This dataset contains ESRI shapefiles of mapped glacial landforms, i.e., initial cirques, cirques, moraines, and moraine crests in the region formerly occupied by the former Haslach glacier in the southern Black Forest (48° N, 8° E WGS 1984), south-west Germany. The last glaciation maximum ice extent of the former Haslach glacier, inferred from ice-marginal moraines, is also provided. Geomorphological mapping was undertaken for the selection of suitable sites for beryllium-10 surface exposure dating of moraine-boulder surfaces for the establishment of a regional glacier chronology. The mapping of glacial landforms in the region formerly occupied by the former Haslach glacier in the southern Black Forest involved the interpretation of derivatives of the high-resolution DGM1 digital elevation model (xy-resolution: 1 m) of the State Agency for Geoinformation and Land Development (LGL) of the state of Baden-Württemberg, freely available at: https://opengeodata.lgl-bw.de/#/(sidenav:product/3) (last access: 6 February 2025), coupled with extensive field campaigns in 2020-2022 CE. To achieve the greatest possible accuracy during the mapping of glacial landforms, exposures were inspected, if available. The shapefiles can be opened with open-source geographic information system software. The coordinate reference system of the shapefiles is EPSG 25832: ETRS89 / UTM Zone 32N (https://epsg.io/25832, last access: 6 February 2025).
Der Kartendienst (WMS-Gruppe) stellt die Geodaten aus dem Landschaftsprogramm Saarland die Themenkarte Wald und Landwirtschaft dar.:Im Landschaftsprogramm werden Räume zur Entwicklung von Auen-/Bruchwäldern bzw. Gewässerbegleitenden Erlen-/Eschenwäldern über ein Symbol dargestellt. Die Auswahl der Räume erfolgt auf den potentiellen Standorten dieser Waldgesellschaften unter Berücksichtigung der aktuellen Ausprägung der vorkommenden Lebensgemeinschaften mit ihren Pflanzen- und Tierarten und möglicher Konflikte mit bestehenden Nutzungen. Diese Räume müssen im Rahmen von konkreten Projekten zur Waldentwicklung näher untersucht und flächenmäßig konkretisiert werden. s. Landschaftsprogramm Saarland, Kapitel 9.9 und 6.5.4
<p>The present dataset from Germany is encompassed in the European Biodiversa BioRodDis project (Managing BIOdiversity in forests and urban green spaces: Dilution and amplification effects on RODent microbiomes and rodent-borne DISeases. Project coordinator: Nathalie Charbonnel, Senior researcher (DR2, INRAE), nathalie.charbonnel@inrae.fr - https://www6.inrae.fr/biodiversa-bioroddis). The project comes with the purpose to explore on a large scale the relationship between biodiversity of rodents, rodent-borne diseases dynamics and differences over time in a changing climate and it includes data of small terrestrial mammals from temperate forests and urban parks from the following countries: Belgium, France, Germany, Ireland and Poland. The present dataset includes records of small mammals (Rodentia) occurrences trapped in urbanised and forested areas in northeast Germany in the district of Potsdam (Brandenburg). Samplings and data collection took place throughout three years and during a total of four seasons: winter 2020, spring 2021, autumn 2021 and spring 2022. The number of sampling sites varied between 2 and 4 per seasons, with two main sites (Germany EastA and Germany EastB) being permanent in each sampling season. These variations are mainly due to the impact of SARS-CoV-2 pandemic regulations (2020, 2021) on the organisation and the execution of fieldwork and to the exclusion subsequently of forested sites with very low density of animals (≤10 individuals: Germany EastC, Germany EastB). The two main sampling sites represent different levels of anthropisation. The site Germany EastA is around the Botanical Garden belonging to the University of Potsdam with a mixture of sealed and wooded areas and a constant human presence while the site Germany EastB is a forested sub-urbanised area outside of the city