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.
Untersuchung der Veraenderung der Mikroflora vor, waehrend und nach der Selbsterhitzung von Torf. Simulierung der Selbsterhitzung im Laborversuch unter kontrollierten Bedingungen. Untersuchung des Stoffwechsels von thermophilen Torforganismen, Isolierung und Charakterisierung von pflanzenwirksamen Stoffen, die bei der Selbsterhitzung von Torf entstehen.
The biotrophic fungus Ustilago maydis infects corn and induces the formation of tumors. In order for the fungus to proliferate in the infected tissue, U. maydis has to redirect the metabolism of the host to the site of infection. We wish to elucidate how this is accomplished. To this end we will perform transcript profiling during the time course of infection for both, the fungus and the maize plant. This will be complemented by metabolome analysis of different tissues during infection as well as by apoplastic fluid analysis. The goals will be to identify the carbon sources taken up by the fungus during biotrophic growth, to identify the transporters required for uptake, determine their specificity and elucidate how these carbon sources are provided by the plant. Fungal mutants affected in discrete stages of pathogenic development will be included in these studies. Likely candidate genes for carbon uptake/supply as well as for redirecting host metabolism will be functionally characterized by generating knockouts in the fungus and by isolating plants carrying mutations in respective genes or by generating transgenic plants expressing RNAi constructs.
Selbst in tiefen Sedimentschichten unter z.T. mehreren Kilometern mächtiger Sedimentbedeckung finden sich noch aktive Mikroorganismen. Mit zunehmender Tiefe steigt die Temperatur im Untergrund an und überschreitet irgendwann die Grenze bis zu welcher Leben möglich ist. Die bisher festgestellte Temperaturobergrenze von Leben auf der Erde wurden an Mikroorganismen von hydrothermalen Systemen, sogenannten Schwarzen Rauchern gemessen und liegt bei ca. 120 Grad C. In Sedimenten hingegen liegt die Grenze deutlich niedriger. Messdaten aus Ölfeldern deuten auf eine Grenze von ca. 80 Grad C hin. Diese Diskrepanz zwischen hydrothermalen und sedimentären Systemen wurde dadurch erklärt, dass die Mikroorganismen in Sedimenten nicht genügend Energie gewinnen können um die bei hohen Temperaturen verstärkt notwendigen Reparaturen ihrer Zellbestandteile wie DNA und Proteinen durchzuführen. Interessanterweise lässt sich metabolische Aktivität bei extrem hohen Temperaturen nur dann nachweisen, wenn die Experimente unter hohem Druck stattfinden. IODP Expedition 370 wurde spezifisch zur Klärung der Frage nach dem Temperaturlimit von Leben in sedimentären Systemen durchgeführt. Im Nankai Graben vor der Küste Japans herrscht ein recht hoher geothermischer Gradient von ca. 100 Grad C/km, d.h. das gesamte Temperaturspektrum in dem Leben möglich ist erstreckt sich über ein Tiefeninterval von etwas mehr als einem Kilometer. Durch modernste Bohr- und Labortechniken war es möglich, Proben von höchster Qualität zu gewinnen, welche garantiert frei von Kontamination sind. Die Expedition hat einen stark interdisziplinären Charakter, so dass eine Vielzahl von biologischen und chemischen Parameter gemessen wurde, welche eine detaillierte Charakterisierung des Sediments erlauben. Das beantragte Projekt ist ein wichtiger Teil der Expedition, da Sulfatreduktion der quantitativ wichtigste anaerobe Prozess für den Abbau von organischem Material im Meeresboden ist. Im Rahmen einer MSc Arbeit wurden bereits erste Messungen durchgeführt. Diese konnten zeigen das Sulfatreduktion über die gesamte Kernlänge messbar ist, wenn auch z.T. mit extrem geringen Raten. Im Rahmen des beantragten Projekts sollen weitere Messungen durchgeführt werden, unter anderem auch unter hohem Druck. Dazu soll ein Hochdruck Temperatur-Gradientenblock gebaut und betrieben werden. Neben Sedimenten von IODP Exp. 370 sollen weitere Experimente mit hydrothermal beeinflusstem Sediment aus dem Guaymas Becken durchgeführt werden. Ein Vergleich zwischen diesen beiden Sedimenten soll weitere Einblicke in einen der wichtigsten biologischen Prozesse im Meeresboden liefern und ein besseres Verständnis über die Grenzen von Leben im allgemeinen.
Besondere Anpassungen der Wuestenassel Hemilepistus reaumuri sind ihr Sozialverhalten im Familienverband und extreme Orientierungsleistungen, mit deren Hilfe sie ihre Hoehle wiederfindet. Beide Verhaltenskomplexe wurden und werden untersucht. Der Riedfrosch Hyperolius viridiflavus vermag in der Trockenzeit mehrere Monate in der Vegetationsschicht semiarider Gebiete ohne Nahrungsaufnahme zu ueberstehen. In diesem Zusammenhang werden die Art des Verdunstungsschutzes und besondere Anpassungen beim Atmungsstoffwechsel untersucht.
Die Symbiose der arbuskulären Mykorrhiza zwischen Landpflanzen und Glomeromyceten steigert die Aufnahme von Mineralien durch die Pflanze und hat daher ein großes Potential einen Beitrag zur nachhaltigen Landwirtschaft zu leisten. Grundlegend für diese Symbiose sind hochverzweigte pilzliche Strukturen, die Arbuskeln, welche Nährstoffe in die Wirtszelle übertragen. Wir haben den GRAS Transkriptionsfaktor REDUCED ARBUSCULAR MYCORRHIZA1 (RAM1) als einen Regulator der arbuskulären Entwicklung identifiziert und möchten nun die Regulation und Funktion dieses wichtigen Regulators aufklären.
Isolierung von Chlorphenol-abbauenden Mikroorganismen. Charakterisierung der Abbauleistung. Untersuchungen zum Abbauweg (insbesondere von 2,4,6-Trichlorphenol). Isolierung und Identifizierung von Metaboliten. Reinigung und Charakterisierung von Enzymen aus dem Abbauweg.
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