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This is a description of your Web Feature Server. The GeoServer is a full transactional Web Feature Server, you may wish to limit GeoServer to a Basic service level to prevent modificaiton of your geographic data.
KoeppenGeiger Mid-Holocene (geonode:mpi_esm_p_midholocene_r1i1p1_koeppen_geiger_classification_1)
This geospatial dataset, is a Koeppen-Geiger climate classification of the MPI-ESM-P Mid-Holocene (6k yBP) r1i1p1 model simulations according to the PMIP III 21k experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
Settlement areas of early Neolithic sites in the Rhineland (geonode:_13a_earlyneolithic_rhl_iso4km_wgs84)
So-called “settlement areas” derived from the site distribution map of the early Neolithic in the German Rhineland (Richter / Claßen 1997). The areas were delimited in a six steps protocol from several isolines of different site-densities (Hilpert et al. 2008; Zimmermann et al. 2004; Zimmermann et al. 2009). The distance value of the Largest Empty Circle (LEC) is 4 km. The data set was updated with data from the Cultural Heritage administration in Bonn. Access to high precision location of archaeological sites is limited due to protection reasons. Therefore, only settlement areas are chosen for Web publication. Ressources: J. Hilpert/ K.P. Wendt/A. Zimmermann, A Hierarchical Model of Scale Levels for Estimations of Population Densities. In: A. Posluschny/K. Lambers/I. Herzog (eds.), Layers of Perception. Proceedings of the 35th International Conference on Computer Applications and Quantitative Methods in Archaeology (CAA), Berlin, Germany, April 2–6, 2007. Kolloquien zur Vor- und Frühgeschichte, Vol. 10 (Bonn 2008): 252-256. J. RICHTER / E. Claßen 1997, Neolithikum. Geschichtlicher Atlas Rheinlande Beih. II/2 Karte 1: Alt- und Mittelneolithikum (Köln 1997) A. Zimmermann/J. Richter/T. Frank/K.P. Wendt, Landschaftsarchäologie II: Überlegungen zu Prinzipien einer Landschaftsarchäologie. Ber. RGK 85, 2004:37–95. A. Zimmermann/J. Hilpert/K.P. Wendt, Estimations of Population Density for Selected Periods between the Neolithic and AD 1800. Human Biology 2009: 357–380.
Alpine LGM Glaciation Extends (geonode:lgm_alpen)
LGM Glaciation extends in the european alpine region. From the Book website: http://booksite.elsevier.com/9780444534477/ The book Ehlers, Gibbard, Hughes: Quaternary Glaciations - Extent and Chronology Volume 15: A closer look presents an up-to-date, detailed overview of Quaternary glaciations all over the world, including the presentation of digital maps which can be used in a geographical information system (GIS) and all recent information on the impact global and climate change has on these glaciations and the impact on our Earth. The maps (Google Earth files) as well as related digital information are available on this companion website. With these larger scale digital base maps, the accuracy of the information is increased by a factor of 16. The difference is striking. Unfortunately, no single, uniform base map can be used at the 1:250,000 scale, therefore, in order to cope with the different levels of accuracy encountered, some of the layers of information have been grouped, so that only consistent information is shown together. All the spatial data are given in shapefiles to allow for easy import into other GIS systems. As not all readers will have ArcGIS at their disposal, in order to allow the non-GIS community a glimpse at the maps some are available as PDFs and also as KMZ files that can be read using Google Earth. Nevertheless the user must be aware of the fact that those files are only images, and their contents are restricted to what has been selected by the author. We hope that the current map set will be widely used, and that it will form the basis for further investigations.
