Der verstärkte Ausbau erneuerbarer Energien ist erklärtes Ziel der Bundesregierung. Das Energiekonzept von 2010 formuliert hierzu Ausbauziele, die den Nutzungsdruck auf die verfügbaren Flächen deutlich erhöhen. Um Konflikte mit anderen Raumnutzungen zu vermeiden, ist es erforderlich, die regionalen Flächenpotenziale für die Nutzung erneuerbarer Energien zu kennen. Nur so kann jeder Raum entsprechend seiner Möglichkeiten optimal genutzt werden. Gegenstand und Zielsetzung: Ziel des Forschungsvorhabens ist die Entwicklung, Erprobung und Anwendung eines praxisgerechten Berechnungsmodells zur Abschätzung der regionalen Flächenpotenziale der verschiedenen EE-Sparten. Hintergrund ist dabei nicht, Vorgaben bezüglich umsetzbarer Flächenpotenziale für Bundesländer oder Regionen zu generieren. Es soll vielmehr ein Beitrag zur Methodenentwicklung und -harmonisierung geleistet werden, der eine transparente Zieldiskussion ermöglicht, die es erlaubt, die raum- und umweltverträgliche Umsetzbarkeit von Beginn an adäquat in den Entscheidungsprozess einzubeziehen. Das Modell soll die Berechnung raum- und umweltverträglicher EE-Flächenpotenziale auf Bundes-, Landes- und regionaler Ebene ermöglichen und hinreichende Einzelfallgerechtigkeit aufweisen. Die Ergebnisse der Analyse sollen die Akteure in die Lage versetzen, Handlungsempfehlungen für den weiteren Ausbau EE formulieren zu können sowie bestehende Zielsetzungen auf ihre Raum- und Umweltverträglichkeit zu prüfen. Dazu soll den Akteuren eine Methodik angeboten werden, die - im Gegensatz zu bestehenden Analyseansätzen - Raumansprüche und Raumwirkungen der einzelnen EE-Sparten in Abhängigkeit von den naturräumlichen Potenzialen sowie von den politischen, rechtlichen und insbesondere planerischen, technischen und wirtschaftlichen Rahmenbedingungen berücksichtigt. Darüber hinaus wird eine flexible Anpassung des Kriterienkatalogs vor dem Hintergrund der Planungsebene sowie der regionalen Eigenschaften ermöglicht. Der Anwender soll mit dem bereitgestellten Grundgerüst der Methodik und mit den Erläuterungen zur ebenenspezifischen Anpassung eigene, regionsspezifische Auswertungen unter Verwendung eigener Restriktionskriterien durchführen können. Das Vorgehen wird anhand der Fallstudien verdeutlicht. Im Ergebnis des Vorhabens werden ein standartisierter Datenkatalog und Hinweise auf weitere, ggf. regional verfügbare Datensätze erarbeitet.
This dataset includes a road traffic noise indicator mapped according to the European Noise Directive (2002/49/EC). The indicator, Lden (Day-Evening-Night Level), describes the equivalent sound pressure level of a 24-hour period, with separate values for daytime, evening, and nighttime periods and is commonly used in environmental noise assessments to evaluate the impact of noise sources on human health and well-being. The input data, simulated noise levels provided as shapefiles by the federal states of Germany, were cleansed and harmonized prior rasterizing them to 10 x 10m.