composed by mixed coniferous forests, meadows, crossed by a main road and with occasional human presence (hunters, foresters). All animals were live captured (as in Schirmer et al., 2019) using a combination of Ugglan and Longworth traps for a total of 100-150 traps, depending on site and year. Traps were placed in 4 to 6 lines with 25m distance, and each line was composed by a total of 25 traps placed with 10m distance from each other. Fieldwork actions generally started with 1-4 days of pre-baiting followed by 1-10 days of trapping, according to efficiency of trapping and subprojects included. The sites Germany EastC and Germany EastD were excluded from the last two seasons because of very low trapping success during the previous seasons. All the traps were controlled daily during early morning hours and were activated again in the evening, with animals spending not more than eight hours in the trap. Baiting mixture consisted of oat flakes and apples and all traps were equipped with insulating material, like hay or wood wool. Taxonomical identification was determined in the field at species level according to morphology and previously recorded species occurrences in the sampling area (Dolch, 1995). Molecular identification of Apodemus flavicollis and Microtus individuals that were subsequently dissected was performed by the CBGP (France) using CO1 sequencing for Microtus species following Pagès et al., 2010, and DNA fingerprinting (AP-PCR) for Apodemus species (Bugarski-Stanojević et al., 2013). Dissections and body measurements were performed following the protocols described in Herbreteau et al., 2011. At the end of all seasons, a total of 620 occurrences of rodents was recorded, belonging to two main families (Muridae, Cricetidae) and four different species (Apodemus flavicollis, Apodemus agrarius, Myodes glareolus and Microtus arvalis). Additionally, for a subset of individuals (n=264), body measurements like weight, body length, head width, tail length and hind foot length as well as sexual maturity data were recorded. Animals were captured in accordance with the applicable international and institutional guidelines for the use of animals in research. The trapping and collection of rodents was performed under the permission of “Landesamt für Arbeitsschutz, Verbraucherschutz und Gesundheit Brandenburg (LAVG)“ (no. 2347-A-16-1-2020 for procedure, LUGV_RW7-4744/41+5#243052/2015 and N1 0424 for trapping) and “Landesamt für Umwelt Brandenburg (LfU)” (no. LFU-N1-4744/97+17#194297/2020, for sites and species exemptions). This project was funded through the 2018-2019 BiodivERsA joint call for research proposals, under the BiodivERsA3 ERA-Net COFUND programme, and coordinated by the German Science Foundation DFG (Germany). Citations: 1) Bugarski-Stanojević, V., Blagojević, J., Adnađević, T., Jovanović, V., & Vujošević, M. (2013). Identification of the sibling species Apodemus sylvaticus and Apodemus flavicollis (Rodentia, Muridae)—Comparison of molecular methods. Zoologischer Anzeiger - A Journal of Comparative Zoology, 252(4), 579–587. https://doi.org/10.1016/j.jcz.2012.11.004 2) Dolch, D. (1995). Naturschutz und Landschaftspflege in Brandenburg. 97. 3) Herbreteau, V., Jittapalapong, S., Rerkamnuaychoke, W., Chaval, Y., Cosson, J.-F., & Morand, S. (2011). Protocols for field and laboratory rodent studies. 56. 4) Pagès, M., Chaval, Y., Herbreteau, V., Waengsothorn, S., Cosson, J.-F., Hugot, J.-P., Morand, S., & Michaux, J. (2010). Revisiting the taxonomy of the Rattini tribe: A phylogeny-based delimitation of species boundaries. BMC Evolutionary Biology, 10(1), 184. https://doi.org/10.1186/1471-2148-10-184 5) Schirmer, A., Herde, A., Eccard, J. A., & Dammhahn, M. (2019). Individuals in space: Personality-dependent space use, movement and microhabitat use facilitate individual spatial niche specialization. Oecologia, 189(3), 647–660. https://doi.org/10.1007/s00442-019-04365-5</p>
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".