BIOME 6000 - Version 4.2 shapefile (geonode:biome4_3)
Vegetation maps for the Mid-holocene and last Glacial Maximum The Palaeovegetation Mapping Project (generally known as BIOME 6000: Prentice and Webb, 1998) was inaugurated in 1994 with the aim of providing global maps describing the vegetation patterns at 6000±500 yr B.P. (on the radiocarbon time scale) and the last glacial maximum (defined as 18,000±1000 yr B.P. on the radiocarbon time scale, equivalent to 21,000 yr B.P. on the calendar time scale) for use by the modelling community. The BIOME 6000 project has used a standard methodology to map vegetation patterns using fossil pollen and plant-macrofossil data from individual sites. The taxa represented in the pollen or plant-macrofossil assemblages are first allocated to plant functional types (PFTs) on the basis of the life form, leaf form, phenology and bioclimatic tolerance of the plant species included within the taxon. Because of the lack of taxonomic resolution in pollen identification, some taxa can be classified into more than one PFT. Biomes (i.e. major vegetation types at a regional scale) are defined by combinations of PFTs, where these combinations usually include both characteristic and dominant groups. Some PFTs which are known to occur within a given biome are not included in the biome definition because they occur in too many biomes to provide discriminatory power. Once the taxon to PFT and PFT to biome classifications are made, the affinity of pollen or plant-macrofossil assemblages from individual sites for each biome is calculated. Each assemblage is allocated to the biome for which it has the highest affinity. In cases where the assemblage has equal affinity for more than one biome, which can occur when one biome is defined by a subset of the PFTs that characterise another biome, the assemblage is allocated to the biome defined by the subset. The published version of the BIOME 6000 database (Version 3: Prentice et al., 2000) was based on maps produced on a region by region basis over a number of years. Here, we have fused the information from the various regions and standardised the biome names. We recognise 40 biomes, using names that are broadly consistent with the BIOME4 equilibrium biogeography-biochemistry model (Kaplan et al., 2003). Since Version 3 of the BIOME 6000 database was released, there have been three new palaeovegetation mapping initiatives. Harrison et al. (2001) added a number of sites from the continental shelf east of China which date to the last glacial maximum. The Pan-Arctic Initiative (PAIN) extended the site coverage from the high-northern latitudes at both 6000 yr B.P. and the last glacial maximum (Bigelow et al., 2003). Pickett et al. (2004) extended the coverage to the SEAPAC (South East Asia and the Pacific) region at both 6000 yr B.P. and the last glacial maximum. These data sets are included in the current version of the BIOME 6000 data set (Version 4.2). BIOME 6000 Version 4.2 has records for 11166 modern sites, 1794 sites at 6000 yr B.P., and 318 sites at 18,000 yr B.P.
BIOME 6000 - Version 4.2: 06 ka BP (geonode:biome06ka)
Vegetation map for the Mid-holocene around 6 ka BP. The Palaeovegetation Mapping Project (generally known as BIOME 6000: Prentice and Webb, 1998) was inaugurated in 1994 with the aim of providing global maps describing the vegetation patterns at 6000±500 yr B.P. (on the radiocarbon time scale) and the last glacial maximum (defined as 18,000±1000 yr B.P. on the radiocarbon time scale, equivalent to 21,000 yr B.P. on the calendar time scale) for use by the modelling community. The BIOME 6000 project has used a standard methodology to map vegetation patterns using fossil pollen and plant-macrofossil data from individual sites. The taxa represented in the pollen or plant-macrofossil assemblages are first allocated to plant functional types (PFTs) on the basis of the life form, leaf form, phenology and bioclimatic tolerance of the plant species included within the taxon. Because of the lack of taxonomic resolution in pollen identification, some taxa can be classified into more than one PFT. Biomes (i.e. major vegetation types at a regional scale) are defined by combinations of PFTs, where these combinations usually include both characteristic and dominant groups. Some PFTs which are known to occur within a given biome are not included in the biome definition because they occur in too many biomes to provide discriminatory power. Once the taxon to PFT and PFT to biome classifications are made, the affinity of pollen or plant-macrofossil assemblages from individual sites for each biome is calculated. Each assemblage is allocated to the biome for which it has the highest affinity. In cases where the assemblage has equal affinity for more than one biome, which can occur when one biome is defined by a subset of the PFTs that characterise another biome, the assemblage is allocated to the biome defined by the subset. The published version of the BIOME 6000 database (Version 3: Prentice et al., 2000) was based on maps produced on a region by region basis over a number of years. Here, we have fused the information from the various regions and standardised the biome names. We recognise 40 biomes, using names that are broadly consistent with the BIOME4 equilibrium biogeography-biochemistry model (Kaplan et al., 2003). Since Version 3 of the BIOME 6000 database was released, there have been three new palaeovegetation mapping initiatives. Harrison et al. (2001) added a number of sites from the continental shelf east of China which date to the last glacial maximum. The Pan-Arctic Initiative (PAIN) extended the site coverage from the high-northern latitudes at both 6000 yr B.P. and the last glacial maximum (Bigelow et al., 2003). Pickett et al. (2004) extended the coverage to the SEAPAC (South East Asia and the Pacific) region at both 6000 yr B.P. and the last glacial maximum. These data sets are included in the current version of the BIOME 6000 data set (Version 4.2). BIOME 6000 Version 4.2 has records for 11166 modern sites, 1794 sites at 6000 yr B.P., and 318 sites at 18,000 yr B.P.
