The Germany Mosaic is an analysis-ready spatiotemporal dataset with a consistent data-cube structure, covering the entire territory of Germany from 1984 to 2025. It provides consistently processed Landsat-based raster and vector products on a common spatial grid, enabling long-term analysis of environmental and landscape dynamics as well as machine-learning and AI-based applications. For each year, two seasonal six-band multispectral composites are provided, representing the periods May–July and August–October. All usable Landsat observations acquired within the respective seasonal period are included in the compositing process to maximize spatial coverage and minimize cloud contamination, resulting in virtually cloud-free seasonal image composites. The spatial organization is consistent across the complete time series: all years use the same pixel size, spatial grid, tile geometry, band structure, and variable schema for the vector products. Germany is represented by 54 overlapping tiles of 120 × 120 km with a 20 km overlap. This standardized structure enables direct spatial and temporal comparison across more than four decades without additional spatial harmonization or restructuring. In addition to the multispectral raster data, the dataset contains image-segmentation polygons (“zones”) representing approximately homogeneous image regions, together with associated spectral, textural, statistical, geometric, and contextual attributes. The vector datasets include 32 attributes covering seasonal spectral information, vegetation indices, temperature, image quality, texture and variability measures, Rao’s diversity, elevation-related information, and indicators concerning the form, size, connectivity, distribution, and spatial relationships of the zones. Additional raster products provide elevation data and statistics for seasonal vegetation change. The consistent data-cube structure makes the Germany Mosaic particularly suitable for large-scale time-series analysis, change detection, environmental monitoring, machine learning, and other AI-based workflows. It can also provide a data foundation for environmental modelling and digital-twin applications. This dataset constitutes the second version and continuation of the previously published Germany Mosaic 1984–2023 dataset (DOI: 10.1594/PANGAEA.967266) and extends the temporal coverage through 2025 while introducing a revised and standardized spatial organization based on 54 consistently defined tiles, a 20 km overlap between the tiles, and new raster and vector features. Content - Two seasonal six-band multispectral image composites per year: May–July and August–October - Seasonal vegetation-change statistics as a raster image (AIR Files) - One separate set of elevation data with identical tiling - Image-segmentation polygons (“zones”) with 32 spectral, textural, statistical, geometric, and contextual attributes - Raster and vector products for each year from 1984 to 2025 - Data organized in 54 overlapping tiles covering Germany - Formats: GeoTIFF for all raster products; Esri Shapefile for all vector products - Optical Sensors: Thematic Mapper (TM) on Landsat 4, Enhanced Thematic Mapper (ETM) on Landsat 5, Operational Land Imager (OLI) on Landsat 8 and 9 - Thermal Sensors: Thermal Infrared Sensor (TIRS) on Landsat 4, 5, 8, and 9 - Elevation Sensors: Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra Satellite - All usable observations within the two seasonal periods are included in the compositing process Features - Time: Two seasonal composites per year, May–July and August–October - Data cube: Same pixel size, spatial grid, tile geometry, band structure, and variable schema for all years - Raster: Six-band spectral image composites for each season (summer and autumn) and one three-band image composite for seasonal vegetation-change statistics - Elevation: Raster data once for each tile - Vector: Image-segmentation polygons (“zones”) with spectral and geometric attributes - Attributes: 32 attributes covering seasonal spectral composites (six bands), seasonal vegetation indices (NDVI, NIRv), seasonal vegetation-change statistics, seasonal temperature, elevation, seasonal image-quality index, textures, Rao’s diversity, size, geometry, connections, and spatial relationships of the zones - Tiles: 54 overlapping 120 × 120 km tiles with 20 km overlap - CRS: WGS 84 / UTM zone 32N (EPSG:32632) - Quality: Virtually cloud-free seasonal composites generated from all usable observations See D-Mosaic_Manual for details and background. Applications - Time-series and change analysis, environmental monitoring, machine learning, AI-based workflows, environmental modelling, and digital twins Figures Figure 1 – RGB visualization of three vector attributes based on polygons: - Red: structural diversity (texture) - Green: highest vegetation activity (plant metabolic activity) during the year - Blue: seasonal variation between summer and autumn - Black: zone boundaries Figure 2 – Distribution of the 54 overlapping 120 × 120 km tiles covering Germany.
