Other language confidence: 0.771736494345375
Rewetting peatlands is an important measure to reduce greenhouse gas (GHG) emissions. However, after rewetting, the areas are highly heterogeneous in terms of GHG exchange, which depends on water level and source, vegetation, previous use, and duration of rewetting. These challenging conditions require new technologies that go beyond discrete sampling. Here we present data from two autonomous lander platforms deployed at the sediment-water interface (bottom lander) of a shallow coastal peatland (approx. 1 m water depth) that was rewetted by brackish water from the Baltic Sea, thus becoming part of the coastal water through a permanent connection. These landers were equipped with six commercially available state-of-the-art sensors, and temporal high-resolution measurements of physico-chemical variables, including partial pressures of carbon dioxide (CO2) and methane (CH4), were made. The resolution of the field data ranged from 10 seconds to 120 minutes and was obtained for partial pressure of CO2 (Contros HydroC-CO2) and CH4 (Contros HydroC-CH4), temperature, salinity, pressure (water depth), oxygen (O2) (CTD-O2 with SBE-37SMP-ODO), the concentrations of phosphate (SBE HydroCycle PO4), nitrate (SBE SUNA V2), chlorophyll a and the turbidity (both with SBE-FLNTUSB ECO) as stationary measurements at two different locations in close proximity. The CTD and oxygen measurements provide exact water depth data for the respective lander locations. In the other data sets (e.g., CO2 measurements) rounded data are inserted instead of the exact depth data, which is 0.6 m for lander_1 and 0.9 m for lander_2. SUNA raw data are provided for completeness. However, we found them of insufficient quality to estimate nitrate concentrations due to interferences and biofouling. The deployment and recovery of the landers, and thus the measurements, took place between 02 June 2021 and 09 August 2021, and the sensors were operated under permanent wired power supply and a centralized timestamp. The sensors were maintained and cleaned bi-weekly. Results show considerable temporal fluctuations expressed as multi-day, diurnal, and event-based variability, with spatial differences caused by biologically-dominated variables.
Le projet d'etude vise a determiner quel(s) impact(s) subissent la flore et la faune de la Grande Caricaie, a la suite des mesures d'entretien realisees pour maintenir les zones naturelles de la rive a un stade de marecages non-boises (moyens de lutte contre l'atterrissement et l'embroussaillement par fauchage et debroussaillement). trois programmes de surveillance scientifique ont ete mis sur pied concernant la vegetation (depuis 1985; resp. M. Antoniazza) et les invertebres (depuis 1989; resp. B. Muelhauser). Le but principal de ces programmes est d'assurer une protection la plus complete possible aux organismes vivant dans le plus grand paysage marecageux de Suisse. (FRA)
Rewetting peatlands is an important measure to reduce greenhouse gas (GHG) emissions. However, after rewetting, the areas are highly heterogeneous in terms of GHG exchange, which depends on water level and source, vegetation, previous use, and duration of rewetting. These challenging conditions require new technologies that go beyond discrete sampling. Here we present data from two autonomous lander platforms deployed at the sediment-water interface (bottom lander) of a shallow coastal peatland (approx. 1 m water depth) that was rewetted by brackish water from the Baltic Sea, thus becoming part of the coastal water through a permanent connection. These landers were equipped with six commercially available state-of-the-art sensors, and temporal high-resolution measurements of physico-chemical variables, including partial pressures of carbon dioxide (CO2) and methane (CH4), were made. The resolution of the field data ranged from 10 seconds to 120 minutes and was obtained for partial pressure of CO2 (Contros HydroC-CO2) and CH4 (Contros HydroC-CH4), temperature, salinity, pressure (water depth), oxygen (O2) (CTD-O2 with SBE-37SMP-ODO), the concentrations of phosphate (SBE HydroCycle PO4), nitrate (SBE SUNA V2), chlorophyll a and the turbidity (both with SBE-FLNTUSB ECO) as stationary measurements at two different locations in close proximity. The CTD and oxygen measurements provide exact water depth data for the respective lander locations. In the other data sets (e.g., CO2 measurements) rounded data are inserted instead of the exact depth data, which is 0.6 m for lander_1 and 0.9 m for lander_2. SUNA raw data are provided for completeness. However, we found them of insufficient quality to estimate nitrate concentrations due to interferences and biofouling. The deployment and recovery of the landers, and thus the measurements, took place between 02 June 2021 and 09 August 2021, and the sensors were operated under permanent wired power supply and a centralized timestamp. The sensors were maintained and cleaned bi-weekly. Results show considerable temporal fluctuations expressed as multi-day, diurnal, and event-based variability, with spatial differences caused by biologically-dominated variables.
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.
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.
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.
Samples were taken to study the effect of storm surges on ecosystem functioning of salt marsh microbial communities. Sediment samples were collected from experimental salt marsh islands located in the back-barrier tidal flats of Spiekeroog Island, German North Sea (53°45′N, 7°43′E). The islands consist of three elevation zones (0.7 m, 1.0 m, and 1.3 m above mean sea level), corresponding to pioneer zone, lower salt marsh, and upper salt marsh. Six islands were sampled (three initially bare; three transplanted with lower salt marsh sediment and vegetation). Sampling was conducted in September 2022 (pre-disturbance), March 2023 (post-winter storm surges), and August 2023 (recovery phase). Surface sediments (upper 2 cm) were collected using syringe cores. Pooled samples were analyzed for chlorophyll a as a proxy for microphytobenthos biomass using ethanol extraction and spectrophotometric pigment analysis. Extracellular polymeric substances (EPS) were quantified using EDTA extraction followed by phenol–sulfuric acid carbohydrate analysis. DNA was extracted from sediment subsamples using a Qiagen PowerSoil kit. Prokaryotic abundance was estimated by quantitative PCR targeting the 16S rRNA gene (primers 519F/907R), using an Escherichia coli 16S rRNA gene standard curve. The dataset includes chlorophyll a concentrations (µg g⁻¹ dry sediment), EPS carbohydrate concentrations, and prokaryotic 16S rRNA gene copy numbers for all sampling times, elevations, and treatments.