BIOME 6000 - Version 4.2: 18 ka BP (geonode:biome18ka)
Vegetation map for the late Last Glacial Maximum period around 18 ka BP. The Palaeovegetation Mapping Project (generally known as BIOME 6000: Prentice and Webb, 1998) was inaugurated in 1994 with the aim of providing global maps describing the vegetation patterns at 6000±500 yr B.P. (on the radiocarbon time scale) and the last glacial maximum (defined as 18,000±1000 yr B.P. on the radiocarbon time scale, equivalent to 21,000 yr B.P. on the calendar time scale) for use by the modelling community. The BIOME 6000 project has used a standard methodology to map vegetation patterns using fossil pollen and plant-macrofossil data from individual sites. The taxa represented in the pollen or plant-macrofossil assemblages are first allocated to plant functional types (PFTs) on the basis of the life form, leaf form, phenology and bioclimatic tolerance of the plant species included within the taxon. Because of the lack of taxonomic resolution in pollen identification, some taxa can be classified into more than one PFT. Biomes (i.e. major vegetation types at a regional scale) are defined by combinations of PFTs, where these combinations usually include both characteristic and dominant groups. Some PFTs which are known to occur within a given biome are not included in the biome definition because they occur in too many biomes to provide discriminatory power. Once the taxon to PFT and PFT to biome classifications are made, the affinity of pollen or plant-macrofossil assemblages from individual sites for each biome is calculated. Each assemblage is allocated to the biome for which it has the highest affinity. In cases where the assemblage has equal affinity for more than one biome, which can occur when one biome is defined by a subset of the PFTs that characterise another biome, the assemblage is allocated to the biome defined by the subset. The published version of the BIOME 6000 database (Version 3: Prentice et al., 2000) was based on maps produced on a region by region basis over a number of years. Here, we have fused the information from the various regions and standardised the biome names. We recognise 40 biomes, using names that are broadly consistent with the BIOME4 equilibrium biogeography-biochemistry model (Kaplan et al., 2003). Since Version 3 of the BIOME 6000 database was released, there have been three new palaeovegetation mapping initiatives. Harrison et al. (2001) added a number of sites from the continental shelf east of China which date to the last glacial maximum. The Pan-Arctic Initiative (PAIN) extended the site coverage from the high-northern latitudes at both 6000 yr B.P. and the last glacial maximum (Bigelow et al., 2003). Pickett et al. (2004) extended the coverage to the SEAPAC (South East Asia and the Pacific) region at both 6000 yr B.P. and the last glacial maximum. These data sets are included in the current version of the BIOME 6000 data set (Version 4.2). BIOME 6000 Version 4.2 has records for 11166 modern sites, 1794 sites at 6000 yr B.P., and 318 sites at 18,000 yr B.P.
BIOME 6000 - Version 4.2: Recent (00 ka) (geonode:biome00ka)
Global vegetation map for recent vegetation dispersal. The Palaeovegetation Mapping Project (generally known as BIOME 6000: Prentice and Webb, 1998) was inaugurated in 1994 with the aim of providing global maps describing the vegetation patterns at 6000±500 yr B.P. (on the radiocarbon time scale) and the last glacial maximum (defined as 18,000±1000 yr B.P. on the radiocarbon time scale, equivalent to 21,000 yr B.P. on the calendar time scale) for use by the modelling community. The BIOME 6000 project has used a standard methodology to map vegetation patterns using fossil pollen and plant-macrofossil data from individual sites. The taxa represented in the pollen or plant-macrofossil assemblages are first allocated to plant functional types (PFTs) on the basis of the life form, leaf form, phenology and bioclimatic tolerance of the plant species included within the taxon. Because of the lack of taxonomic resolution in pollen identification, some taxa can be classified into more than one PFT. Biomes (i.e. major vegetation types at a regional scale) are defined by combinations of PFTs, where these combinations usually include both characteristic and dominant groups. Some PFTs which are known to occur within a given biome are not included in the biome definition because they occur in too many biomes to provide discriminatory power. Once the taxon to PFT and PFT to biome classifications are made, the affinity of pollen or plant-macrofossil assemblages from individual sites for each biome is calculated. Each assemblage is allocated to the biome for which it has the highest affinity. In cases where the assemblage has equal affinity for more than one biome, which can occur when one biome is defined by a subset of the PFTs that characterise another biome, the assemblage is allocated to the biome defined by the subset. The published version of the BIOME 6000 database (Version 3: Prentice et al., 2000) was based on maps produced on a region by region basis over a number of years. Here, we have fused the information from the various regions and standardised the biome names. We recognise 40 biomes, using names that are broadly consistent with the BIOME4 equilibrium biogeography-biochemistry model (Kaplan et al., 2003). Since Version 3 of the BIOME 6000 database was released, there have been three new palaeovegetation mapping initiatives. Harrison et al. (2001) added a number of sites from the continental shelf east of China which date to the last glacial maximum. The Pan-Arctic Initiative (PAIN) extended the site coverage from the high-northern latitudes at both 6000 yr B.P. and the last glacial maximum (Bigelow et al., 2003). Pickett et al. (2004) extended the coverage to the SEAPAC (South East Asia and the Pacific) region at both 6000 yr B.P. and the last glacial maximum. These data sets are included in the current version of the BIOME 6000 data set (Version 4.2). BIOME 6000 Version 4.2 has records for 11166 modern sites, 1794 sites at 6000 yr B.P., and 318 sites at 18,000 yr B.P.