Web Map Service (WMS) für die Bereitstellung von Satellitendaten des Copernicus-Programms. Der LGV bereitet die Daten zur einfacheren Nutzung quartalsweise auf. Die dargestellten Geodaten beinhalten zusätzlich eine zeitliche Dimension (WMS-Time). Zur genaueren Beschreibung der Daten und Datenverantwortung nutzen Sie bitte den Verweis zur Datensatzbeschreibung.
**Hinweis** Dieser Datensatz wird bis zur Fertigstellung der neuen Version (2026) nicht mehr aktualisiert. Die Europäische Weltraumorganisation (ESA) stellt innerhalb des Copernicus-Programms kostenfrei Satellitendaten der Missionen Sentinel (dt. Wächter) zur Verfügung. Der LGV bereitet die Daten zur einfacheren Nutzung quartalsweise auf. Originär können die Satellitendaten über die nationale Plattform CODE-DE (Copernicus Data and Exploitation Platform – Deutschland) bezogen werden. Datengrundlage: - Sentinel-2 L2A: Multispektrale, atmosphärisch korrigierte Daten - Georeferenziertes Mosaik - Kachel-Anzahl: 25 - Kachel-Größe: 8 km x 8 km - Kachel-Auswahl: Aktualität und Grad der Wolkenbedeckung - Farbdarstellung: RGB, CIR, NDVI - Bodenauflösung: 10m - Farbtiefe: 8 bit RGB (Red Green Blue): Die Bandkombination aus Rot (B4), Grün (B3) und Blau (B2) bildet die menschliche Farbwahrnehmung nach. Gesunde Vegetation wird grün, urbane Flächen werden weiß / grau und Wasserflächen werden, abhängig der Trübung, blau dargestellt. CIR (Color Infrared): Die Bandkombination aus nahem Infrarot (B8), Rot (B4) und Grün (B3) hebt die Vegetation hervor. Diese reflektiert aufgrund des Chlorophyllgehalts der Pflanzen im nahen Infrarotbereich besonders stark und wird rötlich dargestellt. Urbane Flächen erscheinen cyan-blau / grau und Wasserflächen dunkelblau. NDVI (Normalized Difference Vegetation Index): Der NDVI ist ein häufig angewendeter Index, welcher zur Einschätzung der Vegetation herangezogen wird. Er berechnet sich aus den Bändern Nahes Infrarot (B8) und Rot (B4): NDVI = (NIR-Rot)/(NIR+Rot) <b>Hinweis</b> "Keine Daten verfügbar": Wenn innerhalb eines Quartals ausschließlich Daten mit hoher Wolkenbedeckung vorliegen, wird kein Mosaik erzeugt. Dies ist insbesondere in Wintermonaten möglich. [© Contains modified Copernicus Sentinel data [2018-2024], processed on CODE-DE]
Oberflächentemperaturen am Abend des 13.08.2000 und Morgen des 14.08.2000 sowie die Temperaturdifferenzen Abend-Morgen, langwelliger Wellenlängenbereich zwischen 10,4 bis 12,5 µm, Bearbeitungsstand März 2001.
Oberflächentemperaturen am Abend des 14.09.1991 und Morgen des 15.09.1991 sowie die Temperaturdifferenzen Abend-Morgen, langwelliger Wellenlängenbereich zwischen 10,4 bis 12,5 µm, Bearbeitungsstand November 1992.
This raster dataset shows forest canopy cover loss (FCCL) in Germany at a monthly resolution from September 2017 to October 2025. It is similar to the product developed by Thonfeld et al. (2022) but was fully reprocessed and updated to reveal the most recent forest disturbance dynamics (Thonfeld et al. 2026). The combination of Sentinel-2A/B and Landsat-8/9 data allows for a high temporal resolution while the pixel size of the product is 10 m. The results are clipped to the stocked area 2018 mapped by the Thünen-Institute (Langner et al. 2022, https://doi.org/10.3220/DATA20221205151218). The dataset contains predominantly larger canopy openings resulting from different drivers but also larger clusters of standing deadwood. FCCL can result from abiotic (e.g. wind, fire, drought, hail) drivers, biotic (e.g. insects, funghi) drivers or a combination of both as well as from sanitary and salvage logging and planned harvest. The first version with canopy cover losses from January 2018 - April 2021 (Thonfeld et al. 2022) can be accessed here: https://geoservice.dlr.de/web/datasets/tccl.
