The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2022 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data, radar data from Sentinel-1, and a digital elevation model. The input features explicitly incorporate phenological information to capture seasonal vegetation dynamics relevant for species discrimination. A total of over 80,000 training and test samples were compiled from publicly accessible sources, including urban tree inventories, Google Earth Pro, Google Street View, and field observations. The final classification was generated using an XGBoost machine learning algorithm. The Tree Species Germany product achieves an overall F1-score of 0.89. For the dominant species pine, spruce, beech, and oak, class-wise F1-scores range from 0.76 to 0.98, while F1-scores for other widespread species such as birch, alder, larch, Douglas fir, and fir range from 0.88 to 0.96. The product provides a consistent, high-resolution, and up-to-date representation of tree species distribution across Germany. Its transferable, cost-efficient, and repeatable methodology enables reliable large-scale forest monitoring and offers a valuable basis for assessing spatial patterns and temporal changes in forest composition in the context of ongoing climatic and environmental dynamics.
A detailed attributed point feature shapefile of 6059 pingo locations in a 3.5 × 10⁶ km² region of northern Asia was manually assembled from 1:200 000 scale Russian topographic maps. These medium-scale maps are based on detailed mapping efforts at 1:50 000 and 1:100 000 scales, which in turn are derived from aerial photography acquired in the 1970-1980s (Soviet Military Topographic Survey or Voenno-Topograficheskoe Upravle-nie General'nogo Shtaba, VTU GSh). A first order analysis of pingo distribution was carried out with respect to permafrost, landscape characteristics, surface geology, hydrology, climate, and elevation datasets using a Geographic Information System. The study area comprises the North Asian lowland regions of North, Northeast, Far East and Central Siberia and adjacent mountain ranges from 60.0° N to 76.3° N latitude and 60.0° E to 180.0° W longitude. The geographic re-gions covered in this study are the northern part of the West Siberian Lowlands including the Yamal and Gydan peninsulas, Taymyr Peninsula, Putorana Plateau, Khatanga-Anabar-Olenek Lowlands, Lena River Delta, Lena River Valley, central Yakutian Lowlands around Yakutsk; Yana-Indigirka-Kolyma Lowlands, New Siberian Islands, and the far east Siberian Chukotka region.
Die dargestellten Gebiete bilden die Schwerpunktvorkommen der Zielarten des Wiesenvogelschutzprogramms (Uferschnepfe, Kiebitz, Brachvogel, Rotschenkel, Bekassine, Austernfischer, Braunkehlchen und Wachtelkönig) auf landwirtschaftlich genutzten Flächen ab. Vor dem Hintergrund der Schirmartenfunktion der ausgewählten Arten bilden sie die prioritäre Kulisse für die Umsetzung des Wiesenvogelschutzprogramms im Rahmen des Niedersächsischen Wegs. Es sind zum einen die für die ausgewählten Wiesenvogelarten wichtigen EU-Vogelschutzgebiete (EU-VSG) und zum anderen Gebiete außerhalb der EU-VSG mit noch signifikanten Brutvorkommen abgebildet. Es handelt sich dabei um keine vollständige Verbreitungskarte, da nicht sämtliche Vorkommen aller Zielarten abgedeckt sind. Brutvorkommen auf Flächen außerhalb landwirtschaftlicher Nutzung (Moore, Heiden, Salzwiesen, Truppenübungsplätze) sind hier nicht dargestellt. Die Identifizierung der Landnutzung erfolgte auf Basis der Daten aus ATKIS-DLM (2017) sowie auf Basis der landwirtschaftlichen Feldblöcke (SLA, 2021).Die Darstellung differenziert nach Besiedlung durch die Zielarten (Brutvorkommen Limikolen - Brutvorkommen Braunkehlchen - Vorkommen von Limikolen und Braunkehlchen). Die landesweit wichtigsten Brutvorkommen der Zielart Wachtelkönig werden dadurch mit abgedeckt und werden nicht gesondert dargestellt.
The SuK-Nord (INSPIRE) shows the geological distribution of aggregates (sand and gravel) in Northern Germany, especially north of the southernmost maximum of the Scandinavian inland ice sheet (Saalian and Elsterian glaciation). According to the Data Specifications on Mineral Resources (D2.8.III.21) and Geology (D2.8.II.4_v3.0) the content of the map is stored in two INSPIRE-compliant GML files: SuK_Nord_EarthResource_polygon.gml contains the distribution of aggreagtes (sand and gravel) as polygons. SuK_Nord_GeomorphologicFeature.gml contains the southernmost maximum of the Scandinavian inland ice sheet (Saalian and Elsterian glaciation) as lines. The GML files together with a Readme.txt file are provided in ZIP format (SuK_Nord-INSPIRE.zip). The Readme.text file (German/English) contains detailed information on the GML files content. Data transformation was proceeded by using the INSPIRE Solution Pack for FME according to the INSPIRE requirements.