Early Neolithic sites in Central Europe (geonode:_13_earlyneolithic_ce_sites_wgs84)
Distribution of early Neolithic Bandkeramik sites (from 7500-6950 calBC) in Central Europe. Location of sites is derived by digitizing published maps (resources see below). The distribution map is used to estimate population densities within the Rhine-LUCIFS-Project (funded by the German Research Foundation), which is part of the PAGES framework. Ressources: J. Preuss (ed.), Das Neolithikum in Mitteleuropa: Kulturen, Wirtschaft, Umwelt vom 6. bis 3. Jahrtausend v.u.Z., Übersichten zum Stand der Forschung, Karte 1: Verbreitung neolithischer Kulturen in Mitteleuropa (Weissbach 1998).
Gibraltar and Alboran Sea LGM sea level change (GEBCO 2014) (geonode:gibraltaralboranlgm)
No abstract providedBased on the GEBCO 2014 dataset [1], the area between the 0m coastline of today and the -120m coastline of the Last Glacial Maximum (LGM), is derived and provided in this dataset. To achieve a more realistic and higher resoluting coastline, for producing larger scale maps, the gebco dataset was interpolated from the 30 arc seconds original resolution, to a 0,001 degree resoluting raster. [1] http://www.gebco.net/data_and_products/gridded_bathymetry_data/
Iberian Peninsula Drainage Lines (geonode:drainage_line)
Drainage lines of the iberian peninsula derived with ArcGIS from ASTER Global DEM dataset.
InaSAFE analysis result (geonode:sealevelchangelgm_2)
InaSAFE analysis result
InaSAFE analysis result (geonode:sealevelchangelgm_1)
InaSAFE analysis result
KoeppenGeiger LGM (geonode:mpi_esm_p_lgm_r1i1p1_koeppen_geiger_classification_1)
This geospatial dataset, is a Koeppen-Geiger climate classification of the MPI-ESM-P Last Glacial Maximum (21k yBP) r1i1p1 model simulations according to the PMIP III LGM experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
KoeppenGeiger LGM Clipped (geonode:lgm_koppengeiger_clipped_mpi_esm_p)
This geospatial dataset, is a Koeppen-Geiger climate classification of the MPI-ESM-P Last Glacial Maximum (21k yBP) r1i1p1 model simulations according to the PMIP III LGM experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
KoeppenGeiger Mid-Holocene clipped (geonode:midholo_koeppengeiger_clipped)
This geospatial dataset, is a Koeppen-Geiger climate classification of the MPI-ESM-P Mid-Holocene (6k yBP) r1i1p1 model simulations according to the PMIP III 21k experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
KoeppenGeiger Pre-Industrial (geonode:mpi_esm_p_preindustrial_r1i1p1_koeppen_geiger_classification)
This geospatial dataset, is a Koeppen-Geiger climate classification of the MPI-ESM-P Pre-Industrial (0k yBP) r1i1p1 model simulations according to the PMIP III Pre-Industrial experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
KoeppenGeiger Pre-Industrial Clipped (geonode:preindustrial_koeppengeiger_clipped)
This geospatial dataset, is a Koeppen-Geiger climate classification of the MPI-ESM-P Pre-Industrial (0k yBP) r1i1p1 model simulations according to the PMIP III Pre-Industrial experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
LGM Glaciation Extends (geonode:lgm)
This dataset comprises the extends of the LGM glaciations. From the Book website: http://booksite.elsevier.com/9780444534477/ The book Ehlers, Gibbard, Hughes: Quaternary Glaciations - Extent and Chronology Volume 15: A closer look presents an up-to-date, detailed overview of Quaternary glaciations all over the world, including the presentation of digital maps which can be used in a geographical information system (GIS) and all recent information on the impact global and climate change has on these glaciations and the impact on our Earth. The maps (Google Earth files) as well as related digital information are available on this companion website. With these larger scale digital base maps, the accuracy of the information is increased by a factor of 16. The difference is striking. Unfortunately, no single, uniform base map can be used at the 1:250,000 scale, therefore, in order to cope with the different levels of accuracy encountered, some of the layers of information have been grouped, so that only consistent information is shown together. All the spatial data are given in shapefiles to allow for easy import into other GIS systems. As not all readers will have ArcGIS at their disposal, in order to allow the non-GIS community a glimpse at the maps some are available as PDFs and also as KMZ files that can be read using Google Earth. Nevertheless the user must be aware of the fact that those files are only images, and their contents are restricted to what has been selected by the author. We hope that the current map set will be widely used, and that it will form the basis for further investigations.