The KOR250 (INSPIRE) in the scale of 1:250,000 shows occurrences and deposits of mineral resources in Germany, which lie close to the Earth’s surface, i.e. can be mined in open-pits, quarries or near-surface mines. These mineral resources include industrial minerals, aggregates, peat, lignite, oil shales, and natural brines. The map is derived from the KOR250, the digital successor of the map series KOR200 „Map of Near-Surface Deposits of the Federal Republic of Germany 1:200,000”, which has been published since 1984. The KOR200 and KOR250 have been published by the Federal Institute for Geosciences and Natural Resources together with the State Geological Surveys of the federal states on behalf of the Federal Ministry for Economic Affairs and Energy. Primary purpose of the KOR250 is to display Germany’s potential of domestic raw materials in a comparable way. The explanations given in the printed booklets accompanying the KOR200 are not available in the digital KOR250. In the KOR250 besides the defined deposits and differently coloured areas of raw materials, "active mines" (= operations) at time of publication or "focal points of several active mines" are marked with one symbol each. These mines are not included in the KOR250 (INSPIRE) as often the headquarters of the mining company and not the mining site itself is displayed as well as in many regions the dataset is outdated. As the map sheets of the KOR200 have been generated over more than three decades the timeliness of data is extremely different. For more detail, the current large-scale raw material maps of the Federal State Geological Surveys should always be consulted. The point data displayed in KOR250 (INSPIRE) indicate very small, but worth mentioning prospects of certain raw materials. According to the Data Specification on Mineral Resources (D2.8.III.21) the content of the map is stored in two INSPIRE-compliant GML files: KOR250_EarthResource_polygon.gml comprises the mineral resources as polygons. KOR250_EarthResource_point.gml comprises the mineral resources as points. The GML files together with a Readme.txt file are provided in ZIP format (KOR250-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. Notes: It should be noted that according to the INSPIRE commodity code list, most magmatites and metamorphites were assigned to the two values "granite" and "basalt". From a geological point of view and with regard to its origin, this assignment is often misleading. For more information on the outcropping rock of a specific raw material occurrence, the German name from the original KOR250 was mapped to the attribute name of the class GeologicFeature. Link KOR200: https://www.bgr.bund.de/EN/Themen/Min_rohstoffe/Projekte/Rohstoffverfuegbarkeit_laufend_en/KOR_200_en.html
This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2018. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying (in the meantime slightly updated) methodology can be found in Asam et al. 2022.
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 September 2024. 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 July 2024 through September 2024 from the annual and overall statistics and summarized the respective FCCL as additional attribute. The dataset is a fully reprocessed continuation of the assessment in Thonfeld et al. (2022).
The GCEF (Global Change Experimental Facility) is a large field experiment in Bad Lauchstädt (federal state of Saxony-Anhalt, Central Germany/Germany) for the investigation of the consequences of climate change for ecosystem processes under different land use options. The GCEF consists of 50 plots with a size of 16 m x 24 m. The experiment started in 2014 and is planned to be conducted for at least 15 years. The 50 plots are arranged in 10 blocks (5 plots per block). Half of the blocks are subjected to a climate change treatment. For this, all plots are equipped with a large steel constructions (5 m heigth), mobile shelters and side panels as well as an irrigation system. Each block is equipped with 1 rain sensor (in the grazing plot), thus the values from each block have to be assigned to all the plots within the block. Precipitation data including artificial irrigation in GCEF since 2014. Measurements have been made in the 10 mainplots of the experiment as sum over 15 minutes with tipping scales. The data are sampled to monitor the manipulation of precipitation according to the experimental plan and as basis data of climatic conditions on the plots. For details of the experiment see Schädler et al. (2019), Ecosphere (https://doi.org/10.1002/ecs2.2635) and the document with the detailed experiment design.
Die im 10 jährigen Turnus durchzuführende Bundeswaldinventur ist ein auf dem Bundeswaldgesetz beruhender Auftrag. Die 4. BWI ist in den Jahren 2021 und 2022 durchzuführen. Eine entsprechende Durchführungsverordnung wird in 2016/17 erarbeitet und auf Bund-Länder-Ebene abgestimmt. In einem weiteren Schritt (2018/19) werden gegebenenfalls vorzunehmende Modifikationen der Informationsgewinnung und die Option landesspezifischer Sonderhebungen abgestimmt und in einer Aufnahmeanweisung festgelegt. Die Felddatenerhebung ist vorzubereiten und umfasst folgende Punkte (2020): ggf. Beschaffung von Spezialgeräten (voraussichtlich modernes GNSS), Ausschreibung der Felddatenerhebung durch freiberufliche Spezialisten; Schulung der Aufnahmetrupps. Die Durchführung der Felddatenerhebungen in den Jahren 2021 und 22 umfasst Qualitätssicherung (Kontrollaufnahmen) und Datenmanagement (Prüfung, Korrekturen). Die Auswertungsphase (2023-24) beinhaltet in Kooperation mit dem bundesweit zuständigen Thünen-Institut insbesondere die Interpretation der Ergebnisse; darüber hinaus erfolgen landespezifische Auswertungen und Analysen. Diese werden in Abstimmung mit dem MLR in einer öffentlichen Veranstaltung vorgestellt (BWI-Kolloquium) Die Daten werden für weitere Forschungsvorhaben in einer FVA-Datenbank vorgehalten.
