These are geospatial data that characterize the distribution of polar bear denning habitat on the National Petroleum Reserve–Alaska (NPR-A). Two datasets are included in this data package, they are both vector geospatial datasets of putative denning habitat (one set each for the western and eastern portion of the NPR-A). Each vector dataset is provided in both ESRI shapefile and Keyhole Markup Language (KML) formats. Denning habitat was estimated from computer interrogation of digital terrain models (DTM) derived from Interferometric Synthetic Aperture Radar (IfSAR) imagery collected by sensors mounted on fix-wing aircraft. Den habitat is defined as abrupt landscape features (e.g., coastal and riverbanks, lake shores) that are likely to accumulate snow to a depth sufficient for polar bears to build a maternal den (i.e., > 1 meter deep).

The PGC Map Catalog holds an archive of historical and contemporary polar maps. PGC obtains libraries of paper maps, scans them at a very high resolution, and provides digital copies in many formats, most with accompanying georeferencing information.

This data package includes 3 child items with mapping data of polar bear den habitat on the Arctic Coastal Plain of Alaska. Child Item 1: "Data Used to Compare Photo-Interpreted and IfSAR-Derived Maps of Polar Bear Denning Habitat for the 1002 Area of the Arctic National Wildlife Refuge, Alaska, 2006-2016" Child Item 2: "Polar Bear Maternal Den Habitat on the Coastal Plain of Northern Alaska Between the Colville River and the Alaska/Canada border" Child Item 3: "Mapping Data of Polar Bear Maternal Den Habitat, National Petroleum Reserve–Alaska (NPR-A)"

Circumpolar map of all polar bear subpopulation as recognized by the IUCN/Polar Bear Specialist Group in 2009 (PBSG 2010a). Total area covered = 24 mill. km(2). Vongraven, D and Peacock, E. Development of a pan-Arctic monitoring plan for polar bears:background paper. Published in the Arctic Biodiversity trends 2010, Indicator #01 - released in May 2010

Updated in feb.2025 with spatial data from 2022

The landscape physiography map displays regions of plains, hills, mountains, glaciers and lakes. Generally, plains are flat or gently rolling landscapes less than 200 m above sea level. Hills are more dissected than plains (more surface roughness) and are 200-500 m in elevation. Mountains have greater surface roughness and are above 500 m in elevation. The Alaska Arctic Tundra Vegetation Map is a more detailed map of the Alaska portion of the Circumpolar Arctic Vegetation Map. The Alaska Arctic Tundra Vegetation Map is a more detailed map of the Alaska portion of the Circumpolar Arctic Vegetation Map. The landscape mapping is the same as the Circumpolar Arctic Vegetation Map. Back to Alaska Arctic Tundra Vegetation Map (Raynolds et al. 2006) Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI , Bioclimate Subzone, Elevation, False Color-Infrared CIR, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Raynolds, M.K., Walker, D.A., Maier, H.A. 2005. Plant community-level mapping of arctic Alaska based on the Circumpolar Arctic Vegetation Map. Phytocoenologia. 35(4):821-848. http://doi.org/10.1127/0340-269X/2005/0035-0821 Raynolds, M.K., Walker, D.A., Maier, H.A. 2006. Alaska Arctic Tundra Vegetation Map. 1:4,000,000. U.S. Fish and Wildlife Service. Anchorage, AK.

The landscape physiography map was derived from topographic data, regional physiographic maps, and visual interpretation of the AVHRR false-CIR image. Physiographic codes were assigned to each of the vegetation polygons. For Greenland, Russia and the United States, detailed landscape maps formed the basis of the vegetation polygons (see vegetation mapping section). However, these regional landscape maps did not have a uniform legend and could not be easily combined. The detailed landscape categories were combined into the ten general categories most useful in creating the vegetation map which were further combined into five categories for the final map (plains, hills, mountains, glaciers (including nunataks), and water (including lakes, lagoons, and ocean)). The plateau category was merged with the either the hill category (500 m elevation), because it was interpreted differently in different portions of the map. Riparian areas were also not consistently defined in different parts of the map. Many riparian areas, though of great ecological importance, were also too narrow to map (less than 5 km width). For these reasons, the riparian physiography category (and the riparian vegetation category) were mapped as linear features, not polygons. Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Elvebakk, A. 1999. Bioclimate delimitation and subdivisions of the Arctic. Pages 81-112 in I. Nordal and V. Y. Razzhivin, editors. The Species Concept in the High North - A Panarctic Flora Initiative. The Norwegian Academy of Science and Letters, Oslo. Yurtsev, B. A. 1994. Floristic divisions of the Arctic. Journal of Vegetation Science 5:765-776.