LGM LandMask (LowRes) (geonode:lowres)
This Dataset is derived from ETOPO1 Gloabl Relief Dataset [1]. The landmask is the -120m Bathymetrie contourlien as coast line. [1] http://www.ngdc.noaa.gov/mgg/global/global.html
LGM Sea Level change (EMODnet) (geonode:edmonet120m)
The dataset represents the areae between 0m and -120m, the sea level change between LGM and today, of the EMODnet Digital Terrain Model (DTM). The DTM is generated for European sea regions from selected bathymetric survey data sets and composite DTMs, while gaps with no data coverage are completed by integrating the GEBCO Digital Bathymetry. The original EMODnet data can be freely obtained from: http://www.emodnet-hydrography.eu/
LGM major lakes (geonode:lgm_lakes)
This is GIS dataset contains the major inland waters (rivers and lakes) of Europe during the LGM. The data was collected from data published in scholarly works.
LGM major rivers (geonode:river_lgm)
This is GIS dataset contains the major inland waters (rivers and lakes) of Europe during the LGM. The data was collected from data published in scholarly works.
LGM sealevel change (GEBCO 2014 - HiRes) (geonode:lgm_sealevel_change_hires)
Based on the GEBCO 2014 dataset [1], the area between the 0m coastline of today and the -120m coastline of the Last Glacial Maximum (LGM), is derived and provided in this dataset. To achieve a more realistic and higher resoluting coastline, for producing larger scale maps, the gebco dataset was interpolated from the 30 arc seconds original resolution, to a 0,001 degree resoluting raster. [1] http://www.gebco.net/data_and_products/gridded_bathymetry_data/
LGM sealevel change (GEBCO 2014) (geonode:lgm_sealevel_change_hires_1)
The dataset shows the area between the current coastline (0m) and the LGM coastline (-120m). The data is derived from the GEBCO 2014 dataset.
Ombrotypes of Africa (geonode:ombrotypes_nafrica)
This GIS Data contains a digitalized version of a map of the "Computerized Bioclimatic Maps of the World" publication from S.Rivas-Martinez & S.Rivas-Saenz (http://editaefa.com/mostrarArticulo.php?articulo=65). The maps were obtained from the http://www.globalbioclimatics.org/form/maps.htm website, georeferenced and then digitized. The georeferencing was done on a custom ERTS_1989_LAEA projection (EPSG:3035) , with the Central Meridian set to 20,0° and the Latitude of origin: 0,0.
Ombrotypes of Spain (geonode:ombrotypes_spain)
This GIS Data contains a digitalized version of a map of the "Computerized Bioclimatic Maps of the World" publication from S.Rivas-Martinez & S.Rivas-Saenz (http://editaefa.com/mostrarArticulo.php?articulo=65). The maps were obtained from the http://www.globalbioclimatics.org/form/maps.htm website, georeferenced and then digitized. The georeferencing was done on a custom ERTS_1989_LAEA projection (EPSG:3035) , with the Central Meridian set to 20,0° and the Latitude of origin: 0,0.
