The Copernicus Global Land Service (CGLS) is earmarked as a component of the Land service to operate a multi-purpose service component that provides a series of bio-geophysical products on the status and evolution of land surface at global scale. The Dynamic Land Cover map at 100 m resolution (CGLS-LC100) is …

La base de données géographique CORINE Land Cover (CLC) est un inventaire biophysique de l'occupation des terres. Il est produit dans le cadre du programme européen d'observation de la terre Copernicus (39 États européens). Données de référence, CORINE Land Cover est issue de l'interprétation visuelle d'images satellitaires et est disponible pour les années suivantes : 1990, 2000, 2006 et 2012. Ces bases d'état sont accompagnées par les bases des changements 1990-2000, 2000-2006 et 2006-2012 (données sur les portions du territoire ayant changé d'occupation des sols). La métropole et les DOM sont couverts par CLC. Pour la Guyane, seule la bande côtière a été traitée.

Corine Land Cover 2018 (CLC2018) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018.

CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.

CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring.

Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/.

Corine Land Cover 2018 is the 2018 update of the COPERNICUS pan-European landcover data series. This dataset is the Irish national CORINE 2018 dataset, covering the Republic of Ireland, which will be integrated into a seamless CORINE 2018 landcover map of Europe. The dataset is based on interpretation of satellite imagery and national in-situ vector data. It is mapped to the standard CORINE classification system (link) and data specifications - minimum mapping unit (mmu) of 25ha and the minimum feature width of 100m.

Corine Land Cover 2006 (CLC2006) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2006. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.

CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring.

Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/.

This dataset features a map of building types for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. A random forest classification was used to map the predominant type of buildings within a pixel. We distinguish single-family residential buildings, multi-family residential buildings, commercial and industrial buildings and lightweight structures. Building types were predicted for all pixels where building density > 25 %. Please refer to the publication for details.

Temporal extent

Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building type values are categorical, according to the following scheme:

0 - No building

1 - Commercial and industrial buildings

2 - Single-family residential buildings

3 - Lightweight structures

4 - Multi-family residential buildings

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment.

This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.

Temporal extent
The acquisition dates of the different data sources vary to some degree:
- Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017.
- Dependent variables: the 3D building models are from 2012-2020 depending on data provider.
- Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016.
Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015.

Data format
The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.

Further information
For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication
Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: https://doi.org/10.1016/j.rse.2020.112128

Acknowledgements
The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. The European Settlement Mask was obtained from the European Commission. 3D building models were obtained from Berlin Partner für Wirtschaft und Technologie GmbH, Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung, Landeshauptstadt Potsdam, Bezirksregierung Köln / Geobasis NRW, and Kompetenzzentrum Geodateninfrastruktur Thüringen. This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

The Copernicus Atmosphere Monitoring Service provides the capacity to continuously monitor the composition of the Earth's atmosphere at global and regional scales. The main global near-real-time production system is a data assimilation and forecasting suite providing two 5-day forecasts per day for aerosols and chemical compounds that are part of the chemical scheme. Prior to 2021-07-01 only two parameters were available, 1. Total Aerosol Optical Depth at 550 nm surface 2. Particulate matter d < 25 um surface Note that system:time_start refers to forecast time.

Urban Atlas 2018 provides reliable, inter-comparable, high-resolution land use and land cover data with integrated population estimates for 788 Functional Urban Areas (FUA) with more than 50,000 inhabitants for the 2018 reference year in EEA38 countries (EU, EFTA, Western Balkans countries, as well as Türkiye) and the United Kingdom.

Urban Atlas is a joint initiative of the European Commission Directorate-General for Regional and Urban Policy and the Directorate-General for Defence Industry and Space in the frame of the EU Copernicus programme, with the support of the European Space Agency and the European Environment Agency.

Per each of the FUAs, a ZIP is provided which includes: (1) the vector data in OGC GeoPackage SQLite format (ETRS89-LAEA, EPSG:3035); (2) PDF document with a high-resolution map of the area; (3) PDF document with the delivery report; (4) symbology files in .lyr, .qml and .sld formats; and (5) a xml document with metadata.

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 DAILY provides aggregated values for each day for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, daily minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Daily total precipitation values are given as daily sums. All other parameters are provided as daily averages. ERA5 data is available from 1979 to three months from real-time. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Daily aggregates have been calculated based on the ERA5 hourly values of each parameter.

This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems.

