Mapping and classifying the seabed of the West Greenland continental shelf. Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60┬░N to 72┬░N in depths of 61ÔÇô725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200ÔÇô700 m below 72┬░N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists.

The High Resolution Layer Cropping Patterns - Cropping Seasons Types (CPCSY) raster product provides the number of different crop types grown in a 3-year period [0-3] (excluding cover crops). This dataset is provided annually starting in 2017 with 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles covering the EEA38 countries. High Resolution Layer Croplands product is part of the European Union’s Copernicus Land Monitoring Service. This dataset includes data from the French Overseas Territories (DOMs)

The "EMODnet Digital Bathymetry (DTM) - 2018" is a multilayer bathymetric product for Europe’s sea basins covering: • the Greater North Sea, including the Kattegat and stretches of water such as Fair Isle, Cromarty, Forth, Forties,Dover, Wight, and Portland • the English Channel and Celtic Seas • Western Mediterranean, the Ionian Sea and the Central Mediterranean Sea • Iberian Coast and Bay of Biscay (Atlantic Ocean) • Adriatic Sea (Mediterranean) • Aegean - Levantine Sea (Mediterranean). • Madeira and Azores (Macaronesia) • Baltic Sea • Black Sea • Norwegian and Icelandic Seas • Canary Islands (Macaronesia) • Arctic region and Barentz Sea The DTM is based upon more than 9400 bathymetric survey data sets and Composite DTMs that have been gathered from 49 data providers from 24 countries riparian to European seas. Also Satellite Derived Bathymetry data products have been included derived from Landsat 8 satellite images. The source reference layer in the portal viewing service gives metadata of the data sets used with their data providers; the metadata also acknowledges the data originators. The incorporated survey data sets itself can be discovered and requested for access through the Common Data Index (CDI) data discovery and access service that in September 2018 contained > 27.000 survey data sets from European data providers for global waters. This discovery service makes use of SeaDataNet standards and services and have been integrated in the EMODnet portal (https://emodnet.ec.europa.eu/en/bathymetry#bathymetry-services ). The Composite DTMs are described using the Sextant Catalogue Service that makes also use of SeaDataNet standards and services. Their metadata can be retrieved through interrogating the Source Reference map in the Central Map Viewing service (https://emodnet.ec.europa.eu/geoviewer/ ). In addition, the EMODnet Map Viewer gives users wide functionality for viewing and downloading the EMODnet digital bathymetry such as: • water depth (refering to the Lowest Astronomical Tide Datum - LAT) in gridded form on a DTM grid of 1/16 * 1/16 arc minute of longitude and latitude (ca 115 * 115 meters) • option to view depth parameters of individual DTM cells and references to source data • option to download DTM in 58 tiles in different formats: EMO, EMO (without GEBCO data), ESRI ASCII, ESRI ASCII Mean Sea Level, XYZ, NetCDF (CF), RGB GeoTiff and SD • option to visualize the DTM in 3D in the browser without plug-in • layer with a number of high resolution DTMs for coastal regions • layer with wrecks from the UKHO Wrecks database. The EMODnet DTM is also available by means of OGC web services (WMS, WFS, WCS, WMTS), which are specified at the EMODnet Bathymetry portal. The original datasets themselves are not distributed but described in the metadata services, giving clear information about the background survey data used for the DTM, their access restrictions, originators and distributors and facilitating requests by users to originator.

The Copernicus High Resolution Layer Tree Cover Precense Change (TCPC) 2018-2021 raster product provides information on the change between the reference years 2018 and 2021 and consists of 4 thematic classes (unchanged areas with no tree cover / new tree cover / loss of tree cover / unchanged areas with tree cover). The class 255 = outside area is predefined by the 100m boundary layer and remains unchanged. This layer for previous reference year compraissions is called Tree Cover Change Mask (TCCM). This dataset is provided in 20 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles covering the EEA38 countries. The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. Confidence layer available for the dataset. This dataset includes data from the French Overseas Territories (DOMs)

The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the third product level of EGMS: Ortho. to derive two further layers; one of purely vertical displacements (the one described by this metadata), the other of purely east-west displacements. Both layers are resampled to a 100 m grid. The Ortho product eases the interpretation process of non-experts since the viewing geometry has not to be considered anymore. EGMS Ortho is visualised as a vector map of measurement points colour-coded by average velocity (vertical or east-west components) and distributed to users in comma-separated values format. Each point is associated with a time series of displacement, i.e. a plot with values of displacement per acquisition of the satellite.

The GEBCO_2022 Grid is a global continuous terrain model for ocean and land with a spatial resolution of 15 arc seconds. In regions outside of the Arctic Ocean area, the grid uses as a base Version 2.4 of the SRTM15_plus data set (Tozer, B. et al, 2019). This data set is a fusion of land topography with measured and estimated seafloor topography. Included on top of this base grid are gridded bathymetric data sets developed by the four Regional Centers of The Nippon Foundation-GEBCO Seabed 2030 Project. The GEBCO_2022 Grid represents all data within the 2022 compilation. The compilation of the GEBCO_2022 Grid was carried out at the Seabed 2030 Global Center, hosted at the National Oceanography Centre, UK, with the aim of producing a seamless global terrain model. Outside of Polar regions, the Regional Centers provide their data sets as sparse grids i.e. only grid cells that contain data are populated. These data sets were included on to the base using a remove-restore blending procedure. This is a two-stage process of computing the difference between the new data and the base grid and then gridding the difference and adding the difference back to the existing base grid. The aim is to achieve a smooth transition between the new and base data sets with the minimum of perturbation of the existing base data set. The data sets supplied in the form of complete grids (primarily areas north of 60N and south of 50S) were included using feather blending techniques from GlobalMapper software. The GEBCO_2022 Grid has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and the General Bathymetric Chart of the Oceans (GEBCO). It aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor by 2030 and make it available to all. Funded by the Nippon Foundation, the four Seabed 2030 Regional Centers include the Southern Ocean - hosted at the Alfred Wegener Institute, Germany; South and West Pacific Ocean - hosted at the National Institute of Water and Atmospheric Research, New Zealand; Atlantic and Indian Oceans - hosted at the Lamont-Doherty Earth Observatory, Columbia University, USA; Arctic and North Pacific Oceans - hosted at Stockholm University, Sweden and the Center for Coastal and Ocean Mapping at the University of New Hampshire, USA.

The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product.

Envision Mapping was sub-contracted by Heriot Watt University for Scottish Natural Heritage (SNH) to undertake broad scale subtidal biotope mapping of Sullom Voe cSAC. Sullom Voe in the Shetland Isles is the most northerly site in the UK to be selected as a representative of large shallow inlets and bays, and within the site series it is the only Scottish example of a ria (known locally as a ?voe?). The boreal-arctic (northern) species-rich communities of Sullom Voe are restricted to Shetland voes and are not represented elsewhere in the SAC series. The purpose was to map the main features and biota using acoustic remote sensing techniques combined with grab and video sampling.

This gridded product visualizes 1960 - 2014 water body dissolved oxygen concentration (umol/l) in the North Sea domain, for each season (winter: December – February; spring: March – May; summer: June – August; autumn: September – November). It is produced as a Diva 4D analysis, version 4.6.11: a reference field of all seasonal data between 1960-2014 was used; results were logit transformed to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. Every step of the time dimension corresponds to a 10-year moving average for each season. The depth dimension allows visualizing the gridded field at various depths.

<p>Original provider: Happywhale Dataset credits: Happywhale and contributors Abstract: Happywhale.com is a resource to help you know whales as individuals, and to benefit conservation science with rich data about individual whales.-nbsp;</p>