Mature Support Notice: This item is in mature support as of September 2020. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item. For more information, see Basemaps Life Cycle Support Status.The Arctic Ocean Basemap is designed to be used as a basemap for the Arctic Ocean by marine GIS professionals and as a reference map by anyone interested in ocean data for the Arctic region. The map is comprised of two layers: the base layer and the reference layer. The base layer focuses on marine bathymetry. It also includes inland waters overlaid on land cover and shaded relief imagery. There are no labels or boundary lines on the base layer. The reference layer is designed to be used as annotation for features on the base layer. This map service includes marine water body names and undersea feature names. Land features include administrative boundaries, cities, and inland waters names. The map was compiled from a variety of best available sources from several data providers, including General Bathymetric Chart of the Oceans GEBCO_08 Grid version 20100927 and IHO-IOC GEBCO Gazetteer of Undersea Feature Names August 2010 version (https://www.gebco.net), the International Bathymetric Chart of the Arctic Ocean (https://ibcao.org), National Oceanic and Atmospheric Administration (NOAA) and National Geographic for the oceans; and Garmin, HERE, and Esri for topographic content. The basemap was designed and developed by Esri. The Arctic Ocean Basemap currently provides coverage down to a scale of ~1:881k. The Arctic Ocean Basemap can and will be extended with higher resolution bathymetric data. You can contribute your bathymetric data to this map and have it served by Esri for the benefit of the GIS community. For more information on how to contribute data, please contact Oceanbasemapteam@esri.com. NOTE: Data from the GEBCO_08 grid shall not be used for navigation or for any other purpose relating to safety at sea. The GEBCO_08 Grid is largely based on a database of ship-track soundings with interpolation between soundings guided by satellite-derived gravity data. In some areas, data from existing grids are included. The GEBCO_08 Grid does not contain detailed information in shallower water areas, information concerning the generation of the grid can be found on GEBCO’s web site: GEBCO. The GEBCO_08 Grid is accompanied by a Source Identifier (SID) Grid which indicates which cells in the GEBCO_08 Grid are based on soundings or existing grids and which have been interpolated. The latest version of both grids and accompanying documentation is available to download, on behalf of GEBCO, from the British Oceanographic Data Centre (BODC) https://www.bodc.ac.uk/data/online_delivery/gebco/. The names of the IHO (International Hydrographic Organization), IOC (intergovernmental Oceanographic Commission), GEBCO (General Bathymetric Chart of the Oceans), NERC (Natural Environment Research Council) or BODC (British Oceanographic Data Centre) may not be used in any way to imply, directly or otherwise, endorsement or support of either the Licensee or their mapping system. Coordinate System: Alaska Polar Stereographic (WKID 5936) Scale Range: 1: 451,295,122 down to 1: 881,435

The dataset contains model output presented in the manuscript 'A long-term dataset of climatic mass balance, snow conditions and runoff in Svalbard (1957-2018)', which is considered for publication in The Cryosphere. The data are structured in 3-D arrays containing spatially distributed and annual mean values of the variables specified below. The spatial resolution is 1x1-km. Variables included in the dataset: Air temperature

The dataset contains several shapefiles with information about Universidad glacier, which were derived from datasets not available to IG PAS. Said datasets were made available without charge to the PI of the "Changes of surface hydrology of a mountain glacier studied with very high resolution aerial and satellite images and machine learning " project for the purpose of realizing the goals of the project.

Retirement Notice: This item is in mature support as of July 2024 and will retire in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.ArcticDEM is a National Geospatial-Intelligence Agency (NGA) and National Science Foundation (NSF) public-private initiative to automatically produce a high-resolution, high-quality Digital Surface Model (DSM) of the Arctic using sub-meter, stereoscopic satellite imagery collected by DigitalGlobe’s satellite constellation.The Arctic DEM layer is rendered here as Aspect Map. Using the server-side aspect function, this layer provides a colorized representation of aspect. The orientation of the downward sloping surface is indicated by different colors, rotating from green (North) to blue (East), to magenta (South) to orange (West). For more information on the source data, see ArcticDEM.

Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.