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

Census data were provided by the German Federal Statistical Offices.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

CORINE Land Cover (CLC) was specified to standardize data collection on land in Europe to support environmental policy development. The reference year of first CLC inventory was 1990 (CLC1990), and the first update created in 2000. Later the update cycle has become 6 years. The number of participating countries has increased over time − currently includes 33 European Environment Agency (EEA) member countries and six cooperating countries (EEA39) with a total area of over 5.8 Mkm2. Ortho-corrected high spatial resolution satellite images provide the geometrical and thematic basis for mapping. In-situ data (topographic maps, ortho-photos and ground survey data) are essential ancillary information. The project is coordinated by the EEA in the frame of the EU Copernicus programme and implemented by national teams under the management and quality control (QC) of the EEA. The basic technical parameters of CLC (i.e. 44 classes in nomenclature, 25 hectares minimum mapping unit (MMU) and 100 meters minimum mapping width) have not changed since the beginning, therefore the results of the different inventories are comparable.

The layer of CORINE Land Cover Changes (CHA) is produced since the second CLC inventory (CLC2000). CHA is derived from satellite imagery by direct mapping of changes taken place between two consecutive inventories, based on image-to-image comparison. Change mapping applies a 5 ha MMU to pick up more details in CHA layer than in CLC status layer. Integration of national CLC and CHA data includes some harmonization along national borders. Two European validation studies have shown that the achieved thematic accuracy is above the specified minimum (85 %). Primary CLC and CHA data are in vector format with polygon topology. Derived products in raster format are also available. The seamless European CLC and CHA time series data (CLC1990, CLC2000, CLC2006, CLC2012, CLC2018 and related CHA data) are distributed in the standard European Coordinate Reference System defined by the European Terrestrial Reference System 1989 (ETRS89) datum and Lambert Azimuthal Equal Area (LAEA) projection (EPSG: 3035). Results of the CLC inventories can be downloaded from Copernicus Land site free of charge for all users.

CLC data can contribute to a wide range of studies with European coverage, e.g.: ecosystem mapping, modelling the impacts of climate change, landscape fragmentation by roads, abandonment of farm land and major structural changes in agriculture, urban sprawl, water management.

Corine Land Cover 2000 (CLC2000) is one of the datasets produced within the frame the Corine Land Cover programme referring to land cover / land use status of year 2000.

The Corine Land Cover (CLC) is a European programme, coordinated by the European Environment Agency (EEA), providing consistent and thematically detailed information on land cover and land cover changes across Europe. CLC products are based on the classification of satellite images by the national teams of the participating countries - the EEA member and cooperating countries (EEA39). National CLC inventories are further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres.

The CLC programme provides important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Community as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture and implementing the EU Water Framework Directive. The CLC programme is a part of the Copernicus Land Monitoring Service (https://land.copernicus.eu/) run by the European Commission and the European Environment Agency, which provides environmental information from a combination of air- and space-based observation systems and in-situ monitoring.

Additional information about CLC (product description, mapping guides and class descriptions) can be found here: https://land.copernicus.eu/user-corner/technical-library/.

This dataset is a compilation of geographic rasters from multiple environmental data sources. It aims at making the life of SDM users easier. All rasters cover the metropolitan French territory, but have varying resolutions and projections. Each directory inside the main directory "0_mydata" contain a single environmental raster. Punctual extraction of raster values can be easily done for large sets of WGS84-(longitude,latitude) points coordinates and for multiple rasters at the same time through the R function get_variables of script _functions.R from Github repository: https://github.com/ChrisBotella/SamplingEffort. All data sources are accessible on the web and free of use, at least for scientific purpose. They have various conditions of citations. Anyone diffusing a work using the present data must reference along with the present DOI, the original source data employed. Those source data are described in the paragraphs below. We provide the articles to cite, when required, and webpages for access.

Pedologic Descriptors of the ESDB v2: 1 km × 1 km Raster Library : The library contains multiple soil pedology (physico-chemical properties of the soil) descriptors raster layers covering Eurasia at a resolution of 1 km. We selected 11 descriptors from the library. They come from the PTRDB. The PTRDB variables have been directly derived from the initial soil classification of the Soil Geographical Data Base of Europe (SGDBE) using expert rules. For more details, see [1, 2] and [3]. The data is maintained and distributed freely for scientific use by the European Soil Data Centre (ESDAC) at http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster. The 11 rasters are in the directories "awc_top", "bs_top", "cec_top", "dimp", "crusting", "erodi", "dgh", "text", "vs", "oc_top", "pd_top".

Corine Land Cover 2012, Version 18.5.1, 12/2016 : It is a raster layer describing soil occupation with 48 categories across Europe (25 countries) at a resolution of 100 m. This data base of the European Union is freely accessible online for all use at http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012. The raster of this variable is in the directory "clc".

