Funktionen von Roseobacter- Populationen mittels kultivierungsunabhängigen metagenomischen und metatranskriptomischen Ansätzen analysiert werden. Um gen- und taxonspezifische Muster und metabole Schlüsselfunktionen dieser Gruppe zu identifizieren, werden Stoffwechsel- und funktionelle Profile von repräsentativen Proben aus der Nordsee, dem Südpolarmeer, von Biofilmen und Mesokosmen mittels modernster Pyrosequencing-Methodik untersucht. Außerdem werden Metagenombanken angelegt und hinsichtlich wichtiger Funktionen gesichtet, z.B. Genen mit Bedeutung bei Quorum Sensing, Energiestoffwechsel und der Sekundärstoffsynthese.
Das Forschungsvorhaben entstand aus der Forschungskooperation unserer beiden Arbeitsgruppen, die in den letzten Jahren auch durch die DFG im Rahmen von Arbeitsaufenthalten Dr. Ivanova s am HKI gefördert wurde. Das Ziel der Arbeiten ist die Untersuchung von Actinomyceten antarktischer Herkunft mit bekannter taxonomischer Zuordnung hinsichtlich ihres Bildungsvermögens für bekannte und neue Sekundärmetabolite unter Einsatz chromatografischer und instrumentalanalytischer Methoden (z.B. LC-MS, ESI-MS/CID-MS/MS). Folgende Ergebnisse werden erwartet:= Isolierung und Strukturaufklärung neuer bioaktiver Strukturen= Gewinnung von Aussagen über die genetischen Reserven antarktischer Actinomyceten bezüglich Bildung von Sekundärmetaboliten.= Gewinnung von Aussagen über die globale Verbreitung von Bildnern häufig vorkommender Sekundärmetabolite von Actinomyceten und Pilzen (z.B. Nactine, Polyether, Anthracycline und sesquiterpenoide Strukturen).
Warming and acidification of the oceans as a consequence of increasing CO2-concentrations occur at large scales. Numerous studies have shown the impact of single stressors on individual species. However, studies on the combined effect of multiple stressors on a multi-species assemblage, which is ecologically much more realistic and relevant, are still scarce. Therefore, we orthogonally crossed the two factors warming and acidification in mesocosm experiments and studied their single and combined impact on the brown alga Fucus vesiculosus associated with its natural community (epiphytes and mesograzers) in the Baltic Sea in all seasons (from April 2013 to April 2014). We superimposed our treatment factors onto the natural fluctuations of all environmental variables present in the Benthocosms in so-called delta-treatments. Thereby we compared the physiological responses of F. vesiculosus (growth and metabolites) to the single and combined effects of natural Kiel Fjord temperatures and pCO2 conditions with a 5 °C temperature increase and/or pCO2 increase treatment (1100 ppm in the headspace above the mesocosms). Responses were also related to the factor photoperiod which changes over the course of the year. Our results demonstrate complex seasonal pattern. Elevated pCO2 positively affected growth of F. vesiculosus alone and/or interactively with warming. The response direction (additive, synergistic or antagonistic), however, depended on season and daylength. The effects were most obvious when plants were actively growing during spring and early summer. Our study revealed for the first time that it is crucial to always consider the impact of variable environmental conditions throughout all seasons. In summary, our study indicates that in future F. vesiculosus will be more affected by detrimental summer heat-waves than by ocean acidification although the latter consequently enhances growth throughout the year. The mainly negative influence of rising temperatures on the physiology of this keystone macroalga may alter and/or hamper its ecological functions in the shallow coastal ecosystem of the Baltic Sea.
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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