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.
Das Wissen über die Menge, Zusammensetzung und Umsetzung der organischen Substanz in Böden der gemäßigten Breiten beschränkt sich bis auf wenige Ausnahmen auf die Oberböden (A-Horizonte und Auflagen). Hier finden sich die höchsten Konzentrationen der organischen Substanz. Jüngere Inventurarbeiten haben nun gezeigt, dass auch im Unterboden (B- und Cv-Horizonte) beträchtliche Mengen an organischer Substanz, allerdings in niedrigen Konzentrationen vorliegen. Ziel des geplanten Vorhabens ist es, (1) die Menge der organischen Substanz im Unterboden zu erfassen, (2) ihre Zusammensetzung und Herkunft zu bestimmen und (3) ihre Umsetzbarkeit zu erfassen. Daraus sollen Rückschlüsse auf die Stabilisierungsmechanismen der organischen Substanz im Unterboden gezogen werden. Nach einer Inventur der Bodenprofile an den SPP-Standorten (C-Gehalte, 14C-Alter) erfolgt die Erfassung der Zusammensetzung der organischen Substanz mittels Festkörper-13C-NMR-Spektroskopie. Die Zusammensetzung der Lipid-, Polysaccharid- und Ligninfraktion soll Hinweise auf die Herkunft der stabilisierten organischen Substanz differenziert nach oberirdischen, unterirdischen Pflanzenrückständen und mikrobiellen Resten geben. Abbauversuche unter kontrollierten Bedingungen im Labor und die Erfassung des 14C-Alters des freigesetzten CO2 sollen Aufschluß über die Umsetzbarkeit des 'jungen' und 'alten' C im Unterboden geben. Dabei werden jeweils die Profile über die gesamte Entwicklungstiefe betrachtet, um die Unterbodenhorizonte in Bezug zu den Oberböden und zu den Ergebnissen anderer AG im SPP zu setzen. Darauf aufbauend können dann in den nächsten Phasen des SPP die Eigenschaften der organischen Substanz im Unterboden und die Regulation der C-Umsetzungen im Unterboden untersucht werden.
Genetisch programmierbare Engineered Living Materials/ELM haben mit dem Aufkommen der Ära der Synthetischen Biologie an Dynamik gewonnen. Endosporen von Bacillus subtilis bergen großes Potenzial für ELM, da sie eine zweite Schicht zur Implementierung von Funktionalitäten darstellen, die genetisch programmiert werden können. Unter Nährstoffmangel bildet B. subtilis hochresistente Sporen, die die DNA durch den Zellwandkortex und drei Proteinschichten schützen. Durch die translationale Fusion eines Gens von Interesse mit einem Gen, das ein geeignetes Ankerprotein kodiert, können die Zielproteine immobilisiert werden. Die resultierenden SporoBeads bleiben lange Zeit stabil, können aber nach der Induktion innerhalb von Stunden keimen. Im Rahmen dieses SPP werden wir das SporoBead-Konzept mit dem Bioprinting kombinieren, um funktionalisierte Endosporen in Materalien zu integrieren, die induzierende Faktoren für adaptive Funktionen enthalten. Wir werden Protokolle sowie Polymer- und Kompositmaterialien entwickeln, die eine Kontrolle sowohl der Sporenbildung als auch der Keimung ermöglichen. Anschließend kombinieren wir die auf der Sporenoberfläche präsentierte Aktivität mit einer zweiten, die nach der Keimung exprimiert wird, und erzeugen so zwei Funktionen, die geschaltet werden können. Verschiedene Bioprinting-Technologien (Extrusion, Kern-Mantel und Drop-on-Demand) werden es uns ermöglichen, diese Sporen in mehreren Anordnungen zu positionieren. Beide Partner werden ihre Technologien weiterentwickeln, um eine vielseitige SporoPrinting-Plattform sowie drei verschiedene Demonstratoren zu etablieren: (i) Als Machbarkeitsstudie soll ein ELM dienen, das die Fluoreszenz von Grün auf Rot umschaltet. Es basiert auf einem doppelt fluoreszierend markierten Stamm, der einen Fluorophor auf der Sporenoberfläche trägt und einen zweiten in vegetativen Zellen produziert. Dadurch wird die optische Beobachtung des Übergangs von der Spore zur vegetativen Zelle ermöglicht. Es werden Bioinks geeigneter Zusammensetzung sowie verschiedene Bioprinting-Strategien basierend auf Materialdesign und -morphologie erforscht, um den Übergang zwischen den verschiedenen Entwicklungsstadien zu steuern. Als nächstes werden wir (ii) die SporoPrinting-Plattform durch die Einführung der Streptomyceten erweitern, um Protokolle zu entwickeln, die die Kultivierung zweier verschiedener Gruppen von Organismen in einer genau definierten räumlich-zeitlichen Anordnung ermöglichen. Wir werden einen auf Antibiotika reagierenden Biosensoraufbau entwickeln, der es ermöglicht, potenzielle Streptomycetenproduzenten auf bestimmte Arten von Antibiotika zu untersuchen, basierend auf der spezifischen Induktion von B. subtilis-Biosensoren. Abschließend entwickeln wir (iii) ein ELM mit einem programmierbaren Dunkel-Hell-Muster, das auf einer auf SporoBeads-präsentierten Melanin-produzierenden in Kombination mit einer -entfärbenden Aktivität basiert, die von vegetativen Zellen exprimiert und abgesondert wird.
Organismen im Plankton bilden komplexe Gemeinschaften die substanziell zur globalen Primärproduktion beitragen und die Grundlage des marinen Nahrungsnetzes bilden. Dieses Projekt adressiert die Rolle von Sekundärmetaboliten in der Organisation von komplexen Plankton Gemeinschaften. Wir untersuchen den Einfluss des Bakteriums Kordia algicida das Mikroalgen lysieren kann auf das Plankton Microbiom. Die Regulation der Interaktion und die kaskadierenden Effekte auf die Lebensgemeinschaften im Meer werden in Labor- und Felduntersuchungen adressiert.
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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