Flussauen mit ihrem typischen Muster von unterschiedlichen Lebensräumen sind in Mitteleuropa stark gefährdet. Die Bundesanstalt für Gewässerkunde (BfG) hat daher das Integrierte Flussauenmodell INFORM (Integrated Floodplain Response Model) für den Einsatz an Bundeswasserstraßen erstellt, mit dem ökologische Modellierungen durchgeführt werden können. Das modular aufgebaute Modellsystem INFORM verknüpft hydrologische, hydraulische, morphologische und bodenkundliche Modelltechniken mit ökologischen Modellen. Dabei werden vor allem sogenannte Lebensraumeignungsmodelle (Habitatmodelle) angewendet. Solche Modelle beschreiben die potentielle Eignung eines Standortes als Lebensraum für bestimmte Arten und / oder Artengruppen anhand der Ausprägung mehrerer abiotischer Standortparameter. Die Bearbeitung folgt prinzipiell dem Wirkungspfad Abfluss - Flusswasser - Grundwasser - Boden - Biotik unter Berücksichtigung morphologischer Einflüsse, des direkten Einflusses der Überflutung und der (landwirtschaftlichen) Nutzung. Die Bewertung der prognostizierten Veränderungen erfolgt an Hand von Gesetzen und Verordnungen und beinhaltet auch die Auswirkungen des menschlichen Handelns.
Die Begrenzung der Länge von Nahrungsketten ist eine der klassischen, aber immer noch unbeantworteten Fragen der Ökologie von Lebensgemeinschaften. In diesem Projekt und in einem parallelen Projekt limnologischer Ausrichtung (Antragsteller: Dr. H. Stibor, LMU München) soll versucht werden, zwei zentrale Hypothesen zu überprüfen: Die Omnivorie-Hypothese und die Hypothese der energetischen Begrenzung. Omnivore sind Organismen, die ihre Nahrung mindestens zwei tropischen Ebenen entnehmen. Dadurch werden sie gleichzeitig zu Nahrungskonkurrenten ihrer Beuteorganismen auf der unmittelbar unter den Omnivoren angesiedelten tropischen Ebene. Diese können dem doppelten Druck (Konkurrenz, Fraß) nicht widerstehen und werden aus dem System verdrängt, wodurch es zu einer Verkürzung der Nahrungskette kommt. Hypothese der energetischen Begrenzung. Wegen der Energieverluste, die bei jedem Transferschritt in der Nahrungskette auftreten, begrenzt die Höhe der Primärproduktion die Länge von Nahrungsketten, da bei zu langen Ketten die Energiezufuhr zu niedrig wäre, um die tropische Ebene der terminalen Räuber zu unterhalten. Überprüfung der Omnivorie-Hypothese. In künstlich zusammengestellte Modell-Nahrungsnetze im Labor (Mikrokosmen) werden an der Basis (mixotrophe Algen) und in der Mitte (omnivore Zooplankter) Omnivore eingefügt und die Struktur der Nahrungsnetze mit Kontroll-Nahrungsnetzen ohne Omnivore verglichen. Überprüfung der Hypothese der energetischen Begrenzung. Die Entwicklung der omnivorenhaltigen und omnivorenfreien Modell-Nahrungsnetze wird bei unterschiedlicher Trophie und damit Primärproduktion verfolgt.
An der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft (WSL) in Birmensdorf, Schweiz, wird von 2000 bis 2004 ein multidisziplinäres Modell Ökosystem-Experiment durchgeführt. Ziel dieses Großprojekts ist es, Stoffflüsse zu untersuchen und die Reaktionen von Pflanzen- und Organismengemeinschaften auf chronische Belastungen (Cd, Cu, Zn, sauerer Regen) zu verfolgen bzw. zu verstehen. Mit den Resultaten sollen die Risiken von Begrünung, Aufforstung und Bodenstabilisierung mit Pflanzen abgeschätzt werden können. Der verwendete landwirtschaftliche Oberboden wurde mit Filterstaub aus der metallverarbeitenden Industrie vermischt. Die daraus resultierenden Schwermetallbelastungen des Versuchsbodens betragen für Cd 10 ppm, für Cu 400 ppm und für Zn 2700 ppm. Das Projekt 'Einfluss des Abbaus von Laubstreu auf den Transport von Schwermetallen im Boden' untersucht die Änderung der Metall-Komplexbildung für Kupfer und Zink während des Streuabbaus. In wässrigen Streuextrakten wird das Ausmaß der Komplexierung mit Hilfe der Gleichgewichts-Ionenaustausch-Methoden bestimmt. Die Charakterisierung der Bindung erfolgt mit Infrarotspektroskopie (FTIR), die organischen Säuren im Extrakt werden mit der Gaschromatographie bestimmt. Untersucht werden Proben aus Freilandversuchen.