Um die Gesundheit der Menschen und die Vegetation vor den Einflüssen zu hoher Luftschadstoffbelastungen zu schützen, wird die Luftqualität laufend untersucht und nach gesetzlichen Vorschriften beurteilt. Dafür betreibt das Landesamt für Umwelt (LfU) in Schleswig-Holstein ein Netz aus Messstationen, an denen mit unterschiedlichen Methoden Luftschadstoffe gemessen werden. Die Messdaten aus Schleswig-Holstein und viele zusätzliche Informationen zu den Messungen werden an das Umweltbundesamt weiter geleitet und von dort gemeinsam mit den Daten aller Bundesländer an die Europäische Kommission gemeldet. Alle aktuell veröffentlichten Daten sind als ***vorläufig*** einzustufen, da sie zu Ihrer schnellen Information zunächst automatisch auf Gültigkeit geprüft werden. Vor der abschließenden Bewertung und Beurteilung der Luftqualität findet später eine mehrstufige Prüfung nach gesetzlichen Vorgaben statt. Bei den CSV-Dateien „fehlt“ am Tag der Umstellung von Normalzeit (MEZ) auf Sommerzeit (MESZ) die 3-Uhr-Messung, am Tag der Umstellung von Sommer- auf Normalzeit gibt es hingegen zwei 3-Uhr-Messungen. Die JSON-Dateien sind von dieser Problematik nicht betroffen, hier wird durchgängig Normalzeit verwendet. [Informationen zur Messstation](https://www.schleswig-holstein.de/DE/Fachinhalte/L/luftqualitaet/Messstationen/LuebeckMoislAl.html)
Um die Gesundheit der Menschen und die Vegetation vor den Einflüssen zu hoher Luftschadstoffbelastungen zu schützen, wird die Luftqualität laufend untersucht und nach gesetzlichen Vorschriften beurteilt. Dafür betreibt das Landesamt für Umwelt (LfU) in Schleswig-Holstein ein Netz aus Messstationen, an denen mit unterschiedlichen Methoden Luftschadstoffe gemessen werden. Die Messdaten aus Schleswig-Holstein und viele zusätzliche Informationen zu den Messungen werden an das Umweltbundesamt weiter geleitet und von dort gemeinsam mit den Daten aller Bundesländer an die Europäische Kommission gemeldet. Alle aktuell veröffentlichten Daten sind als ***vorläufig*** einzustufen, da sie zu Ihrer schnellen Information zunächst automatisch auf Gültigkeit geprüft werden. Vor der abschließenden Bewertung und Beurteilung der Luftqualität findet später eine mehrstufige Prüfung nach gesetzlichen Vorgaben statt. Bei den CSV-Dateien „fehlt“ am Tag der Umstellung von Normalzeit (MEZ) auf Sommerzeit (MESZ) die 3-Uhr-Messung, am Tag der Umstellung von Sommer- auf Normalzeit gibt es hingegen zwei 3-Uhr-Messungen. Die JSON-Dateien sind von dieser Problematik nicht betroffen, hier wird durchgängig Normalzeit verwendet. [Informationen zur Messstation](https://www.schleswig-holstein.de/DE/Fachinhalte/L/luftqualitaet/Messstationen/LuebeckMoislAl.html)
Um die Gesundheit der Menschen und die Vegetation vor den Einflüssen zu hoher Luftschadstoffbelastungen zu schützen, wird die Luftqualität laufend untersucht und nach gesetzlichen Vorschriften beurteilt. Dafür betreibt das Landesamt für Umwelt (LfU) in Schleswig-Holstein ein Netz aus Messstationen, an denen mit unterschiedlichen Methoden Luftschadstoffe gemessen werden. Die Messdaten aus Schleswig-Holstein und viele zusätzliche Informationen zu den Messungen werden an das Umweltbundesamt weiter geleitet und von dort gemeinsam mit den Daten aller Bundesländer an die Europäische Kommission gemeldet. Alle aktuell veröffentlichten Daten sind als ***vorläufig*** einzustufen, da sie zu Ihrer schnellen Information zunächst automatisch auf Gültigkeit geprüft werden. Vor der abschließenden Bewertung und Beurteilung der Luftqualität findet später eine mehrstufige Prüfung nach gesetzlichen Vorgaben statt. Bei den CSV-Dateien „fehlt“ am Tag der Umstellung von Normalzeit (MEZ) auf Sommerzeit (MESZ) die 3-Uhr-Messung, am Tag der Umstellung von Sommer- auf Normalzeit gibt es hingegen zwei 3-Uhr-Messungen. Die JSON-Dateien sind von dieser Problematik nicht betroffen, hier wird durchgängig Normalzeit verwendet. [Informationen zur Messstation](https://www.schleswig-holstein.de/DE/Fachinhalte/L/luftqualitaet/Messstationen/LuebeckMoislAl.html)
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