Hydrographic Descriptor of BD Carthage v3: BD Carthage is a spatial relational database holding many informations on the structure and nature of the french metropolitan hydrological network. For the purpose of plants ecological niche, we focus on the geometric segments representing watercourses, and polygons representing hydrographic fresh surfaces. The data has been produced by the Institut National de l’information Géographique et forestière (IGN) from an interpretation of the BD Ortho IGN. It is maintained by the SANDRE under free license for non-profit use and downloadable at:
http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX
From this shapefile, we derived a raster containing the binary value raster proxi_eau_fast, i.e. proximity to fresh water, all over France.We used qgis to rasterize to a 12.5m resolution, with a buffer of 50m, the shapefile COURS_D_EAU.shp on
one hand, and the polygons of SURFACES_HYDROGRAPHIQUES.shp with attribute NATURE=“Eau douce
permanente” on the other hand.We then created the maximum raster of the previous ones (So the value of 1 correspond to an approximate distance of less than 50m to a watercourse or hydrographic surface of fresh water). The raster is in the directory named "proxi_eau_fast".

USGS Digital Elevation Data : The Shuttle Radar Topography Mission achieved in 2010 by Endeavour shuttle measured elevation at three arc second resolution over most of the earth surface. Raw measures have been post-processed by NASA and NGA in order to correct detection anomalies. The data is available from the U.S. Geological Survey, and downloadable on the Earthexplorer (https://earthexplorer.usgs.gov/). One may refer to https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects for more informations. the elevation raster is in the directory named "alti".

Potential Evapotranspiration of CGIAR-CSI ETP : The CGIAR-CSI distributes this worldwide monthly potential-evapotranspiration raster data. It is pulled from a model developed by Antonio Trabucco [4, 5]. Those are estimated by the Hargreaves formula, using mean monthly surface temperatures and standard deviation from WorldClim 1:4 (http://www.worldclim. org/), and radiation on top of atmosphere. The raster is at a 1km resolution, and is
freely downloadable for a nonprofit use at: http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description. This raster is in the directory "etp".

Bioclimatic Descriptors of Chelsea Climate Data 1.1: Those are raster data with worldwide coverage and 1 km resolution. A mechanistical climatic model is used to make spatial predictions of monthly mean-max-min temperatures, mean precipitations and 19 bioclimatic variables, which are downscaled with statistical models integrating historical measures of meteorologic stations from 1979 to today. The exact method is explained in the reference papers [6] and [7]. The data is under Creative Commons Attribution 4.0 International License and downloadable at (http://chelsa-climate.org/downloads/). The 19 bioclimatic rasters are located in the directories named "chbio_X".

ROUTE500 1.1: This database register classified road linkages between cities (highways, national roads, and departmental roads) in France in shapefile format, representing approxi-mately 500,000 km of roads. It is produced under free license (all uses) by the IGN. Data are available online at http://osm13.openstreetmap.fr/~cquest/route500/. For deriving the variable “droute_fast”, the distance to the main roads networks, we computed with qGis the distance raster to the union of all elements of the shapefile ROUTES.shp (segments).

References :

[1] Panagos, P. (2006). The European soil database. GEO: connexion, 5(7), 32–33.

[2] Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L. (2012). European Soil Data
Centre: Response to European policy support and public data requirements. Land Use Policy,
29(2),329–338.

[3] Van Liedekerke, M. Jones, A. & Panagos, P. (2006). ESDBv2 Raster Library-a set of rasters
derived from the European Soil Database distribution v2. 0. European Commission and the
European Soil Bureau Network, CDROM, EUR, 19945.

[4] Zomer, R., Bossio, D., Trabucco, A., Yuanjie, L., Gupta, D. & Singh, V. (2007). Trees and
water: smallholder agroforestry on irrigated lands in Northern India.

[5] Zomer, R., Trabucco, A., Bossio, D. & Verchot, L. (2008). Climate change mitigation: A
spatial analysis of global land suitability for clean development mechanism afforestation and
reforestation. Agriculture, ecosystems & environment, 126(1), 67–80.

[6] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler,
M. (2016). Climatologies at high resolution for the earth’s land surface areas. arXiv preprint
arXiv:1607.00217.

[7] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler, M.
(2016). CHELSEA climatologies at high resolution for the earth’s land surface areas (Version
1.1).

The Timor-Leste land cover and change dataset covers an area of 14 931km2 and mapped with dichotomous (8 land cover classes)and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2016, LCC: 2000, 2005, 2010). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 4413 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data).