The Circumpolar Arctic Vegetation Map (CAVM) is a geoecological map (front) of the entire Arctic with a unified legend (back). It is the first vegetation map of an entire global biome at a comparable resolution. It was funded by the US National Science Foundation (OPP-9908-829), the US Fish & Wildlife Service, the US Geological Survey and the US Bureau of Land Management. The CAVM region is north of the climatic limit of trees and is characterized by an arctic climate, arctic flora, and tundra vegetation. It excludes tundra regions than have a boreal flora such as the boreal oceanic areas of Iceland and the Aleutian Islands and alpine tundra south of the latitudinal treeline. The map was published at 1:7.5 million scale and displays the vegetation using 15 units (CAVM Team 2003, legend details: www.arcticatlas.org/maps/themes/cp/cpvg). The methods used to make the map are described in Walker et al. 2005. The CAVM is a polygon (vector) map. The GIS data are in shapefile format, and include fields for bioclimate subzone, floristic province, lake cover, landscape, substrate chemistry and vegetation category. There is also a landscape age shapefile which was created after the publication of the CAVM (Raynolds et al. 2009) In addition, there are a number of raster maps of the same extent (the Arctic), based on satellite data from the Advanced High Resolution Radiometer (AVHRR) instruments. These include the false color-infrared and NDVI images which formed the base maps for the CAVM mapping effort (Walker et al. 2005, Raynolds et al. 2006), a recent biomass map (Raynolds et al. 2012), biomass trends (Epstein et al. 2012), NDVI trends (Bhatt et al. 2010), and Summer Warmth Index (Raynolds et al. 2008). Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation Layer References CAVM Team. 2003. Circumpolar Arctic Vegetation Map, scale 1:7 500 000. Conservation of Arctic Flora and Fauna (CAFF) Map No. 1. U.S. Fish and Wildlife Service, Anchorage, Alaska. Bhatt, U. S., D. A. Walker, M. K. Raynolds, J. C. Comiso, H. E. Epstein, G. J. Jia, R. Gens, J. E. Pinzon, C. J. Tucker, C. E. Tweedie, and P. J. Webber. 2010. Circumpolar arctic tundra vegetation change is linked to sea ice decline. Earth Interactions 14:1-20. doi: 10.1175/2010EI1315.1171. Epstein, H. E., M. K. Raynolds, D. A. Walker, U. S. Bhatt, C. J. Tucker, and J. E. Pinzon. 2012. Dynamics of aboveground phytomass of the circumpolar arctic tundra during the past three decades. Environmental Research Letters 7:015506 (015512 pp). Raynolds, M. K., D. A. Walker, and H. A. Maier. 2006. NDVI patterns and phytomass distribution in the circumpolar Arctic. Remote Sensing of Environment 102:271-281. Raynolds, M. K., J. C. Comiso, D. A. Walker, and D. Verbyla. 2008. Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI. Remote Sensing of Environment 112:1884-1894. Raynolds, M. K. and D. A. Walker. 2009. The effects of deglaciation on circumpolar distribution of arctic vegetation. Canadian Journal of Remote Sensing 35:118-129. Raynolds, M. K. 2009. A geobotanical analysis of circumpolar arctic vegetation, climate, and substrate. PhD Thesis, University of Alaska, Fairbanks. Raynolds, M. K., D. A. Walker, H. E. Epstein, J. E. Pinzon, and C. J. Tucker. 2012. A new estimate of tundra-biome phytomass from trans-Arctic field data and AVHRR NDVI. Remote Sensing Letters 3:403-411. Raynolds, M. K., D. A. Walker, A. Balser, C. Bay, M. W. Campbell, M. M. Cherosov, F. J. A. Daniëls, P. B. Eidesen, K. A. Ermokhina, G. V. Frost, B. Jedrzejek, M. T. Jorgenson, B. E. Kennedy, S. S. Kholod, I. A. Lavrinenko, O. Lavrinenko, B. Magnússon, S. Metúsalemsson, I. Olthof, I. N. Pospelov, E. B. Pospelova, D. Pouliot, V. Y. Razzhivin, G. Schaepman-Strub, J. Šibík, M. Y. Telyatnikov, and E. Troeva. 2019. A raster version of the Circumpolar Arctic Vegetation Map (CAVM). Remote Sensing of Environment 232:111297. Walker, D. A., M. K. Raynolds, F. J. A. Daniels, E. Einarsson, A. Elvebakk, W. A. Gould, A. E. Katenin, S. S. Kholod, C. J. Markon, E. S. Melnikov, N. G. Moskalenko, S. S. Talbot, B. A. Yurtsev, and CAVM Team. 2005. The Circumpolar Arctic Vegetation Map. Journal of Vegetation Science 16:267-282.

