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.
Es werden u.a. wissenschaftliche Workshops durchgefuehrt: 26./27. November 1981: Expoquimia Barcelona 19./20. November 1984: Expoquimia Barcelona 20./21. Maerz 1986: EPF-L, Lausanne in Zukunft werden auch biochemische und Metabolismus-Studien einbezogen.
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.
Die streu- und humusfressende Makrofauna spielt eine wichtige Rolle beim Abbau und bei der Stabilisierung organischer Substanz im Boden. Anhand ausgewählter Modellorganismen (Käfer- und Dipterenlarven) soll untersucht werden, welche Rolle den besonderen physikochemischen Verhältnissen in den Intestinaltrakten dieser Tiere, insbesondere den extrem alkalischen Darmabschnitten, sowie der ausgeprägten Darmmikrobiota bei den Stabilisierungsprozessen zukommt. Mit chromatographischen und spektro-skopischen Methoden sollen die chemische und mikrobielle Transformation der organischen Substanz und der mikrobiellen Biomasse des Bodens verfolgt werden. Weitere Schwerpunkte liegen bei der Rolle von Humin-stoffen als Mediatoren der mikrobiellen Reduktion von Eisenverbindungen sowie beim Beitrag der mikrobiellen Produk-tion des Darms zur refraktären organischen Substanz in den Ausscheidungen der Tiere. Die Untersuchungen beinhalten den Einsatz von Mikrosensoren, die Mikroinjektion von Radiotracern und Fütterungsexperimente mit Huminstoff-Modellverbindungen.
Messung von Umwandlungsgeschwindigkeit, Verteilung in Organen und Geweben von ausgewaehlten Umweltchemikalien in Labortieren sowie Insekten und Mikroorganismen; Strukturausklaerung der Umwandlungsprodukte; Gesamtbilanzmessung bis zu den Endprodukten des Abbaus, um die Abbaukapazitaet dieser Organismen bzw. die Belastung der Umwelt durch Umwandlungsprodukte zu erkennen.
Im Zuge der Sanierung von Braunkohlepyrolysealtlasten stellen Pflanzenkläranlagen mit Iris eine aussichtsreiche Biotechnologie dar, Belastungen mit phenolischen Komponenten zu entsorgen. Die hier durchgeführten Arbeiten untersuchen den Abbau, die Translokation von Phenol in Iris und beschreiben stoffwechselphysiologische Reaktionen bei Phenolzugabe.
Development of insecticide resistance in insect pest species is one of the main threats of agriculture nowadays. The cotton bollworm, Helicoverpa armigera, is the noctuid species possessing by far the most reported cases of insecticide resistance worldwide, correlated with one of the widest geographical distributions of any agricultural pest species. This turns H. armigera into an adequate model to study resistance mechanisms in detail. The main mechanisms underlying insecticide resistance are target side insensitivity and metabolism, mainly due to carboxylesterases and cytochrome P450 monooxygenases. Just recently, the resistance mechanism of an Australian H. armigera strain toward the pyrethroid fenvalerate was ascribed to a single P450, CYP337B3. CYP337B3 is a naturally-occurring chimera between CYP337B2 and CYP337B1 evolved by an unequal crossing-over event. This enzyme had acquired new and exclusive substrate specificities resulting in the detoxification of fenvalerate. This is the first known case of recombination as an additional genetic mechanism, besides over-expression and point mutation, leading to insecticide resistance. Therefore, CYP337B1, CYP337B2, and CYP337B3 are ideal candidates for studying structure-function relationships in P450s. The project aims to characterize amino acids that are crucial for the activity of CYP337B3 toward detoxification of fenvalerate. Additionally, cross-resistance conferred by CYP337B3 enables the determination of common structural moieties of pyrethroids favoring detoxification by CYP337B3 and those leading to resistance breaking. Pyrethroids with identified resistance breaking moieties could be used to control even pyrethroid-resistant populations of H. armigera. Another advantage of this system is the conferment of insecticide resistance by CYP337B3 that is not restricted to Australia but seems to be a more common mechanism as recently revealed by the finding of the chimeric P450 in a cypermethrin-resistant Pakistani strain. To shed light on the contribution of CYP337B3 to pyrethroid resistance of H. armigera and even closely related species worldwide, field populations from different countries will be screened by PCR for the presence of CYP337B3 and its parental genes. If applicable, the allele frequency of CYP337B3 will be determined being a convenient method to conclude the resistance level of the tested populations. Finally, the project will result in advising farmers on the control of populations of H. armigera and related species possessing CYP337B3. This will even become more important due to the climate change allowing H. armigera to spread northward including central Europe, where H. armigera is not yet able to survive wintertime.
Der größte Teil unserer Landschaften entwässert direkt in Gewässer erster und zweiter Ordnung. Im Mittelpunkt des Projekts stehen Untersuchungen zum Stoffrückhalt und zu Transformationsprozessen in der Übergangszone zwischen Grundwasser und den Gewässern niederer Ordnung. Dazu wird ein räumlich und zeitlich hoch auflösendes Monitoring von Wasser- und Stoffflüssen mit innovativer online-Sondentechnik, komponenten- und enantiomerspezifischer Isotopenanalytik und molekularbiologischen Werkzeugen kombiniert. Ergänzt werden die Felduntersuchungen durch prozessbasierte reaktive Transportmodelle. Durch diese Kombination modernster Methoden soll ein umfassendes Verständnis der räumlich-zeitlichen Muster von Reaktivität und Umsätzen in Abhängigkeit von Landnutzung und hydraulischen Randbedingungen erreicht werden.
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