Satellite Color Images, Vegetation Indices, and Metabolism Indices from Nordhausen, Germany from 1986 – 2023

DOI

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.

Identifier
DOI https://doi.pangaea.de/10.1594/PANGAEA.972273
Related Identifier IsPartOf https://doi.pangaea.de/10.1594/PANGAEA.967266
Related Identifier References https://doi.org/10.3390/rs16071139
Related Identifier References https://doi.org/10.5281/ZENODO.8116370
Related Identifier References https://github.com/c7sepe2/Imalys_ESIS-Software-Tools
Related Identifier IsDocumentedBy https://gdz.bkg.bund.de/index.php/default/blattschnitt-der-topographischen-karte-1-100-000-tk100-b100.html
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.972273
Provenance
Creator Selsam, Peter ORCID logo; Lausch, Angela ORCID logo; Bumberger, Jan ORCID logo
Publisher PANGAEA
Contributor Helmholtz Centre for Environmental Research - UFZ
Publication Year 2024
Rights Creative Commons Attribution 4.0 International; Data access is restricted (moratorium, sensitive data, license constraints); https://creativecommons.org/licenses/by/4.0/
OpenAccess false
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 1968 data points
Discipline Earth System Research
Spatial Coverage (10.665W, 51.199S, 11.332E, 51.599N)
Temporal Coverage Begin 1986-05-01T00:00:00Z
Temporal Coverage End 2023-10-31T00:00:00Z