We present HeideBench, a very-high-resolution multispectral uncrewed aerial vehicle dataset for forest crown phenology collected over a forest patch in Dölauer Heide, Halle (Saale), Germany. Dölauer Heide is currently dominated by pine plantations (Kiefernforste), which cover the largest area but are increasingly affected by dieback, while its potential natural vegetation is sessile oak–hornbeam forest rich in small-leaved lime (Albrecht et al., 1993). In addition to these pine stands, the area contains near-natural mixed deciduous forests with oaks, birches, and beeches, making it a particularly relevant setting for observing seasonal canopy development under contrasting forest structures and ongoing ecological transition. Against this background, HeideBench provides repeated observations of the same forest patch through the growing season. The dataset contains 18 georeferenced multispectral GeoTIFF orthomosaics acquired between 6 March 2025 and 5 November 2025, spanning a 244-day seasonal period from early spring to late autumn. The acquisitions have a median revisit interval of 14 days, with intervals ranging from 4 to 27 days, and an average ground sampling distance of 5.53 cm per pixel. The valid imaging footprint covers approximately 32.1 ha and is bounded by 11.902653–11.911325°E and 51.499959–51.508576°N. Data were collected using a DJI Mavic 3M Enterprise uncrewed aerial vehicle equipped with four multispectral cameras measuring green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (860 nm) reflectance, in that order. Flights used a real-time kinematic (RTK) positioning module for centimeter-level geolocation, and all data are provided in coordinate reference system EPSG:25832. Imagery was processed with Agisoft Metashape 2.3.1 to generate calibrated multispectral orthomosaics. The dataset further includes 5,885 crop-safe individual tree crown instance segmentations over the same footprint, extracted with the DeepTrees software package (Khan et al., 2025). HeideBench is intended to support crown-centric analyses of seasonal canopy development, temporal representation learning, phenology-aware feature extraction, and the evaluation of tree crown delineation under seasonal change. HeideBench is a result of the Dynamic Platform Project titled "PhenoEmbed: Multispectral UAV AI Embeddings for phenology-aware tree crown delineation" of the Integration Platform 1: "Sustainable future land use" (IP1) at the Helmholz Centre for Environmental Research (UFZ) in Leipzig, Germany.
Zielsetzung: Ziel von PeroCycle ist es, ein industrietaugliches Recyclingverfahren für Perowskitmodule zu entwickeln. Da Perowskitmodule umwelt- und gesundheitsschädliches Blei enthalten, sollte bereits jetzt an die Entsorgung der Module nach Erreichen der Lebensdauer gedacht werden. In unserem Projekt sollen Perowskit-Minimodule am ZSW hergestellt und verkapselt werden. Dadurch, dass unterschiedliche Arten von Modulen recycelt werden sollen, wird gleichzeitig die Praxistauglichkeit des Recyclingverfahrens geprüft. Die verkapselten Perowskit-Module sollen bei der FLAXRES GmbH mittels Lichtpulstechnologie aufgetrennt werden. Getestet werden soll eine Auftrennung so, dass der Glas-Polymer-Verbund, und damit das Glas als Ganzes, effektiv vom Absorbermaterial getrennt wird. Somit soll im Gegensatz zum gängigen Schreddern keine Vermischung mit den anderen Materialien erfolgen. Das Glas kann daher erneut zu Flachglas verarbeitet werden. Das Perowskit-Absorbermaterial wird sortenrein eingesammelt und es muss lediglich 1/3000 der Gewichtsmenge eines Moduls chemisch aufbereitet werden. Nach der Trennung der Materialien erfolgt die Entwicklung und Optimierung eines Perowskit-Recyclingverfahrens beim assoziierten Partner Solaveni GmbH. Für den Recyclingprozess werden selbstentwickelte nicht brennbare, kostengünstige und umweltfreundliche Lösungsmittelsysteme eingesetzt, die den Einsatz von toxischen Lösungsmitteln obsolet machen und auf den Einsatz von extremen Bedingungen, wie bspw. hohe Temperaturen verzichten. Dieser Ansatz soll es ermöglichen, die Kosten und die Umweltauswirkungen zu minimieren, indem der Energieverbrauch und die Abfallproduktion reduziert und die Kreislaufwirtschaft gefördert wird. Das angedachte Verfahren umfasst chemische und physikalische Bearbeitungsverfahren, wobei mindestens 90% der alten Absorbermaterialien zurückgewonnen werden sollen, die nach dem Recyclingprozess eine Reinheit von >=99% aufweisen. Am ZSW sollen aus den recycelten Absorbermaterialien (sowie mit den recycelten TCO-beschichteten Gläsern) neue Perowskitmodule hergestellt werden. Die Module aus den recycelten Materialien sollen mindestens 90% des Wirkungsgrads der frisch hergestellten Referenzproben aufweisen.