Paleocoastline 14 m below mean sea level (geonode:coastline__14m)
This GIS dataset contains a modelled land mask for - 14m sea level low stand during Older Dryas , Europe area and also Younger Dryas, Mediterranean area based on global geoarchives. The sea level data was collected from data published in scholarly works (Lambeck2005, Fleming1998) and GIS proceeded with QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 16 m below mean sea level (geonode:coastline__16m)
This GIS dataset contains a modelled land mask for - 16m sea level low stand during Allerød period for Europe area based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded with QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 18 m below mean sea level (geonode:coastline__18m)
This GIS dataset contains a modelled land mask for - 3m sea level low stand during MIS 2 based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded with QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 19 m below mean sea level (geonode:coastline__19m)
This GIS dataset contains a modelled land mask for - 19m sea level low stand during Older Dryas (16 ka), based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 20 m below mean sea level (geonode:coastline__20m)
This GIS dataset contains a modelled land mask for - 20m sea level low stand dating 100 ka, 190 ka based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 21 m below mean sea level (geonode:coastline__21m)
This GIS dataset contains a modelled land mask for - 21m sea level low stand during Oldest Dryas (17 ka), based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 24 m below mean sea level (geonode:coastline__24m)
This GIS dataset contains a modelled land mask for - 24m sea level low stand during Oldest Dryas (19ka), based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 25 m below mean sea level (geonode:coastline__25m)
This GIS dataset contains a modelled land mask for - 25m sea level low stand dating 20 ka, based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 27 m below mean sea level (geonode:coastline__27m)
This GIS dataset contains a modelled land mask for - 27m sea level low stand during Saalian (Riss) glaciation, 200 ka based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 3 m below mean sea level (geonode:coastline__3m)
This GIS dataset contains a modelled land mask for - 3m sea level low stand during Saalian (Riss) glaciation, 190 ka based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded with QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 40 m below mean sea level (geonode:coastline__40m)
This GIS dataset contains a modelled land mask for - 40m sea level low stand during MIS 3 (60ka), based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 45 m below mean sea level (geonode:coastline__45m)
This GIS dataset contains a modelled land mask for - 45m sea level low stand during Weichselian glaciation (115 ka) based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 5 m above mean sea level (geonode:coastline_5m)
This GIS dataset contains a modelled land mask for + 5m sea level high stand during eemian period based on global geoarchives. The sea level data was collected from data published in scholarly works (Cuffey2000) and GIS proceeded with QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 5 m below mean sea level (geonode:coastline__5m)
This GIS dataset contains a modelled land mask for - 5m sea level low stand relvant for 12 ka (Weichselian) for Europe and also for 6 ka in mediterranean area based on global geoarchives. The sea level data was collected from data published in scholarly works (1:Fleming1998, 2:Lambeck2005) and GIS proceeded with QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 50 m below mean sea level (geonode:coastline__50m)
This GIS dataset contains a modelled land mask for - 50m sea level low stand during MIS 5b (90 ka), based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 65 m below mean sea level (geonode:coastline__65m)
This GIS dataset contains a modelled land mask for - 65m sea level low stand during MIS 3, based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 74 m below mean sea level (geonode:coastline__74m)
This GIS dataset contains a modelled land mask for - 74m sea level low stand during MIS 3 (56 ka), based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 77 m below mean sea level (geonode:coastline__77m)
This GIS dataset contains a modelled land mask for - 77m sea level low stand during Weichselian glaciation (70 ka) based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 115 m below mean sea level (geonode:coastline__115m)
This GIS dataset contains a modelled land mask for - 115m sea level low stand during LGM, based on central eastern Adriatic geoarchives. The sea level data was collected from data published in scholarly works (Sikora2014) and GIS proceeded in QGIS and ArcMap. Concerning the workflow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 120 m below mean sea level (geonode:coastline__120m)
This GIS dataset contains a modelled land mask for - 120m sea level low stand during Saalian maximum extent, MIS 6.2 also as beginnig LGM, based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the workflow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 130 m below mean sea level (geonode:coastline__130m)
This GIS dataset contains a modelled land mask for - 130m sea level low stand during LGM, based on global geoarchives. The sea level data was collected from data published in scholarly works (Lambeck2005) and GIS proceeded in QGIS and ArcMap. Concerning the work flow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 80 m below mean sea level (geonode:coastline__80m)
This GIS dataset contains a modelled land mask for - 80m sea level low stand during MIS 3 (40 ka) and based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the workflow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastline 81 m below mean sea level (geonode:coastline__81m)
This GIS dataset contains a modelled land mask for - 3m sea level low stand during Saalian (Riss) glaciation, 180 ka based on global geoarchives. The sea level data was collected from data published in scholarly works (Fleming1998) and GIS proceeded in QGIS and ArcMap. Concerning the workflow GEBCO 2014 was used as raster data. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Paleocoastlines GIS dataset - 130 m below to 5 m above mean sea level m above (geonode:paleocoastlines)
This GIS dataset contains modelled land mask of characteristic sea level high stand/ low stand events during a period from 140 ka until recent times in Europe, the Nearer East and northern parts of Africa. The sea level data was collected from data published in scholarly works (Lambeck2005, Fleming1998, Cuffey2000, Sikora2014) and GIS proceeded using QGIS and ArcMap. Workflow: First of all the bounding box for GEBCO 2014 (30arc) raster data was set to 21.0_1.0_57.0_62.0 (CRC806 area) and interpolated using factor 0,1. Afterwards raster data was reclassified in order to get the specific elevation level that was mentioned in literature. The next steps were the vectorization of data and extraction of land mask followed by modification of attributes in adding sea level information to the dataset.