The CO2 storage potential of the Middle Buntsandstein Subgroup within the Exclusive Economic Zone (EEZ) of the German North Sea was analysed within the framework of the GEOSTOR-Project. A total of 71 potential storage sites were mapped based on existing 3D models, seismic and well data. Static CO2 capacities were calculated for each structure using Monte Carlo simulations with 10,000 iterations to account for uncertainties. All potential reservoirs were evaluated based on their static capacity, burial depth, top seal integrity and trap type. Analysis identified 38 potential storage sites with burial depths between 800 m and 4500 m, reservoir capacities (P50) above 5 Mt CO2 and suitable sealing units. The best storage conditions are expected on the West Schleswig Block where salt-controlled anticlines with moderate burial depths, large reservoir capacities and limited lateral flow barriers are the dominant trap types. Relatively poor storage conditions can be anticipated for small (P50 <5 Mt CO2), deeply buried (> 4500 m) and structurally complex potential storage sites in the Horn and Central Graben. For more detailed information on the methodology and findings, please refer to the full publication: Fuhrmann, A., Knopf, S., Thöle, H., Kästner, F., Ahlrichs, N., Stück, H. L., Schlieder-Kowitz, A. und Kuhlmann, G. (2024) CO2 storage potential of the Middle Buntsandstein Subgroup - German sector of the North Sea. Open Access International Journal of Greenhouse Gas Control, 136 . Art.Nr. 104175. DOI 10.1016/j.ijggc.2024.104175
The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery. Starting backwards from the year 2015 - for which the WSF2015 is used as a reference - settlement and non-settlement training samples for the given target year t are iteratively extracted by applying morphological filtering to the settlement mask derived for the year t+1, as well as excluding potentially mislabeled samples by adaptively thresholding the temporal mean NDBI, MNDWI and NDVI. Finally, binary Random Forest classification in performed. To quantitatively assess the high accuracy and reliability of the dataset, an extensive campaign based on crowdsourcing photointerpretation of very high-resolution airborne and satellite historical imagery has been performed with the support of Google. In particular, for the years 1990, 1995, 2000, 2005, 2010 and 2015, ~200K reference cells of 30x30m size distributed over 100 sites around the world have been labelled, hence summing up to overall ~1.2M validation samples. It is worth noting that past Landsat-5/7 availability considerably varies across the world and over time. Independently from the implemented approach, this might then result in a lower quality of the final product where few/no scenes have been collected. Accordingly, to provide the users with a suitable and intuitive measure that accounts for the goodness of the Landsat imagery, we conceived the Input Data Consistency (IDC) score, which ranges from 6 to 1 with: 6) very good; 5) good; 4) fair; 3) moderate; 2) low; 1) very low. The IDC score is available on a yearly basis between 1985 and 2015 and supports a proper interpretation of the WSF evolution product. The WSF evolution and IDC score datasets are organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2x2 degree size (~222x222km) on the ground. WSF evolution values range between 1985 and 2015 corresponding to the estimated year of settlement detection, whereas 0 is no data. A comprehensive publication with all technical details and accuracy figures is currently being finalized. For the time being, please refer to Marconcini et al,. 2021.
Der INSPIRE Datensatz Schutzgebiete (PS) Hamburg setzt sich aus den Inhalten folgender Datensätze zusammen: Schutzgebietskataster Hamburg Natur- und Landschaftsschutzgebiete, Naturdenkmale; Verordnungen; EG-Vogelschutz- und FFH-Gebiete (Natura 2000), NPHW, Biosphärenreservat, Ramsar- Gebiete. Denkmalkartierung Hamburg In der Denkmalkartierung sind folgende Kategorien (Ebenen, Layer) enthalten: - Denkmalobjekte (symbolhaft): z.B. Statuen, Brunnen, Denkmalanlagen ohne klare Ausdehnung - Grenzsteine: historische Grenzsteine und Grenzmarkierungen - Baudenkmale: z.B. Gebäude, Brücken, bauliche Anlagen - Gewässer: z.B. Hafenbecken, Kanäle, Schleusen, Teiche in Parks und Gärten - Gartendenkmale: z.B. öffentliche Park- und Gartenanlagen, historische Friedhöfe - Ensembles: mindestens aus zwei Objekten bestehend Bodendenkmäler Hamburg Kartierung bekannter archäologischer Schutzgebiete - Denkmäler/Bodendenkmäler - der Freien und Hansestadt Hamburg nach dem Hamburgischen Denkmalschutzgesetz vom 5. April 2013. Auskünfte zu den fachlichen Inhalten können nur die Ansprechparten der Originaldaten geben (siehe Verweise).