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.
We report the results of high throughput, robust, and sensitive method for the precise analysis of 56Fe/54Fe and 57Fe/54Fe, performed using Multi-Collector-Inductively Coupled Plasma-Mass Spectrometer (MC-ICP-MS), Thermo Scientific Neptune PlusTM. We measured the Fe isotope compositions of widely used standard reference materials ranging from basaltic to rhyolitic compositions (JB-2, BHVO-2, BE-N, AGV-1, and RGM-1). We also propose a new iron isotope reference material, IAEA-B5, a basalt from Mount Etna already commonly used as a reference for B and Li isotopes. Funding information: EU-Next Generation EU Mission 4 ‘Education and Research’-Component 2: ‘From research to business’-Investment 3.1: ‘Fund for the realization of an integrated system of research and innovation infrastructures’-Project IR0000032-ITINERIS-Italian Integrated Environmental Research Infrastructures System-CUP B53C22002150006. EPOS European Plate Observing System
This pre-study pilot project will be carried out in Kenya and Tanzania and is part of a more extensive remote sensing project (initiated by the European Space Agency, ESA) aiming to develop a monitoring system for the assessment of land cover change of farmlands, rangelands and forest standings (logging, fires, uncontrolled deforestation, new settlements, etc.) at a national regional level. An integrated approach of remote sensing techniques (both through the use of satellite and ground data), physical vegetation models and ground measurements will be adopted. Operatively, the execution will consist of a 6-month period (pre-study) consisting in a ground campaign along a north-south transect, which is almost unknown to the current vegetation cartography. Based on the field results of the pre-study and within an on-going 30 month period (extended study, see Annexed 3), new classification methods and algorithms will be developed for assessment of land use and cover change using ENVISAT-data. An outcoming of this research will be a system capable to monitor and plan the available agricultural food resources for those developing regions.
Von dem am 16. Juli 1991 erfolgreich gestarteten ersten europaeischen Erderkundungssatelliten ERS-1 werden wesentliche Beitraege zum besseren Verstaendnis der komplexen Vorgaenge und dynamischen Veraenderungen im Gesamtsystem Erde und somit Entscheidungsgrundlagen fuer Massnahmen zum Schutze unserer Umwelt erwartet. Hinsichtlich der Einsatz- und Auswertmoeglichkeiten der SAR-Daten ueber Landoberflaechen mit ausgepraegtem Relief (wie in der Schweiz) bestehen erst geringe Kenntnisse. Es bedarf hier umfangreicher Grundlagenforschung, um diese Daten bei der Loesung aktueller, unser Land betreffender Aufgaben sinnvoll einsetzen zu koennen.
