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.
Das Projekt HUMUS hat zum Ziel, die Wasserbindung der organischen Bodensubstanz urbaner Böden zu charakterisieren. Im Zentrum stehen Geleigenschaften und der Nachweis eines Glasüberganges in der organischen Bodensubstanz. Die meisten Untersuchungen erfolgen mit Hilfe der Differential Scanning Kalorimetrie (DSC). Sie werden durch dielektrische Messungen und 1H-NMR-Relaxation (TP GEO) sowie kinetische Untersuchungen zur DOC-Freisetzung und Quellung ergänzt. Die Feldexperimente und Mikrokosmen der Forschergruppe dienen zur Verknüpfung der Wasserbindung der organischen Bodensubstanz mit Faktoren des Wasserhaushaltes (TP BODEN), Mikroorganismen und ihren Biofilmen (TP MIKRO), der Bodenmesofauna (TP FAUNA) sowie unterschiedlichen Elektrolytbedingungen. In der zweiten Projektphase werden Auswirkungen der urban beeinflußten Humuseigenschaften auf die kleinräumige Variabilität und auf den Wasser- und Stofftransport der urbanen Standorte untersucht werden.
Die Nahrungsstrategien vieler Bodentiere, insbesondere der Mikrofauna, sind nur unzureichend bekannt. Meist erfolgt eine generelle Einteilung nach vorwiegend morphologischen Kriterien. Nahrungsnetzanalysen und Modelle verlangen jedoch nach einer genauen trophischen Klassifizierung. Im geplanten Forschungsprojekt werden zwei biochemische Methoden eingesetzt: Phospholipidfettsäuren als Biomarker für den Transfer von Kohlenstoff in der Nahrungskette und das 15N/14N-Isotopenverhältnis als Indikator für die trophische Stellung der Bodenfauna. Vergleichende Analysen sollen Aufschluss geben über Herkunft und trophische Anreicherung von Stickstoff und Fettsäuren, sowie physiologische Aspekte (z.B. Biosynthese) untersuchen. In Mikrokosmen werden hierzu Nahrungsketten simuliert (Mikroflora (Bakterien, Pilze), Sekundärzersetzer (Nematoden), Räuber (Milben, Collembolen). Daneben erfolgen Manipulationen des physiologischen Stoffwechselzustandes (Proteingleichgewicht, Nahrungsmangel) und Untersuchungen an Freilandfauna. Die zugrunde liegenden biochemischen und physiologischen Mechanismen der trophischen Anreicherung von 15N in einer Nahrungskette wurden bisher nur in wenigen kontrollierten Laborstudien untersucht. Zudem sind Fettsäuremuster von Bodentieren kaum bekannt. Letztere zur Klassifizierung von Nahrungsstrategien zu nutzen, bietet einen neuen und viel versprechenden Ansatz zur Analyse von Bodennahrungsnetzen.
Struktur- und Funktionsbeschreibung des Modell-Oekosystems Breitenbach bei Schlitz/Hessen. Ausgangsinformationen: physikochemische Rahmenbedingungen, Fauna im einzelnen bekannt. Jetzige Aktivitaeten: 1) Praezise Messung physikochemischer Parameter, teils durch staendige automatische Registrierung, Erfassung diurnaler und saisonaler Variationen. 2) Analyse der Primaerproduzenten: Erfassung der Algenflora: Bestandsaufnahme, Standortansprueche, Produktionsleistung dominanter Vertreter (z.Zt. nicht besetzt). Allochthone Nahrungsbasis der Biozoenose: Partikulaeres organisches Material in fliessender Welle und Sediment im Jahresgang. 4) Gehalt an geloesten organischem C, als Nahrungsbasis der Bakterienflora; dessen Festlegung in Bakterienbiomasse. 5) Bakterienzahlen und Aktivitaeten in freier Welle und Sedimenten. 6) Emergenz der Wasserinsekten. 7) Zusammenhang zwischen Emergenz und benthischer Produktion. 8) Laengsverteilung der Zoenose und jaehrliche Unterschiede im Emergenzerfolg der Wasserinsekten in Abhaengigkeit von abiotischen und biotischen Faktoren (Nahrungsversorgung, Konkurrenz). 9) Interaktionen zwischen Aufwuchs und Weidegaengern. 10) Experimentelle Untersuchungen zur Autooekologie dominanter Taxa, vor allem der Wasserinsekten und Amphipoda. 1-9 als Bausteine fuer eine synoekologische Gesamtbetrachtung des Systems.
Hydrologische Fließwege bilden die kritische Verbindung zwischen der Quelle der P Mobilisierung und des P Exports zu den Flüssen. Die Prozesse der P Mobilisierung auf der Standortskale ist vergleichsweise gut verstanden, jedoch ist die Kenntnis des P Transportes in Hängen und Einzugsgebieten durch die Komplexität der Transport-Skalen und Fließprozesse begrenzt. In Hängen können große P Flüsse zum dynamischen P Export beitragen, da P oft in schnellen Fließwegen transportiert wird, insbesondere in bewaldeten Systemen wo präferentielle Fließwege häufig auftreten. Ein adäquates Prozesswissen der Hanghydrologischen Dynamik ist daher wichtig um die P Transport Dynamik zu beurteilen und vorherzusagen. Jedoch wurden bisher solche Studien fast ausschließlich in Einzugsgebieten mit landwirtschaftlicher Nutzung durchgeführt. In dieser experimentellen und modellierungs-basierten Studie über hanghydrologische Prozesse und Phosphortransport werden wir die Auswirkungen der Abflussprozesse auf den P-Transport in bewaldeten Hängen entlang des grundlegenden Hypothesen des SPP untersuchen. Wir werden die Auswirkungen unterschiedlicher Fließwege und Verweilzeiten auf den P Transport und den damit verbundenen hydrologischen Bedingungen untersuchen. Die Hypothese wird getestet, dass die P-Signaturen im Abfluss im Zusammenhang stehen mit den bodenökologischen P-Gradienten und dass die P-Signaturen durch die Verweilzeiten des Wassers im Hang bestimmt werden, die insbesondere durch präferentielle Fließwege bei Niederschlagsereignissen dominiert werden. Diese Hypothesen werden an den vier SPP Standorte im Gebirge mit einem innovativen, kontinuierliche Monitoring-System für unterirdische Hangabflüsse und P-Transport bei hoher zeitlicher Auflösung untersucht. Event-basierte und kontinuierliche Probenahmen für die verschiedenen P Spezies, stabile Wasserisotope und andere geogene Tracer in Niederschlag, Abfluss und Grundwasser werden es uns ermöglichen, Verweilzeiten von Wasser mit den P Flüsse und P Transportprozessen zu verknüpften. Schließlich werden wir ein prozessorientierten hydrologischen Hang-Modell weiterentwickeln um die verschiedenen Fließ-und Transportwege zu simulieren, um die Dynamik von Abfluss und P Transport zwischen der Hang- und Einzugsgebietsskala zu verknüpfen. Die Modellierung wird sich darauf fokussieren die Altersverteilung von Wasser und die bevorzugte Fließwege die durch 'hot spots' bei der Infiltration und P Mobilisierung entstehen in bewaldeten Hängen adäquat darzustellen.
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.
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