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset), Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326), Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change Land cover/change dataset attributes: [map_codeA] - dichotomous level, [map_code} - modular level, [class_name] - corresponding modular class name. Validation dataset attributes (not all are present): [plaus200X] - corresponding class for the change map (i.e. 2000), modular level [plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes [plaus20XX] - corresponding class for the land cover map (i.e. 2016), modular level [plaus20XXr] - corresponding class for the land cover map (i.e. 2016), aggregated classes The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

Groundwater is the water that soaks into the ground from rain and can be stored beneath the ground. Groundwater floods occur when the water stored beneath the ground rises above the land surface. The Historic Groundwater Flood Mapshows the observed peak flood extents caused by groundwater in Ireland. This map was made using satellite images (Copernicus Programme Sentinel-1), field data, aerial photos, as well as flood records from the past. Most of the data was collected during the flood events of winter 2015 / 2016, as in most areas this data showed the largest floods on record.The map is a vector dataset. Vector data portray the world using points, lines, and polygons (area). The floods are shown as polygons. Each polygon has info about the type of flood, the data source, and the area of the flood.The flood extents were calculated using data and techniques with various precision levels, and as such, it may not show the true historic peak flood extents.The Winter 2015/2016 Surface Water Flooding map shows fluvial (rivers) and pluvial (rain) floods, excluding urban areas, during the winter 2015/2016 flood event, and was developed as a by-product of the historic groundwater flood map.The map is a vector dataset. The floods are shown as polygons. Each polygon has info about the type of flood, the data source, and the area of the flood.The flood extents were made using remote sensing images (Copernicus Programme Sentinel-1), which covered any site in Ireland every 4-6 days. As such, it may not show the true peak flood extents.

Pan-EU Land Mask Summary

Considering the land mask for pan-EU, we will closely match the data coverage of https://land.copernicus.eu/pan-european i.e. the official selection of countries listed here: https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39.

There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of AI4SoilHealth Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found here.

The masks are:

1. Landmask

2. ISO-code country mask

3. NUTS3 mask

Name convention

To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:

1. Generic variable name: country.code

2. Variable procedure combination i.e. method standard (standard abbreviation): iso.3166

3. Position in the probability distribution / variable type: c

4. Spatial support (usually horizontal block) in m or km: 30m

5. Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s

6. Time reference begin time (YYYYMMDD): 20210101

7. Time reference end time: 20211231

8. Bounding box (2 letters max): eu

9. EPSG code: epsg.3035

10. Version code i.e. creation date: v20230722

An example of a file-name based on the description above:

country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722

Landmask

The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.

Two reference datasets are used,

1. WorldCover, 10 m resolution.

2. EuroGlobalMap, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.

When generating the land mask, the two reference datasets in a way that:

• If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask.

• Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.

The landmask consists of 4 values:

• 10: not in the pan-EU area, i.e. out of mapping scope

• 1: land

• 2: inland water

• 3: ocean

This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.

ISO-3166 country code mask

This ISO-3166 country code mask is created from EuroGlobalMap country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.

The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.

NUTS-3 mask

The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.

The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.

It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.