This vector dataset is based on a 10 m resolution raster dataset that shows forest canopy cover loss (FCCL) in Germany at a monthly resolution from September 2017 to October 2025. Results at pixel level were aggregated at municipality, district, and federal state level. For the results at administrative level we differentiate between deciduous and coniferous forests. We use the stocked area map 2018 (Langner et al. 2022, https://doi.org/10.3220/DATA20221205151218 ) as a reference forest mask. We differentiate between deciduous and coniferous forests by intersecting the stocked area map with a tree species map (Blickensdoerfer et al. 2024). Pixels of the classes birch, beech, oak, alder, deciduous trees with long lifespan and deciduous trees with short lifespan were classified as deciduous forest and pixels of the classes Douglas fir, spruce, pine, larch and fir as coniferous forest. The coverage of the two datasets is not identical, which is why a few areas of the forest reference map remained unclassified. These were filled with the dominant leaf type map of the Copernicus Land Monitoring Service (CLMS 2025). Therefore, the vector data at administrative level contains information about unclassified forest areas and the total forest area as the sum of deciduous, coniferous, and unclassified forests. The FCCL confidence at pixel level is lowest at the end of the time series because the number of repeated threshold exceedance is used as a criterion to record forest canopy cover losses. Therefore, we excluded August 2024 through October 2025 from the annual and overall statistics and summarized the respective FCCL as additional attribute. The dataset provides an update of the assessment in Thonfeld et al. (2026).
Aufgrund von Ergebnissen einer vorausgehenden Untersuchung, nach der eine differenzierte und mit Bodenbefunden vergleichbare Waldschadenskartierung in aelteren Fichtenreinbestaenden mit Hilfe von LANDSAT-TM-Daten moeglich ist, wird an folgenden Problemen gearbeitet: - Verfahrensverbesserungen durch GIS-Einsatz (Auswahl von Trainingsgebieten; Verifizierung von Befunden; Adjustierung von Daten und Ergebnissen). - Analyse von Zusammenhaengen zwischen Waldschaeden und oekologisch relevanten Einflussfaktoren mittels GIS. - Veraenderungsnachweise. - Einbeziehung anderer Baumarten und Bestandesaufbauformen. - Untersuchung der Einfluesse unterschiedlicher Waldbau-Konzepte in Niedersachsen und Sachsen-Anhalt.
This first part of the project deals with the calibration and intercalibration of remote sensing data (NOAA-AVHRR and LANDSAT images), gathered during the EFEDA 1991 field experiment and the application of corrections for aspect angle, surface emissivity, and atmospheric effects. Data from other observation systems will be included as they become available. The accuracy of the inferred informations is determined by intercomparisons with ground based measurements made under controlled conditions. Surveys will be carried in the experimental area to obtain more information about the angular distribution of the reflected solar radiation over the different plant communities in dependence of the physiological and structural parameters of the plant-soil systems. A major effort will be made to relate directional reflectances, surface temperatures and vegetation indices derived from the spectral measurements made in space to energy and water fluxes between the surface and the atmosphere measured by the EFEDA flux groups and vegetation parameters observed at the surface by the EFEDA vegetation group. The second part of the project deals with the determination of area averages and the study of temporal changes. Validated information derived from the satellite data will be scaled up into a size comparable with the grid width of global models to support the determination of energy and water exchanges between the surface and the atmosphere, and the definition of a desertification index based on satellite observations. For this purpose time series of these data will be analysed with respect to interannual changes related to the weather development and possible trends. An attempt will be made to correlate cumulative annual data of vegetation index with biomass production. The broadening of the geographical scope of EFEDA will be prepared by processing NOAA-AVHRR satellite data for the whole of the Mediterranean coastal zone which can be provided to users with information about the applied corrections and interpretation aids.
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