Which salt formations are suitable for storing hydrogen or compressed air? In the InSpEE-DS research project, scientists developed requirements and criteria for the assessment of suitable sites even if their exploration is still at an early stage and there is little knowledge of the salinaries’ structures. Scientists at DEEP.KBB GmbH in Hanover, worked together with their project partners at BGR and the Leibniz University Hanover, Institute for Geotechnics, to develop the planning basis for the site selection and for the construction of storage caverns in flat layered salt and multiple or double saliniferous formations. Such caverns could store renewable energy in the form of hydrogen or compressed air. While the previous project InSpEE was limited to salt formations of great thickness in Northern Germany, salt horizons of different ages have now been examined all over Germany. To estimate the potential, depth contour maps of the top and the base as well as thickness maps of the respective stratigraphic units were developed. Due to the present INSPIRE geological data model, it was necessary, in contrast to the original dataset, to classify the boundary lines of the potential storage areas in the Zechstein base and thickness layers, whereby the classification of these lines was taken from the top Zechstein layer. Consequently, the boundary element Depth criterion 2000 m (Teufe-Kriterium 2000 m) corresponds on each level to the 2000 m depth of Top Zechstein. However, the boundary of national borders and the boundary of the data basis could not be implemented in the data model and are therefore not included in the dataset. Information on compressed air and hydrogen storage potential is given for the identified areas and for the individual federal states. According to the Data Specification on Geology (D2.8.II.4_v3.0) the content of InSpEE-DS (INSPIRE) is stored in 18 INSPIRE-compliant GML files: InSpEE_DS_GeologicUnit_Isopachs_Zechstein.gml contains the Zechstein isopachs. InSpEE_DS_GeologicUnit_Isobaths_Top_Zechstein.gml and InSpEE_DS_GeologicUnit_Isobaths_Basis_Zechstein.gml contain the isobaths of the top and basis of Zechstein. The three files InSpEE_DS_GeologicStructure_ThicknessMap_Zechstein, InSpEE_DS_GeologicStructure_Top_Zechstein and InSpEE_DS_GeologicStructure_Basis_Zechstein represent the faults of the Zechstein body as well as at the top and at the basis of the Zechstein body. InSpEE_DS_GeologicUnit_Boundary_element_Potential_areas_Zechstein.gml contains the boundary elments of the potential areas at the top and the basis of Zechstein as well as of the Zechstein body. The three files InSpEE_DS_GeologicUnit_Uncertainty_areas_ThicknessMap_Zechstein.gml, InSpEE_DS_GeologicUnit_Uncertainty_areas_Top_Zechstein.gml, InSpEE_DS_GeologicUnit_Uncertainty_areas_Basis_Zechstein.gml represent the uncertainty areas of the Zechstein body as well as at the top and at the basis of the Zechstein body. InSpEE_DS_GeologicUnit_Potentially_usable_storage_areas_Storage_potential_in_the_federal_states.gml comprises the areas with storage potential for renewable energy in the form of hydrogen and compressed air. The six files InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Malm.gml, InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Keuper.gml, InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Muschelkalk.gml, InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Roet.gml, InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Zechstein.gml and InSpEE_DS_GeologicUnit_Salt_distribution_in_Germany_Rotliegend.gml represent the salt distribution of the respective stratigraphic unit. InSpEE_DS_GeologicUnit_General_salt_distribution.gml represents the general salt distribution in Germany. This geographic information is product of a BMWi-funded research project "InSpEE-DS" running from the year 2015 to 2019. The acronym stands for "Information system salt: planning basis, selection criteria and estimation of the potential for the construction of salt caverns for the storage of renewable energies (hydrogen and compressed air) - double saline and flat salt layers".
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