CORINE Land Cover 2018 kuvaa koko Suomen maankäyttöä ja maanpeitettä vuonna 2018. Sykessä EU:n Copernicus Land -hankkeessa tuotettiin Suomen alueelta maanpeiteaineistot sekä laadittiin maanpeitteen muutoksia välillä 2012-2018 kuvaavat aineistot. Aineistot luotiin kahdella tarkkuustasolla: toinen EU-vaatimusten mukaisesti ja toinen kansalliseen käyttöön. Aineisto koostuu rasterimuotoisesta paikkatietokannasta (erotuskyky 20 * 20 m) ja vektorimuotoisesta paikkatietokannasta, jossa pienin maastossa erottuva alue on vähintään 25 ha ja kapeimmillaan 100 metriä. Kansallisen muutosaineiston 2012-2018 pienin kuvio on 0,5 ha ja eurooppalaisen 5 ha. Aineisto on tuotettu Sykessä olemassa oleviin paikkatietoaineistoihin sekä satelliittikuvatulkintaan perustuen. Vektoriaineisto tuotettiin yleistämällä rasteriaineistoa EEA:n CORINE-sääntöjen mukiseksi. Vektoriaineistoissa maankäyttöä/maanpeitettä kuvataan kolmitasoisella hierarkisella luokittelulla. Viisi pääluokkaa (rakennetut alueet; maatalousalueet; metsät sekä avoimet kankaat ja kalliomaat; kosteikot ja avoimet suot sekä vesialueet) jaetaan toisella tasolla yhteensä 15 alaluokkaan.. Kolmannella luokittelutasolla luokat jaetaan edelleen yhteensä 44 alaluokkaan. Rasteriaineistossa on joidenkin luokkien kohdalla vielä neljännen tason kansallisia luokkia. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0). Aineistosta on julkaistu INSPIRE-tietotuote. Käyttötarkoitus: Vektoriaineisto, jossa minimikuviokoko on 25 ha/muutos 5 ha, on tuotettu Euroopan ympäristövirastolle. Tarkempi 20 m resoluutiolla oleva rasteriaineisto ja 1 ha muutosaineisto on tarkoitettu kansalliseen käyttöön kuvaamaan maanpeitettä/maankäyttöä. Aineistoja voidaan käyttää paikkatietoanalyysien lisäksi myös taustakarttoina. Lisätietoja: https://www.syke.fi/fi-FI/Tutkimuskehittaminen/Tutkimus_ja_kehittamishankkeet/Hankkeet/Maankaytto_ja_maanpeiteaineistojen_tuottaminen_CORINE_Land_Cover_2018_hankkeessa_ja_Copernicus_Landaineistojen_validointi_Suomessa https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/CorineMaanpeite2018.pdf https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/CorineMaanpeite2018Luokat.xls https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/CorineMaanpeite2018Luokkakuvaus.pdf CORINE Land Cover 2018 dataset provides information on Finnish land cover and land use on 2018, and its changes from 2012 to 2018. The data was produced as a part of EU Copernicus Land project. The dataset includes several spatial layers: • CLC raster (resolution of 20x20 m) • CLC vector (minimum mapping unit 25 hectares and minimum width 100 m). • Change vector (minimum mapping unit 5 hectares) • Change raster (minimum mapping unit 0,5 hectares) The dataset has been produced in Finnish Environment Institute (Syke), based on automated interpretation of satellite images and data integration with existing digital map data. The vector dataset was produced from raster data by generalization according to the CORINE 2018 project class definitions. The nomenclature of the vector data has 3 hierarchy levels. The first level classes are: artificial surfaces, agricultural areas, forests and seminatural areas, wetlands and water. The second level has 15 classes and the third level 44 sub-classes. The raster dataset has an additional fourth, national class in some of the sub-classes. Syke applies Creative Commons By 4.0 International license for open datasets. Data was produced with funding by the European Union. Copyright Copernicus Programme. Syke has undertaken to distribute the data on behalf of EEA under Specific Contract No 3436/R0-Copernicus/EEA.56936 implementing Framework service contract No EEA/IDM/R0/16/009/Finland. Syke accepts no responsibility or liability whatsoever with regard to the content and use of these data. The vector land cover dataset (25 ha) and the change dataset (5 ha) were produced for the European Environment Agency as a part of EU Copernicus Land project for harmonized land cover maps and statistics in Europe. The more specific raster dataset (20 m x 20 m) and the change (0,5 ha) were produced for national use to provide information on Finnish land cover and land use. The datasets can be used in analyses and as background maps. The source material is generally from years 2016-2017.

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

• Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
• Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

The deliverables will have restricted access at least until the end of ECFAS

Description of the containing files inside the Dataset.

The ECFAS Pan-EU Impact Catalogue collects impact layers associated to the flood scenarios contained in the ECFAS Pan-EU Flood Catalogue. To produce the Flood Catalogue, the coast was divided into geographic regions embracing similar oceanographic conditions, and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. Impacts associated to the flood maps were calculated following the approach described in the technical document of the ECFAS Deliverable 5.3 Algorithms for Impact Assessment (ECFAS Impact Tool; Duo et al., 2021). The ECFAS Impact Tool was adapted to assess the affected population, the damage to buildings, roads and railways and the exposure of a variety of other assets (e.g. agriculture, points of interest, etc.) for the flood scenarios included in the ECFAS Flood Catalogue.

The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.

The Impact Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:

Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. Technical document on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). www.ecfas.eu

The ECFAS Pan-EU Flood Catalogue:

Le Gal, M., Fernández Montblanc, T., Montes Pérez, J., Duo, E., Souto Ceccon, P., Cabrita, P., and Ciavola, P., 2022. ECFAS Pan-EU Flood Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). www.ecfas.eu

The ECFAS Impact Tool:

Duo, E., Montes Pérez, J., and Souto-Ceccon, P.E. (2021). ECFAS Impact Tool, D5.3 – Algorithms for impact assessment - ECFAS project (GA 101004211), www.ecfas.eu, link: https://doi.org/10.5281/zenodo.5809297

This ECFAS Impact Catalogue is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/.

*The size of the uncompressed dataset is 99 GB.

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.