Nicht-konventionelle KW (INSPIRE) presents the results of the NiKo project according to data specifications Energy Resources (D2.8.III.20) und Geology (D2.8.II.4_v3.0). NiKo stands for „unconventional hydrocarbons“, „Nicht-konventionelle Kohlenwasserstoffe“ in German. In the NiKo project the Federal Institute for Geosciences and Natural Resources (BGR) has investigated the potential resources for shale oil and shale gas in Germany. The study was published in 2016 as a report titled „Schieferöl und Schiefergas in Deutschland – Ressourcen und Umweltaspekte“ (available in German only). The colloquial terms shale oil and shale gas refer to oil and natural gas resources in sedimentary shale rock formations, with high organic matter content. In the study, seven formations were identified to have a shale oil and/or gas potential in Germany and their distribution has been mapped in small scale. For each of the formations the organic-rich facies distribution is provided and, if appropriate, the regional potential resource distribution: Fischschiefer (Oligocene), Blättertone (Barremium - Lower-Aptian), Wealden (Berriasium), Posidonienschiefer (Lower-Toarcium), Middel-Rhaetium (Oberkeuper), Permokarbon (Stefanium - Rotliegend) und Lower Carboniferous (Upper Alaunschiefer (Kulm-Facies) + Kohlenkalk-Facies). Corresponding to the overview maps in the report two GML-files for these layers are provided, omitting however the sub-category “possible potential regions”. Bituminous facies distribution (0-5000 m Tiefe) – data specification Geology: GeologicUnit.Distribution_of_bituminous_facies.gml Distribution shale oil and shale gas – potential resources (1000 - 5000 m Tiefe) – data specification Energy Resources: FossilFuelRessource_Potential_resource_regions.gml The distribution maps of the potential resources for shale oil and gas are based on geoscientific criteria. Further non-geoscientific limiting criteria, e.g. exclusion areas, have not been taken into account for the assessment. These assessments are based on appraisements of input parameters naturally with large uncertainties for the potential resources and their distribution in the deep underground. Based on the incipient exploration status of unconventionals in Germany, these resources are considered as undiscovered. The assessed shale oil and gas resources for Germany, represent the order of magnitude of potential resources. Reference: BGR 2016 - Schieferöl und Schieferöl in Deutschland- Potenziale und Umweltaspekte https://www.bgr.bund.de/DE/Themen/Energie/Downloads/Abschlussbericht_13MB_Schieferoelgaspotenzial_Deutschland_2016.html
The data set includes data of 800 herbarium specimens (and 295 duplicates) used for a taxonomic revision of Desplatsia Bocq. (Malvaceae s.l., subfamily Grewioideae, tribe Grewieae). Desplatsia is a genus of trees and shrubs found in tropical West and Central Africa and is characterised by subulately divided stipules, the absence of an androgynophore, stamens that are fused to a tube at the base, and large and distinctive fruits that are dispersed by elephants. In the taxonomic revision four species are recognized (D. subericarpa, D. chrysochlamys, D. dewevrei and D. mildbraedii) and 12 species names are placed into synonymy, two of which have been put into synonymy for the first time: D. floribunda and D. trillesiana. All four species are widely distributed and their conservation status is assessed as Least Concern (LC). A key to the species, full species descriptions, illustrations, a specimen citation list and distribution maps are provided. All herbarium specimens were seen and identified either at the herbaria or online. The revision is published in the European Journal of Taxonomy (Wellsow J., Hart M., Wilkie P. Harris, D.J. (2019). A taxonomic revision of the African genus Desplatsia Bocq. (Malvaceae - Grewioideae). European Journal of Taxonomy 584: 1-38. https://doi.org/10.5852/ejt.2019.584). Acknowledgement: We thank Naturalis and Botanic Garden Meise for sending specimen data. The time span is 1861 to 2019.
Nach Darstellung der Standortverhaeltnisse im Bereich der ca. 70 noch bestehenden Oberharzer Stauteiche - die zur Versorgung des Harzer Bergbaus mit Wasserkraft angelegt wurden - werden das Artengefuege und die Verbreitung ihrer Pflanzengesellschaften durch Tabellen bzw. Verbreitungskarten erlaeutert. Von einigen Teichen sind Vegetationskarten bzw. -transekte erarbeitet worden. Aspekte der Entwicklung des Teichgebietes bei Aenderung der Nutzung und Vorschlaege fuer die Erhaltung der auch aus kultur- und baugeschichtlicher Sicht wertvollen Anlagen runden die Arbeit ab.
Datensammlung zur Verbreitung bzw. Vorkommen epixyler und epipetrischer Flechten im Gebiet von Koeln und Umgebung. Langfristiges Ziel - Erstellung einer Verbreitungskarte dieser Flechten in Abhaengigkeit von Siedlungsdichte und Immissionsbelastung.
SWACI is a research project of DLR supported by the State Government of Mecklenburg-Vorpommern. Radio signals, transmitted by modern communication and navigation systems may be heavily disturbed by space weather hazards. Thus, severe temporal and spatial changes of the electron density in the ionosphere may significantly degrade the signal quality of various radio systems which even may lead to a complete loss of the signal. By providing specific space weather information, in particular now- and forecast of the ionospheric state, the accuracy and reliability of impacted communication and navigation systems shall be improved. According to the pioneer work of Sir E. Appleton the vertical structure of the terrestrial ionosphere may be divided into different layers (D, E, F1, F2) with different physical characteristics. The layers are primarily characterized by its height and peak electron density. The spatial plasma distribution is generated from actual TEC maps by applying a first version of the empirical electron density model NEDM-v1. In correspondence with the update rate of TEC maps the time resolution of the 3 D images is 5 minutes. For details see http://swaciweb.dlr.de/index.php?id=303&L=1 and http://presentations.copernicus.org/EGU2011-7324_presentation.pdf.
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