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Satellite Color Images, Vegetation Indices, and Metabolism Indices from Meiningen, Germany from 1985 – 2023

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.

WMS Nachtaufnahme Hamburg

Dieser WebMapService (WMS) zeigt eine Nachtaufnahme von Hamburg. Zur genaueren Beschreibung der Daten und Datenverantwortung nutzen Sie bitte den Verweis zur Datensatzbeschreibung.

World Settlement Footprint (WSF) 3D - Building Volume - Global, 90m

The World Settlement Footprint (WSF) 3D provides detailed quantification of the average height, total volume, total area and the fraction of buildings at 90 m resolution at a global scale. It is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery in combination with digital elevation data and radar imagery collected by the TanDEM-X mission. The framework includes three basic workflows: i) the estimation of the mean building height based on an analysis of height differences along potential building edges, ii) the determination of building fraction and total building area within each 90 m cell, and iii) the combination of the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. In addition, global height information on skyscrapers and high-rise buildings provided by the Emporis database is integrated into the processing framework, to improve the WSF 3D Building Height and subsequently the Building Volume Layer. A comprehensive validation campaign has been performed to assess the accuracy of the dataset quantitatively by using VHR 3D building models from 19 globally distributed regions (~86,000 km2) as reference data. The WSF 3D standard layers are provided in the format of Lempel-Ziv-Welch (LZW)-compressed GeoTiff files, with each file - or image tile - covering an area of 1 x 1 ° geographical lat/lon at a geometric resolution of 2.8 arcsec (~ 90 m at the equator). Following the system established by the TDX-DEM mission, the latitude resolution is decreased in multiple steps when moving towards the poles to compensate for the reduced circumference of the Earth.

Sentinel-2 Sachsen-Anhalt

Bei dem Datensatz handelt es sich um Fernerkundungsdaten aus dem Copernicus-Programm der Europäischen Kommission und der Europäischen Weltraumorganisation, die für das Gebiet von Sachsen-Anhalt aufbereitet wurden. Die Sentinel-2 Satelliten des Copernicus-Programm liefern multispektrale Aufnahmen im Wellenlängenbereich des sichtbaren Licht (VIS) und nahen Infrarotbereich (NIR) aus denen nahezu wolkenfreie Mosaikbilder erstellt werden. Diese Daten finden insbesondere in der Forst-, Wasser-, und Agrarwirtschaft Anwendung um z.B. zeitliche Veränderungen zu beobachten.

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Bonn, Germany from 1984 – 2023

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.

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Konstanz, Germany from 1984 – 2023

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.

Fernerkundung - Luftbilder Hamburg

In der <b> Fernerkundung - Luftbilder</b> werden aus großer Höhe Bilder von der Erdoberfläche aufgenommen, die anschließend aufbereitet und als hochwertige Geodaten bereitgestellt werden. <br> Diese Aufnahmen unterstützen bei der Dokumentation von Veränderungen, der städtischen Planung und der Überwachung von Umweltentwicklungen. Sie können sowohl als Datengrundlage für KI-Trainingsdaten als auch zur direkten Betrachtung der urbanen Landschaft genutzt werden. <br><br>Unser Ziel ist es, diese bedeutsamen Daten nicht nur Fachleuten, sondern auch der Öffentlichkeit zugänglich zu machen – leicht verständlich und nutzerfreundlich. <br><br><i>"Wie hat sich Hamburg entwickelt?" -- "Wie sah das Grundstück früher aus?" -- "Wo blüht es im Sommer?"</i> <br> <b>&#8594; Ein Blick in die Daten lohnt sich.</b><br><br> <u><i>Hinweis:</i></u> So vielfältig die Anwendungsbereiche sind, so vielfältig sind auch unsere Datensätze. Je nach Aufnahmesystem – ob <b>Drohne</b>, <b>Flugzeug</b> oder <b>Satellit</b> variieren die Bilder in ihrer Qualität und Detailtiefe. Diese Unterschiede zeigen sich etwa in der Bildauflösung (GSD), den Farbdarstellungen (spektrale Auflösung) und/oder der Aktualität der Daten (zeitliche Auflösung). Nähere Informationen sind aus den Metadaten der Datensätze zu entnehmen.<br>

