Im Rahmen des SFB 275 (Universität Tübingen) wurden innerhalb der ersten Antragsphase (1995-1997) die Schwarzschiefer des Unteren Toarciums SW-Deutschlands bearbeitet. In der zweiten Antragsphase (1998-2000) wurde/wird die Schwarzschiefer-Genese in verschiedenen Ablagerungsmilieus unterschiedlicher Zeitscheiben untersucht. Eine Förderung von weiteren zwei Jahren wird beantragt, um die Datenbasis stratigraphisch zu erweitern und ein Synthese-Modell für die Schwarzschieferbildung in Trias und Jura zu erarbeiten.
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.
This pre-study pilot project will be carried out in Kenya and Tanzania and is part of a more extensive remote sensing project (initiated by the European Space Agency, ESA) aiming to develop a monitoring system for the assessment of land cover change of farmlands, rangelands and forest standings (logging, fires, uncontrolled deforestation, new settlements, etc.) at a national regional level. An integrated approach of remote sensing techniques (both through the use of satellite and ground data), physical vegetation models and ground measurements will be adopted. Operatively, the execution will consist of a 6-month period (pre-study) consisting in a ground campaign along a north-south transect, which is almost unknown to the current vegetation cartography. Based on the field results of the pre-study and within an on-going 30 month period (extended study, see Annexed 3), new classification methods and algorithms will be developed for assessment of land use and cover change using ENVISAT-data. An outcoming of this research will be a system capable to monitor and plan the available agricultural food resources for those developing regions.
Das Projekt hat zum Ziel, den Einfluss der Aktivität von Bodentieren auf Umsetzungsprozesse in urbanen Böden zu untersuchen. Neben der Quantifizierung des Beitrages, den die Bodentiere bei der Dekomposition von organischem Material und der Verlagerung von Nähr- und Fremdstoffen leisten, soll insbesondere auf die Wechselwirkungen mit der mikrobiellen Flora eingegangen werden. Da anthropogen geprägte Böden eine in ihrer Vielfalt - gegenüber natürlichen Systemen - reduzierte Bodentiergemeinschaft aufweisen, möchte das Projekt zugleich einen Beitrag zu der Frage leisten, welchen Einfluss jeweils funktionelle Zusammensetzung und Artendiversität der Biozönose auf die bodenbiologisch gesteuerten Prozesse diese Standorte ausüben. Ein weiteres Ziel des Projektes ist die Charakterisierung von Veränderungen in den strukturellen Eigenschaften der untersuchten Böden, die auf Ausscheidungen und auf die Vermengungs- und Grabaktivität der Bodentiere zurückzuführen sind. In der ersten Projektphase wird die Steuerungsfunktion der Bodentiere bei Umsetzungsprozessen, die maßgeblich durch die Aktivität von Mikroorganismen getragen werden, in Mikrokosmen unterschiedlicher Komplexität untersucht. Diese sollen mit standorttypischen Tierarten und Substraten bestückt werden und dynamische, bodenbiologische Prozesse modellhaft beschreiben. Die Übertragung der im Labor gewonnenen Erkenntnisse auf das Freiland erfolgt in einer späteren Projektphase. Zusammenhänge zwischen Besatz von speziellen Tierarten und ... (Text gekürzt)
Dieses Projekt erforscht die Bedeutung von Chemolithoautotrophie und Oberflächeneintrag als Quellen von reduziertem Kohlenstoff für die mikrobielle Gemeinschaft in den Hainich-Aquiferen mittels Mikrokosmen-Experimenten. Basierend auf Raman-Mikrospektroskopie in Kombination mit Isotopenmarkierungs-Experimenten wird eine neue Methode zur Hochdurchsatzsortierung von Zellen etabliert um metabolisch aktive mikrobielle Subpopulationen zu isolieren. Mittels Metagenomanalyse kann dann gezielt deren Rolle in den biogeochemischen Kreisläufen im Grundwasser untersucht 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.
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