Map of the position of question particles in polar questions.

Det mest detaljerte, heldekkende kartdatasettet for landarealet av Svalbard. Produktet har et innhold som i all hovedsak tilsvarer kartserien Svalbard 1:100 000. Produktet oppdateres flere ganger årlig.

The most detailed Svalbard land covering map dataset. The product has a content which on the whole corresponds to the map series Svalbard 1:100 000. The product is updated several times yearly.

Quality

Deler av kartdataene er av eldre dato og ikke egnet for navigasjon. Datakvaliteten er angitt på objektnivå i kartdatasettene (SOSI-egenskapene målemetode og nøyaktighet). Høydeangivelse på punkt- og nodenivå er kun angitt i SOSI-filene.

Parts of the map data are of older dates and not suited for navigation. Data quality is indicated on object level in the map datsets (the SOSI attributes "målemetode" (measuring method) and "nøyaktighet" (accuracy). Elevation on point and node level is present only in the SOSI files.

This dynamic imagery layer features Landsat 8 and Landsat GLS imagery for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.To view this imagery layer, you'll want to add it to a map that is using the Polar projection of WGS_1984_EPSG_Alaska_Polar_Stereographic, for example the Arctic Ocean Basemap or the Arctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery above 82°30’N due to the orbit of the satellite.Geographic CoverageArctic RegionTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA).Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created. Available functions on this layer include:Agriculture with DRA – Bands shortwave IR-1, near-IR, blue (6, 5, 2) with dynamic range adjustment applied on apparent reflectance. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.NDSI Colorized – Normalized difference Snow index (NDSI) with color map, computed as (b3-b6)/(b3+b6) on apparent reflectance. Dark blue represents dense snow, yellow and green areas represent clouds.Bathymetric with DRA – Bands red, green, coastal/aerosol (4, 3, 1) with dynamic range adjustment. Useful in bathymetric mapping applications.Color Infrared with DRA – Bands near-IR, red, green (5, 4, 3) with dynamic range adjustment. Healthy vegetation is bright red while stressed vegetation is dull red.Geology with DRA – Bands shortwave IR-1, near-IR, blue (7, 6, 2) with dynamic range adjustment. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Natural Color with DRA – Natural Color bands red, green, blue (4, 3, 2) displayed with dynamic range adjustmentShort-wave Infrared with DRA – Bands shortwave IR-2, shortwave IR-1, red (7, 6, 4) with dynamic range adjustmentAgriculture – Bands shortwave IR-1, near-IR, blue (6, 5, 2) with fixed stretch applied on apparent reflectance. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Bathymetry – Bands red, green, coastal/aerosol (4, 3, 1) with fixed stretch applied on apparent reflectance. Useful in bathymetric mapping applications.Color Infrared – Bands near-IR, red, green (5, 4, 3) with a fixed stretch. Healthy vegetation is bright red while stressed vegetation is dull red.Geology – Bands shortwave IR-1, near-IR, blue (7, 6, 2) with a fixed stretch. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Natural Color – Natural Color bands red, green, blue (4, 3, 2) displayed with a fixed stretch.Short-wave Infrared – Bands shortwave IR-2, shortwave IR-1, red (7, 5, 4) with a fixed stretchNormalized Difference Moisture Index Colorized – Normalized Difference Moisture Index with color map, computed as (b5 - b6)/(b5 + b6). Wetlands and moist areas are blues, and dry areas in deep yellow and brownNDSI Raw – Normalized difference Snow index (NDSI) computed as (b3 - b6) / (b3 + b6)NDVI Raw – Normalized difference vegetation index (NDVI) computed as (b5 - b4) / (b5 + b4)NBR Raw – Normalized Burn Ratio (NBR) computed as (b5 - b7) / (b5 + b7)Multispectral BandsThe table below lists all available multispectral OLI bands. Natural Color with DRA consumes bands 4,3,2

Band

Description

Wavelength (µm)

Spatial Resolution (m)

1

Coastal aerosol

0.43 - 0.45

30

2

Blue

0.45 - 0.51

30

3

Green

0.53 - 0.59

30

4

Red

0.64 - 0.67

30

5

Near Infrared (NIR)