Indikatoren des Klimawandels mittels Radio-Okkultation

Die internationale wissenschaftliche Gemeinschaft unternimmt große Anstrengungen in der Erforschung des globalen Klimawandels. Dennoch bestehen immer noch Unsicherheiten und Widersprüche im Hinblick auf Detektion, Ursachenzuweisung und Vorhersage von Klimatrends. Ein Hauptgrund ist der Mangel an genauen und langfristig stabilen Klimabeobachtungen. Dieser Umstand war Anlass für das Intergovernmental Panel on Climate Change (IPCC) Aktionen höchster Priorität für die zukünftige Klimabeobachtung zu definieren. Eine viel versprechendes Werkzeug zur Bewältigung dieses Problems ist die Radio-Okkultationsmethode (RO), welche Signale von Navigationssatelliten (GNSS - Global Navigation Satellite System) verwendet. Die besondere Eignung für Klimabeobachtungen resultiert aus der einzigartigen Kombination von Genauigkeit, Langzeit-Stabilität, globaler Bedeckung und Allwetter-Tauglichkeit. Die größte Genauigkeit von Klimavariablen (wie Temperatur und Geopotentielle Höhe von Druckschichten) wird in der oberen Troposphäre und unteren Stratosphäre (UTLS) erzielt. Die Veränderung der thermischen Struktur in dieser Höhenregion ist ein sensitiver Indikator für den Klimawandel. Hauptziel des Projektes ist in diesem Kontext die Exploration und das Auffinden von Indikatoren des atmosphärischen Klimawandels für die UTLS Region unter Verwendung von RO-basierten Klimatologien, sowie von 'proxy' Re-analyse- und Klimamodelldaten zur Untersuchung des Langzeitverhaltens. Da die vorhandenen RO Klimatologien nur über einen beschränkten Zeitraum (kontinuierliche Beobachtungen gibt es erst seit 2002) zur Verfügung stehen, erweitern wir unseren Beobachtungsdatensatz mit Re-Analysedaten bis ins Jahr 1979 zurück. Simulationsdaten von globalen Klimamodellen, kürzlich für den vierten IPCC Assessment Report (AR4) fertig gestellt, werden als'proxy' Daten bis zum Jahr 2050 verwendet. Die systematische Datenexploration, um die robustesten und sensitivsten RO basierten Trendindikatoren zu finden, erfolgt einerseits mittels vordefinierter Indikatoren über einen Multi-Model/Multi-Ensemble Ansatz, andererseits durch eine neue visualisierungs-basierte Exploration von 4D Feldern. Auf Basis der 'besten' Indikatoren erfolgt eine Untersuchung der RO Klimatologien bezüglich Trenddetektierung mit Hilfe von 'optimal detection (fingerprinting)' Methoden. Zudem wird eine Validierung der Klimamodelle mit Hilfe der Beobachtungsdaten durchgeführt. Ziel von INDICATE ist es, mit der Bereitstellung von optimalen RO basierten UTLS Klimatrend-Indikatoren sowie mit der Validierung von Klimamodellen einen wesentlichen Beitrag zu Monitoring und Erforschung des Klimawandels zu leisten.

DIGSTER - Map and Go (Digital Satellite Based Terrain Model) - User Requirements

The project DIGSTER - Map and Go (Digital Based Terrain Mapping) aims at the technical aspects of digital terrrain mapping. For many questions in administration, planning and expertise terrrain mappings are indispensable. The whole process starting with the data acquisition in the field and ending with map products will be digitally performed by the system. Therefore, a platform appropriate for the use in the field (PDA) is combined with technologies from the disciplines of satellite navigation, remote sensing, communication, and mobile geoinformation systems. For DIGSTER a lot of practical applications already exist in connection with policies and directives on the national and also European level.

Nachtaufnahme Hamburg

Bei dieser Nachtaufnahme handelt es sich um ein Mosaik, welches aus zwei unterschiedlichen Datensätzen besteht. Datensatz 1 [Zentrum und Randgebiete] - Satellitenaufnahmen: Jilin-1 - Spektrale Auflösung: RGB - Aufnahmezeitraum: 12/21 – 01/22 - Quelle: Chang Guang Satellite Technology CO., LTD (CGSTL)/Veritas Imagery Services Ltd Datensatz 2 [vereinzelte Randgebiete] - Astronautenphotographie: NASA - Spektrale Auflösung: RGB - Aufnahmezeitraum: 03/22 - Quelle: Earth Science and Remote Sensing Unit, NASA Johnson Space Center; https://eol.jsc.nasa.gov/ Hinweise: Die Datensätze unterscheiden sich hinsichtlich ihrer geometrischen Auflösung. Die Datensätze sind spektral nicht zueinander kalibriert. Verwendung nur zu Visualisierungszwecken.

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