Zielsetzung: Im Sommer heizen sich Altstädte besonders auf, denn es fehlen kühlende Grünstrukturen und Frischluftschneisen, was für die Einwohnenden zu einer gesundheitlichen Belastung führen kann. Zudem führt der hohe Versiegelungsgrad bei einem Starkregenereignis zu einer hohen Abflussmenge, wodurch Kanalisationen an ihre Kapazitätsgrenze stoßen und die Überflutungsgefahr steigt. Dabei sind insbesondere die historisch wertvollen Gebäude vor Beschädigungen zu schützen und zum Teil auch als Kulturdenkmal zu erhalten. Mit Blick auf die spürbaren Folgen des Klimawandels ist es wichtig, Altstädte klimagerecht und zukunftssicher anzupassen, die Lebensqualität im Zentrum der Stadt zu erhalten und eine zeitgemäße Nutzung zu ermöglichen. Die modernen Anforderungen an die Umgestaltung von Altstädten können jedoch zu Konflikten mit den Interessen des Denkmalschutzes führen, historische Gebäude möglichst in ihrer originalen Form zu bewahren. Naturbasierte Lösungen, wie die Gebäudebegrünung, gelten als wirkungsvolle Maßnahmen zur Klimaanpassung. Der große Vorteil von Dach- und Fassadenbegrünung liegt im geringen Freiflächenbedarf, wodurch auch dicht bebaute Gebiete begrünt werden können. Die Dach- und Fassadenbegrünung gewinnen bundesweit an Bedeutung, denn sie bilden einen Mehrfachnutzen für die Stadt. Als einschränkender Faktor für mehr Begrünung an bestehenden Gebäuden wird bislang der Denkmalschutz betrachtet sowie Vorbehalte und Unsicherheiten, alte Gebäude und Bauwerke zu begrünen. Darunter zählt beispielsweise die schädigende Wirkung der Begrünung auf die Bausubstanz sowie Unsicherheit bei der Pflege und Wartung bereits bestehender Gebäudebegrünungen. Dabei können die häufig zentral gelegenen historischen Gebäude als kühle Rückzugsorte in den Städten ausgebildet werden und durch eine Begrünung als wichtige urbane Trittsteinbiotope dienen. Während für den Umgang mit erneuerbaren Energien im Denkmalbestand bereits Praxishinweise erarbeitet wurden, existiert bislang kein Leitfaden für die Denkmalpflege zum fachgerechten Umgang mit Dach- und Fassadenbegrünungen. Jeder Fall wird individuell behandelt und ohne Entscheidungshilfe bewertet. Diese Wissenslücke gilt es zu schließen und darüber hinaus Schulungen für den Denkmalschutzbereich anzubieten, um über die Möglichkeiten und Chancen von Gebäudebegrünung zu informieren.
This dataset comprises dissolved organic matter (DOM) composition from axenic and xenic cultures of Thalassiosira gravida that were cultivated at the Alfred-Wegener-Institute (Bremerhaven, Germany) in March, 2023. After a cell density of ~ 15.000 cells * mL-1 was reached, cultures were filtered through a 0.2 µm polycarbonate (PC) filter (Whatman) that was cleaned by soaking in 10 % hydrochloric acid (HCl, Merck suprapure) for at least 12 h and subsequently rinsing with ultrapure water (Merck Millipore MilliQ). Cultures were grown under two temperatures (9 °C, 13.5 °C) and two photoperiods (16:8 h, 24:0 h light:dark). 2 mL of the sample were filtered through a 0.2 µm regenerated cellulose (RC)-membrane syringe filter (Sartorius) after defrosting. Molecular composition data were acquired with Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) coupled to a reversed-phase liquid chromatography (RPLC) with negative electrospray ionization (ESI) according to Lechtenfeld et al., 2024 (doi: 10.1021/acs.est.3c07219). Measurements were performed on on a solariX XR, Bruker Daltonics, Billerica, U.S.A. at the Helmholtz Centre for Environmental Research (UFZ; Leipzig, Germany). Molecular formulas were assigned and filtered using UltraMassExplorer (Leefmann et., 2019; doi: 10.1002/rcm.8315). If the Total ion chromatogram (TIC) was much higher and/or different in certain retention time windows compared to other samples of the same treatment, the sample was excluded from the dataset. The aim of this study was to investigate responses of algal extracellular release and bacterial DOM transformation to marine heatwave-like conditions.
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.
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