Since the end of the 1980ies the geological, areal and production data of operating mining sites have been collected systematically by LGRB. The periodic update of this information is carried out every four or five years. Main reasons are 1) the preparation of the periodic follow-up of the 12 regional development plans, 2) the work on the near-surface mineral raw material maps published by LGRB, and 3) the periodical editing of the state report for near-surface mineral raw materials published by LGRB at the start of each new election period. The geological data include a detailed documentation of the thickness, petrography and quality of mined rock(s) and the overburden as well as geochemical data gained from rock samples. The areal data refer both to the permitted mining area (zones of recultivation, work and expansion) and to possible areas for the mine expansion (the latter are confidential). Due to the quick spatiotemporal variability of these data, here all mining sites are shown as point data. The confidential annual production data are the basis for the periodic raw material report. In addition, another data are collected, e.g. for the mining permission, the delivery area and the subsequent land use. All these data are stored in the mining site database of the LGRB (Rohstoffgewinnungs-stellendatenbank = RGDB). This one comprises also the data for abandoned mining sites and mines. In total, actual (2021) about 14.000 data records are stored. The name of each mining site (e.g. RG 6826-3) consists of three parts. RG is the abbreviation for "Rohstoffgewinnungsstelle". the following four-digit number means the number of the relevant topographic map 1 : 25.000. The last number means the serial number of the mining site; serial numbers 1-99 mark operating mining sites gathered since the end of the 1980ies ( (today partially already closed) , such > 100 mark abandoned mining sites collected before 1980 and such > 300 mark data of mining sites and mines collected in the course of actual raw material mapping. The mintell4eu data set comprises all mining sites with serial numbers 1-99. In addition, the most important abandoned mines of former or probably still ongoing economic importance.
Ready-to-use version of the Eurasian Modern Pollen Database version 2 (EMPD2; Davis et al., 2020; Chevalier et al., 2019) that includes 90 taxa and 7634 modern pollen samples with pollen sums (excluding Pinus) higher or equal to 100 pollen grains (Tables 1 to 6). Table 7 contains 394 additional sites with pollen sums less than 100 pollen grains when excluding Pinus but higher or equal to 100 pollen grains when Pinus is included. Users can merge Tables 1 and 7 (8028 modern pollen samples) if they consider pollen sums (including Pinus) equal or higher than 100 pollen grains sufficient for accurate reconstructions. This ready-to-use version of the EMPD2 was initially built to do paleoclimatic reconstructions for Southern Europe. For users willing to do paleoclimate reconstructions in regions that may need to re-include some of the taxa that were removed, the intermediate version containing all the counts for the 840 initial taxa and the first grouping to 192 taxa is also available as Table 8.
Monthly, seasonal and annual mixed layer depth (MLD) values at the 1968 sites of the modern dinocyst database by de Vernal et al. (2020). The MLD values were extracted from the World Ocean Atlas 2018 (WOA18) objectively analyzed mean field of Argo floats data of 2005-2017 using a density threshold of 0.125 kg/m3 with reference to 10 m depth. In order to get an MLD value that corresponds to each site, the MLD climatology products were interpolated to the previously published 1968 sites.
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