Red Sea LGM Sealevel Change (geonode:sealevelchangelgm)
Based on the GEBCO 2014 dataset [1], the area between the 0m coastline of today and the -120m coastline of the Last Glacial Maximum (LGM), is derived and provided in this dataset. To achieve a more realistic and higher resoluting coastline, for producing larger scale maps, the gebco dataset was interpolated from the 30 arc seconds original resolution, to a 0,001 degree resoluting raster. [1] http://www.gebco.net/data_and_products/gridded_bathymetry_data/
Settlement areas of early Neolithic sites in Central Europe (geonode:_13_earlyneolithic_ce_iso35km_wgs84)
So-called “settlement areas” derived from the site distribution map of the early Neolithic in Central Europe (Preuss ed. 1998). The areas were delimited in a six steps protocol from several isolines of different site-densities (Hilpert et al. 2008; Zimmermann et al. 2004; Zimmermann et al. 2009). The distance value of the Largest Empty Circle (LEC) is 3.5 km. Ressources: J. Hilpert/ K.P. Wendt/A. Zimmermann, A Hierarchical Model of Scale Levels for Estimations of Population Densities. In: A. Posluschny/K. Lambers/I. Herzog (eds.), Layers of Perception. Proceedings of the 35th International Conference on Computer Applications and Quantitative Methods in Archaeology (CAA), Berlin, Germany, April 2–6, 2007. Kolloquien zur Vor- und Frühgeschichte, Vol. 10 (Bonn 2008): 252-256. J. Preuss (ed.), Das Neolithikum in Mitteleuropa: Kulturen, Wirtschaft, Umwelt vom 6. bis 3. Jahrtausend v.u.Z., Übersichten zum Stand der Forschung, Karte 1: Verbreitung neolithischer Kulturen in Mitteleuropa (Weissbach 1998). M. Schlummer/Th. Hoffmann/R. Dikau/M. Eickmeier/P. Fischer/R. Gerlach/J. Holzkämper/A. J. Kalis/I. Kretschmer/F. Lauer/A. Maier/J. Meesenburg/J. Meurers-Balke/U. Münch/St. Pätzold/F. Steininger/A. Stobbe/A. Zimmermann, From point to area: Upscaling approaches for Late Quaternary archaeological and environmental data. Earth-Science Reviews 131, 2014, 22-48. A. Zimmermann/J. Richter/T. Frank/K.P. Wendt, Landschaftsarchäologie II: Überlegungen zu Prinzipien einer Landschaftsarchäologie. Ber. RGK 85, 2004:37–95. A. Zimmermann/J. Hilpert/K.P. Wendt, Estimations of Population Density for Selected Periods between the Neolithic and AD 1800. Human Biology 2009: 357–380.
Soil types in western Romania (geonode:romanian_soil)
The provided geodata contain the digitized areas covered by loess and loess-like sediments in Hungary (after Balogh et al. 1956) and the respective coverage in the border region of northwest Romania which has been derived from geoscientific maps and data: soil type and texture after Florea et al. (1971), land cover data after CLC 2006 published by EEA (2012) and geomorphometric data based on the DEM SRTM 1 Arc-Second Global provided by USGS (2015)). Therefrom, digitized and reclassified soil types after the Romanian soil map, and two raster datasets of derived morphometric indices are published. Additionally, a merged shapefile of the resulting loess and loess-like sediments, designed for a scale of about 1:500.000, is published. SOIL_RO: Digitized and reclassified soil types after the Romanian soil map (Florea et al. 1971) for the border region of northwest Romania. The reclassification is based on a conversion to the terminology of the WRB system after Vlad et al. (2012). The denotation of the soil types is compiled in Tab. 1. Please cite this data in reference to Lindner, H.,Lehmkuhl, F., Zeeden, C. (2017): Spatial loess distribution in the eastern Carpathian Basin: a novel approach based on geoscientific maps and data. Journal of Maps, Vol. 2(13), p: 173-181, DOI: 10.1080/17445647.2017.1279083
Spatial loess distribution in the eastern Carpathian Basin: Hungary (geonode:loesshungary)
The provided geodata contain the digitized areas covered by loess and loess-like sediments in Hungary (after Balogh et al. 1956) and the respective coverage in the border region of northwest Romania which has been derived from geoscientific maps and data: soil type and texture after Florea et al. (1971), land cover data after CLC 2006 published by EEA (2012) and geomorphometric data based on the DEM SRTM 1 Arc-Second Global provided by USGS (2015)). Therefrom, digitized and reclassified soil types after the Romanian soil map, and two raster datasets of derived morphometric indices are published. Additionally, a merged shapefile of the resulting loess and loess-like sediments, designed for a scale of about 1:500.000, is published. LD_HU: Digitized areas covered by loess and loess-like sediments in Hungary after the geological map of Hungary 1:300.000 (Balogh et al. 1956). The sediments were reclassified collectively according to Koch & Neumeister (2005). For the denotation of the content see Tab. 2. Corresponding publication: Further information on the methods for generation of the provided geodata is published in Lindner et al. (2017); Geodata set of 'Spatial loess distribution in the eastern Carpathian Basin: a novel approach based on geoscientific maps and data'. The publication includes a map compendium. Please cite this data in reference to Lindner, H.,Lehmkuhl, F., Zeeden, C. (2017): Spatial loess distribution in the eastern Carpathian Basin: a novel approach based on geoscientific maps and data. Journal of Maps, Vol. 2(13), p: 173-181, DOI: 10.1080/17445647.2017.1279083