0.85 - 0.88

30

6

SWIR 1

1.57 - 1.65

30

7

SWIR 2

2.11 - 2.29

30

8

Cirrus (in OLI this is band 9)

1.36 - 1.38

30

9

QA Band (available with Collection 1)*

NA

30

*More about the Quality Assessment Band The layer also provides access to TIRS bands as follows: BandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Arctic is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, distinguishing between areas with lichen- and shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the new map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data (reflectance and Normalized Difference Vegetation Index (NDVI)) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including many of the original authors of the CAVM from Canada, Greenland (Denmark), Iceland, Norway (including Svalbard), Russia, and the United States (U.S.). The analysis presented here summarizes the area, geographical distribution, elevation, summer temperatures, and NDVI of the map units. The greater spatial resolution of the Raster CAVM allowed more detailed mapping of water-bodies and mountainous areas. It portrays coastal-inland gradients, and better reflects the heterogeneity of vegetation type distribution than the original CAVM. Accuracy assessment of random 1-kilometer (km) pixels interpreted from 6 Landsat scenes showed an average of 70 percent (%) accuracy, up from 39 % for the original CAVM. The distribution of shrub-dominated types changed the most, with more prostrate shrub tundra mapped in mountainous areas, and less low shrub tundra in lowland areas. This improved mapping is important for quantifying existing and potential changes to land cover, a key environmental indicator for modeling and monitoring ecosystems. The Raster CAVM was released in 2019. Raster map data are available for download from Menedeley Data (DOI: 10.17632/c4xj5rv6kv.2). This data record contains PDFs a 36x36-inch print version of the map at at 1:7,000,000. The print map is illustrated with photographs of representative plant communities and species for each of the 16 map units, data on the area of each unit, and information on the making of raster CAVM. The press-quality version includes a 1/8-inch bleed on all sides to allow the map to printed at 36.25 square inches and trimmed to produce a "full bleed" map with color extending to the edge on all sides.

These layers are polar climatological and other summary environmental layers that may be useful for purposes such as general modelling, regionalisation, and exploratory analyses. All of the layers in this collection are provided on a consistent 0.1-degree grid, which covers -180 to 180E, 80S to 30S (Antarctic) and 45N to 90N (Arctic). As far as practicable, each layer is provided for both the Arctic and Antarctic regions. Where possible, these have been derived from the same source data; otherwise, source data have been chosen to be as compatible as possible between the two regions. Some layers are provided for only one of the two regions.

Each data layer is provided in netCDF and ArcInfo ASCII grid format. A png preview map of each is also provided.

Processing details for each layer:

Bathymetry File: bathymetry Measured and estimated seafloor topography from satellite altimetry and ship depth soundings. Antarctic: Source data: Smith and Sandwell V13.1 (Sep 4, 2010) Processing steps: Depth data subsampled from original 1-minute resolution to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation. Reference: Smith, W. H. F., and D. T. Sandwell (1997) Global seafloor topography from satellite altimetry and ship depth soundings. Science 277:1957-1962. http://topex.ucsd.edu/WWW_html/mar_topo.html Arctic: Source data: ETOPO1 Processing steps: Depth data subsampled to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation on polar stereographic projection. Reference: Amante, C. and B. W. Eakins, ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24, 19 pp, March 2009. http://www.ngdc.noaa.gov/mgg/global/global.html

Bathymetry slope File: bathymetry_slope Slope of sea floor, derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Slope calculated on 0.1-degree gridded depth data (above). Calculated using the equation given by Burrough, P. A. and McDonell, R.A. (1998) Principles of Geographical Information Systems (Oxford University Press, New York), p. 190 (see http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=How%20Slope%20works)

CAISOM model-derived variables Variables derived from the CAISOM ocean model. This model has been developed by Ben Galton-Fenzi (AAD and ACE-CRC), and is based on the Regional Ocean Modelling System (ROMS). It has circum-Antarctic coverage out to 50S, with a spatial resolution of approximately 5km. The values here are averaged over 12 snapshots from the model, each separated by 2 months. These parameters should be treated as experimental.

Reference: Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214

Floor current speed File: caisom_floor_current_speed Current speed near the sea floor.

Floor temperature File: caisom_floor_temperature Potential temperature near the sea floor.

Floor vertical velocity File: caisom_floor_vertical_velocity Vertical water velocity near the sea floor.

