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.
Der Boden beeinflusst durch Wasserhaushalt, Temperaturverhältnisse, Bodenstruktur, Bodenleben, Lufthaushalt und Angebot an Nährstoffen den Charakter und die Qualität des Weines. Der Einfluss des Bodens auf die Weinqualität erfolgt über die Versorgung mit Nährstoffen. Ausreichend und regelmäßig gedüngte Böden erbringen gehaltvollere Weine. Der Nährstoff Kalium spielt in der Rebenernährung eine Schlüsselrolle. Kalium ist für die Wasseraufnahme und den Wasserhaushalt wesentlich, da es quellend wirkt und das Öffnen und Schließen der Spaltöffnungen regelt. Kalium ist als wichtiges Element für viele Enzymreaktionen am Eiweiß- und Kohlehydratstoffwechsel und damit an der Zucker- und Bukettbildung beteiligt. Außerdem fördert es die Trauben- und Holzreife sowie die Frosthärte. Kaliumreiche Weine sind gut gepuffert und dadurch wird die geschmackliche Wirkung der Säuren im Wein als weniger scharf und harmonisch empfunden. In den Richtlinien für die sachgerechte Düngung im Weinbau wird derzeit die jährliche Ausbringung folgender Mengen an Kalium (K) empfohlen: Gehaltsstufe A: 100 kg, Gehaltsstufe B: 83 kg, Gehaltsstufe C: 66 kg und Gehaltsstufe D: 33 kg. Aufgrund immer wieder auftretender Kaliummangelsymptome sowohl an Blättern als auch an Beeren und im Besonderen aufgrund des Auftretens von Traubenwelke soll diese Empfehlung evaluiert und adaptiert werden, um eine gute Versorgung der Reben zu gewährleisten.
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.
Chlorierte Ethene koennen durch anaerobe Bakterien vollstaendig nach Ethen dechloriert werden. Das Ziel des Forschungsvorhabens ist die Mikrobiologie, die die letzten zwei Dechlorierungsschritte von Dichlorethen zu Vinylchlorid und von Vinylchlorid zu Ethen katalysiert, zu identifizieren und genauer zu untersuchen. Anreicherungen zielen darauf ab, Bakterien zu aktivieren, die chlorierte Ethene als Elektronenakzeptor in einer anaeroben Atmung benutzen oder cometabolisch dechlorieren. Die Bakterienanreicherungen sollen mit molekular-oekologischen Methoden untersucht werden um Hinweise auf die Identitaet der dechlorierenden Bakterien zu kriegen. Reinkulturen werden auf ihre Physiologie, Biochemie, Genetik und Oekologie hin untersucht.
Mit 57 Co-Autoren werden vorkommen, Eigenschaften, Analytik, Gewinnung, Verwendung, Entsorgung, Verteilung, Resorption, Stoffwechsel und Wirkungen auf Pflanzen, Tiere und Menschen von Metallverbindungen erfasst und allgemeine Zusammenhaenge zur Risikoanalyse und zum aufstellen von Grenzwerten erarbeitet, insbesondere wurden Umweltchemie und globale Kreislaeufe von Chrom-, Nickel-, Cobalt-, Beryllium-, Arsen-, Cadmium und Selenverbindungen studiert. Uebersichtsberichte ueber Metalltagungen siehe z.B. chemische Rundschau 35, Nr. 16, 9-13 (15. April 1982), Chemosphere 12 (4/5) N 28 - N 36 (1983), 12 (7/8), N 20 - N 27 (1983), 13 (3), N 4 - N 17 (1984) und 13 (7), N 5 - N 30 (1984). Weitere Berichte im Druck.
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.
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