Spatial loess distribution in the eastern Carpathian Basin: Hungary & Romania (geonode:ld_hu_ro)
The provided geodata contain the digitized areas covered by loess and loess-like sediments in Hungary (after Balogh et al. 1956) and the respective coverage in the border region of northwest Romania which has been derived from geoscientific maps and data: soil type and texture after Florea et al. (1971), land cover data after CLC 2006 published by EEA (2012) and geomorphometric data based on the DEM SRTM 1 Arc-Second Global provided by USGS (2015)). Therefrom, digitized and reclassified soil types after the Romanian soil map, and two raster datasets of derived morphometric indices are published. Additionally, a merged shapefile of the resulting loess and loess-like sediments, designed for a scale of about 1:500.000, is published. LD_HU_RO: Merged shapefiles LD_HU and LD_RO. The sediments were reclassified collectively according to Koch & Neumeister (2005). For the denotation of the content see Tab. 2. LD_RO: Areas covered by loess and loess-like sediments the border region of northwest Romania which have been derived from geoscientific maps and data: soil type and texture after Florea et al. (1971), land cover data after CLC 2006 published by EEA (2012) and geomorphometric data based on the DEM SRTM 1 Arc-Second Global provided by USGS (2015)). The sediments were reclassified collectively according to Koch & Neumeister (2005). For the denotation of the content see Tab. 2. LD_HU: Digitized areas covered by loess and loess-like sediments in Hungary after the geological map of Hungary 1:300.000 (Balogh et al. 1956). The sediments were reclassified collectively according to Koch & Neumeister (2005). For the denotation of the content see Tab. 2. Please cite this data in reference to Lindner, H.,Lehmkuhl, F., Zeeden, C. (2017): Spatial loess distribution in the eastern Carpathian Basin: a novel approach based on geoscientific maps and data. Journal of Maps, Vol. 2(13), p: 173-181, DOI: 10.1080/17445647.2017.1279083
Spatial loess distribution in the eastern Carpathian Basin: Romania (geonode:ld_ro)
The provided geodata contain the digitized areas covered by loess and loess-like sediments in Hungary (after Balogh et al. 1956) and the respective coverage in the border region of northwest Romania which has been derived from geoscientific maps and data: soil type and texture after Florea et al. (1971), land cover data after CLC 2006 published by EEA (2012) and geomorphometric data based on the DEM SRTM 1 Arc-Second Global provided by USGS (2015)). Therefrom, digitized and reclassified soil types after the Romanian soil map, and two raster datasets of derived morphometric indices are published. Additionally, a merged shapefile of the resulting loess and loess-like sediments, designed for a scale of about 1:500.000, is published. LD_RO: Areas covered by loess and loess-like sediments the border region of northwest Romania which have been derived from geoscientific maps and data: soil type and texture after Florea et al. (1971), land cover data after CLC 2006 published by EEA (2012) and geomorphometric data based on the DEM SRTM 1 Arc-Second Global provided by USGS (2015)). The sediments were reclassified collectively according to Koch & Neumeister (2005). For the denotation of the content see Tab. 2. The user is referred to the publication (Lindner et al., 2017) for > details on how the data was generated. Please cite this data in reference to Lindner, H.,Lehmkuhl, F., Zeeden, C. (2017): Spatial loess distribution in the eastern Carpathian Basin: a novel approach based on geoscientific maps and data. Journal of Maps, Vol. 2(13), p: 173-181, DOI: 10.1080/17445647.2017.1279083
World Map of Carbonate Rock Outcrops v3.0 (geonode:karst_wgs)
This Dataset contains shapefiles showing the occurence of carbonate rocks on a world map. These shapefiles are projected in WGS84. There is one shapefile showing the relatively continous carbonates and another one indicating rather abundant but not continous carbonates. Source: http://web.env.auckland.ac.nz/our_research/karst/#karst6
World Map of Carbonate Rock Outcrops v3.0 Extended areas (geonode:karst_wgs_max)
This version of of the world map of carbonate rocks shows extended areas of the "World Map of Carbonate Rock", where the karst is rather abundant but not continous.
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