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Morphometries of Emiliania huxleyi in the Pacific sector of the XX Italian Expedition from New Zealand to Antarctic
No full textThis micropalaeontological dataset includes morphometries of the extant coccolithophore species Emiliania huxleyi in two transects in the Pacific sector of the Southern Ocean (from New Zealand to Antarctica and across the Drake Passage). The New Zealand transect: 13 selected surface water samples were collected from the ship's pump of the R/V Italica (at ca. 3 m water depth) from 46.81°S to 62.49°S during the XX Italian Expedition from New Zealand to Antarctica (31st December 2004- 6th January 2005). In total, 377 coccoliths of E. huxleyi were classified and measured using Scanning Electron Microscope (SEM) images
Morphometries of Emiliania huxleyi in the Pacific sector of the POLARSTERN cruise PS97 (ANT-XXXI/3)
No full textThis micropalaeontological dataset includes morphometries of the extant coccolithophore species Emiliania huxleyi in two transects in the Pacific sector of the Southern Ocean (from New Zealand to Antarctica and across the Drake Passage). The Drake Passage transect: 19 water samples were collected on a transect at the western end of the Drake Passage (55.44°S to 61.75°S) during Polarstern Expedition PS97 from 24th February 2016 to 5th March 2016. The shallowest CTD water samples (retrieved at 5, 10 and 20 m water depth) were considered in this study. A total of 203 E. huxleyi coccoliths were classified and measured using SEM images
GEM-2 ice and platelet total thickness measurements for transect AFIN_ATKA_20221027
No full textThe snow+ice thickness (distance from the snow surface to the consolidated ice-platelet ice interface) and the underlying platelet ice thickness was measured by the electromagnetic induction (EM) method. On all transects, we used a multi-frequency EM instrument sensor (GEM-2 by Geophex Ltd) towed on a small sled behind a snowmobile (Hunkeler et al, 2016b). The instrument includes a real-time data processing unit including a GPS receiver which communicates with a pocket PC that is operates the sensor and records the EM and GPS data streams. The GEM-2 is a multi-frequency sensor that can transmit multiple configurable frequencies in the kHz range simultaneously. The sensor setup during AFIN used 5 frequencies with an approximately logarithmic spacing throughout the frequency range of the sensor (1.53, 5.31, 18.33, 63.03, and 93.09 kHz). The transect measurements are based on a sensor calibration, where the GEM-2 was placed in a zero-conductivity environment on the ice shelf before and after each transect to correct for signal offsets in instrument drift. The snow+ice and platelet ice thickness and conductivity retrieval of the calibrated GEM-2 surveys data was done by multi-frequncy inversion in the modified framework of the EMagPy (McLachlan et al., 2020) accounting for the bucking coil bias (Hunkeler at al., 2016a). To validate the retrieved thicknesses, we used the snow depth, ice thickness and platelet ice thickness from the drill hole measurements coincident with the GEM-2 transects. The coincident drill hole data can be found here: doi:10.1594/PANGAEA.968459. For more details we refer to the paper by Neudert et al, 2024: Improved sub-Ice Platelet Layer Mapping with multi-frequency EM Induction Sounding, in review at Journal of Applied Geophysics, Elsevier
GEM-2 ice and platelet total thickness measurements for transect AFIN_ATKA_20221230
No full textThe snow+ice thickness (distance from the snow surface to the consolidated ice-platelet ice interface) and the underlying platelet ice thickness was measured by the electromagnetic induction (EM) method. On all transects, we used a multi-frequency EM instrument sensor (GEM-2 by Geophex Ltd) towed on a small sled behind a snowmobile (Hunkeler et al, 2016b). The instrument includes a real-time data processing unit including a GPS receiver which communicates with a pocket PC that is operates the sensor and records the EM and GPS data streams. The GEM-2 is a multi-frequency sensor that can transmit multiple configurable frequencies in the kHz range simultaneously. The sensor setup during AFIN used 5 frequencies with an approximately logarithmic spacing throughout the frequency range of the sensor (1.53, 5.31, 18.33, 63.03, and 93.09 kHz). The transect measurements are based on a sensor calibration, where the GEM-2 was placed in a zero-conductivity environment on the ice shelf before and after each transect to correct for signal offsets in instrument drift. The snow+ice and platelet ice thickness and conductivity retrieval of the calibrated GEM-2 surveys data was done by multi-frequncy inversion in the modified framework of the EMagPy (McLachlan et al., 2020) accounting for the bucking coil bias (Hunkeler at al., 2016a). To validate the retrieved thicknesses, we used the snow depth, ice thickness and platelet ice thickness from the drill hole measurements coincident with the GEM-2 transects. The coincident drill hole data can be found here: doi:10.1594/PANGAEA.968459. For more details we refer to the paper by Neudert et al, 2024: Improved sub-Ice Platelet Layer Mapping with multi-frequency EM Induction Sounding, in review at Journal of Applied Geophysics, Elsevier
Satellite Color Images, Vegetation Indices, and Metabolism Indices from Ansbach, Germany from 1985 – 2023
No full textThe "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 Augsburg, Germany from 1984 – 2023
No full textThe "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