Selsam Peter 0000-0001-6122-7880 Helmholtz Centre for Environmental Research - UFZ UFZ http://www.ufz.de/ https://ror.org/000h6jb29 Lausch Angela angela.lausch@ufz.de 0000-0002-4490-7232 Helmholtz Centre for Environmental Research - UFZ UFZ http://www.ufz.de/ https://ror.org/000h6jb29 Bumberger Jan jan.bumberger@ufz.de 0000-0003-3780-8663 Helmholtz Centre for Environmental Research - UFZ UFZ http://www.ufz.de/ https://ror.org/000h6jb29 2025 Satellite Color Images, Vegetation Indices, and Metabolism Indices from Aurich, Germany from 1985 – 2023 dataset Helmholtz Centre for Environmental Research - UFZ https://doi.org/10.1594/PANGAEA.971979 2025-01-14T10:14:40 https://doi.org/10.1594/PANGAEA.967266 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. Selsam Peter 0000-0001-6122-7880 Bumberger Jan jan.bumberger@ufz.de 0000-0003-3780-8663 Wellmann Thilo 0000-0002-6852-5095 Pause Marion 0000-0002-3836-2723 Gey Ronny 0000-0003-1028-1670 Borg Erik 0000-0001-8288-8426 Lausch Angela angela.lausch@ufz.de 0000-0002-4490-7232 2024 Ecosystem Integrity Remote Sensing—Modelling and Service Tool—ESIS/Imalys journal article Remote Sensing 16(7) https://doi.org/10.3390/rs16071139 1139 Selsam Peter 0000-0001-6122-7880 Gey Ronny 0000-0003-1028-1670 Lausch Angela angela.lausch@ufz.de 0000-0002-4490-7232 Bumberger Jan jan.bumberger@ufz.de 0000-0003-3780-8663 2023 Imalys - Image Analysis software Zenodo https://doi.org/10.5281/ZENODO.8116370 Selsam Peter 0000-0001-6122-7880 2024 Imalys – ESIS tool for Image Analysis other https://github.com/c7sepe2/Imalys_ESIS-Software-Tools Selsam Peter 0000-0001-6122-7880 2024 D-Mosaic (1984-2023) - data description other 3 https://download.pangaea.de/reference/125290/attachments/D-Mosaik_Data-Description.pdf Blattschnitt der Topographischen Karte 1:100 000 (TK100) (b100) https://gdz.bkg.bund.de/index.php/default/blattschnitt-der-topographischen-karte-1-100-000-tk100-b100.html 7.33252773 7.99909228 53.198494199 53.598450135 7.665810005 53.398472167 1985-05-01T00:00:00 2023-10-31T00:00:00 ESIS_Aurich c2710 53.198494199 7.33252773 1985-05-01T00:00:00 53.598450135 7.99909228 2023-10-31T00:00:00 Remote sensing 1920 CC-BY-4.0 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ Area/locality Area Area http://dbpedia.org/resource/Area Area Year of observation Year obs a AD year Time T http://dbpedia.org/resource/Time Year obs [a AD] Date/time start Date/time start http://en.wikipedia.org/wiki/ISO_8601 Date/time start Date/time end Date/time end http://en.wikipedia.org/wiki/ISO_8601 Date/time end File content Content Content Raster graphic, GeoTIFF format GeoTIFF GeoTIFF Geospatial vector, shapefiles Shapefile Shapefile File type File type File type Site information Site info Site info CurationLevelC Enhanced curation https://wiki.pangaea.de/wiki/Curation_levels citable ecosystem transitions Germany Image segmentation indicators Landsat 5 Landsat 6 Landsat 8 Landsat 9 Landscape pattern Landscape structure NDVI NIRv Vegetation index author104131 author104996 author104997 author104998 author104999 author74068 author81028 curlevel30 event3167321 geocode1599 geocode1600 geocode1601 inst468 journal16659 license21 method10686 param145013 param150986 param152460 param15373 param16142 param512817 param514770 param519108 param99583 ref125224 ref125225 ref125290 ref125291 ref126064 sourceinst468 term41076 term43770 term43975 term48533 term48682 term50190 term52773 term72328 term73325 term73666 term74041 term74042 term74043 term74171 term74172 term74173