Während die Auswirkungen von Klimawandel auf physiologische und ökologische Prozesse das Thema zahlreicher Untersuchungen waren, sind evolutionäre Prozesse im Zusammenhang mit Klimawandel weit weniger gut untersucht. Insbesondere mangelt es an Studien zu möglichen komplexen Wechselwirkungen zwischen ökologischen und evolutionären Prozessen in einer sich ändernden Umwelt. Artspezifische Unterschiede in Anpassungsraten könnten die Dynamik der gesamten Art-Gemeinschaft beeinflussen, umgekehrt könnten sich ökologische Prozesse wie Interaktionen zwischen Arten, Immigration und Emigration auf das Anpassungspotential von Arten auswirken. Die Tatsache, dass Klimawandel zu Veränderungen in mehreren Umweltfaktoren führt, macht Vorhersagen über mögliche Auswirkungen noch schwieriger, da sich Veränderungen in mehreren Stressoren interaktiv auf ökologische und evolutionäre Prozesse auswirken könnten. Die Ziele des vorgeschlagenen Projektes sind die Analyse von ökologischen und evolutionären Prozessen und deren Wechselwirkung (1) bei Veränderung von mehreren Stressoren, (2) bei Umweltveränderung in trophisch einfachen versus trophisch komplexen Gemeinschaften, und (3) bei Umweltveränderung in isolierten versus verbundenen Habitaten. Diese Fragestellungen sollen mit einer Kombination aus Modellierung, Mikrokosmen- und Mesokosmen-Experimenten untersucht werden. In einem Selektionsexperiment über hunderte von Generationen werden mehrere Algenarten bei konstanten bzw. steigenden CO2- und/oder Temperatur-Werten exponiert. Ebenso werden mehrere Ciliatenarten bei konstanter bzw. steigender Temperatur gehalten. Reziproke Transplantationsexperimente testen, ob eine mögliche Anpassung von Algen an steigende CO2-Werte durch gleichzeitige Erhöhung der Temperatur beeinflusst wird. Weiters wird getestet, ob sich Arten von verschiedenen trophischen Ebenen (Algen versus Ciliaten) in ihrer Anpassungsfähigkeit unterscheiden. Reziproke Transplantationsexperimente der gesamten Gemeinschaft werden testen, ob evolutionäre Prozesse die Dynamik der Gemeinschaft beeinflussen. Interaktive Effekte von Umweltveränderung und Habitatkonnektivität auf ökologische und evolutionäre Prozesse werden sowohl in einem Mikrokosmenexperiment als auch in einem Mesokosmenexperiment untersucht. Der Effekt von steigender Temperatur (Mikrokosmenexperiment) bzw. abnehmendem pH-Wert (Mesokosmenexperiment) wird in isolierten bzw. verbundenen Habitaten verglichen. In einem theoretischen Ansatz werden die drei Fragestellungen in einem Modell verknüpft. Zunächst werden Evolution und Umweltveränderung in ein Metagemeinschaftsmodell integriert. Entlang eines Konnektivitäts-Gradienten wird die relative Bedeutung von lokaler Anpassung im Vergleich zu Wanderungsprozessen untersucht. usw.
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.
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.
| Organisation | Count |
|---|---|
| Bund | 850 |
| Europa | 104 |
| Kommune | 4 |
| Land | 32 |
| Wirtschaft | 3 |
| Wissenschaft | 668 |
| Zivilgesellschaft | 13 |
| Type | Count |
|---|---|
| Daten und Messstellen | 216 |
| Förderprogramm | 836 |
| Text | 8 |
| unbekannt | 21 |
| License | Count |
|---|---|
| Geschlossen | 15 |
| Offen | 1057 |
| Unbekannt | 9 |
| Language | Count |
|---|---|
| Deutsch | 705 |
| Englisch | 465 |
| Resource type | Count |
|---|---|
| Archiv | 2 |
| Bild | 2 |
| Datei | 214 |
| Dokument | 7 |
| Keine | 645 |
| Webdienst | 4 |
| Webseite | 220 |
| Topic | Count |
|---|---|
| Boden | 660 |
| Lebewesen und Lebensräume | 1058 |
| Luft | 806 |
| Mensch und Umwelt | 1081 |
| Wasser | 644 |
| Weitere | 1071 |