Surface current speed File: caisom_surface_current_speed Near-surface current speed (at approximately 2.5m depth)

Chlorophyll summer File: chl_summer_climatology Source data: Near-surface chl-a summer climatology from MODIS Aqua Antarctic: Climatology spans the 2002/03 to 2009/10 austral summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Arctic: Climatology spans the 2002 to 2009 boreal summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Reference: Feldman GC, McClain CR (2010) Ocean Color Web, MODIS Aqua Reprocessing, NASA Goddard Space Flight Center. Eds. Kuring, N., Bailey, S.W. https://oceancolor.gsfc.nasa.gov/

Distance to Antarctica File: distance_antarctica Distance to nearest part of Antarctic continent (Antarctic only) Source data: A modified version of ESRI's world map shapefile Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to nearest seabird breeding colony (Antarctic only) File: distance_colony Antarctic source data: Inventory of Antarctic seabird breeding sites, collated by Eric Woehler. http://data.aad.gov.au/aadc/biodiversity/display_collection.cfm?collection_id=61. Processing steps: The closest distance of each grid point to the colonies was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to maximum winter sea ice extent File: distance_max_ice_edge Source data: SMMR-SSM/I passive microwave estimates of daily sea ice concentration from the National Snow and Ice Data Center (NSIDC). Processing steps: Antarctic: Mean maximum winter sea ice extent was derived from daily estimates of sea ice concentration as described at https://data.aad.gov.au/metadata/records/sea_ice_extent_winter. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Arctic: The median March winter sea ice extent was obtained from the NSIDC at http://nsidc.org/data/g02135.html. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Reference: Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated 2008. Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I passive microwave data. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. tp://nsidc.org/data/nsidc-0051.html

Distance to shelf break File: distance_shelf Distance to nearest area of sea floor of depth 500m or less. Derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points in less than 500m of water (i.e. over the shelf) were assigned negative distances. See also distance to upper slope

Distance to subantarctic islands (Antarctic only) File: distance_subantarctic_islands Distance to nearest land mass north of 65S (includes land masses of e.g. South America, Africa, Australia, and New Zealand). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to canyon File: distance_to_canyon Distance to the axis of the nearest canyon (Antarctic only) Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances to nearest canyon axis calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. NOTE: source data extend only as far north as 45S. Do not rely on this layer near or north of 45S. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

Distance to polynya File: distance_to_polynya Distance to the nearest polynya area (Antarctic only) Source data: AMSR-E satellite estimates of daily sea ice concentration at 6.25km resolution Processing steps: The seaice_gt_85 layer (see below) was used. Pixels which were (on average) covered by sea ice for less than 35% of the year were identified. The distance from each grid point on the 0.1-degree grid to the nearest such polynya pixel was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. (NB the threshold of 35% was chosen to give a good empirical match to the polynya locations identified by Arrigo and van Dijken (2003), although the results were not particularly sensitive to the choice of threshold. Reference: Arrigo KR, van Dijken GL (2003) Phytoplankton dynamics within 37 Antarctic coastal polynya systems. Journal of Geophysical Research, 108, 3271. http://dx.doi.org/10.1029/2002JC001739

Distance to upper slope (Antarctic only) File: distance_upper_slope Distance to the "upper slope" geomorphic feature from the Geoscience Australia geomorphology data set. This is probably a better indication of the distance to the Antarctic continental shelf break than the "distance to shelf break" data (above). Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points inside of an "upper slope" polygon were assigned negative distances. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

Fast ice File: fast_ice The average proportion of the year for which landfast sea ice is present in a location Source data: 20-day composite records of East Antarctic landfast sea-ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: The average proportion of the year for which each pixel was covered by landfast sea ice was calculated as an average across 2001--2008. Data were regridded to the 0.1-degree grid using bilinear interpolation.

Distance to fast ice File: distance_to_fast_ice Distance to the nearest location where fast ice is typically present. Source data: 20-day composite records of East Antarctic landfast sea ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: Pixels in the landfast sea ice data that were associated with fast ice presence for more than half of the

This data set provides a landcover map with 16 landcover classes for the northern coastal plain of the the Arctic National Wildlife Refuge (ANWR) on the North Slope of Alaska. The map was derived from Landsat Thematic Mapper (Landsat TM) data, Digital Elevation Models (DEMs), aerial photographs, existing maps, and extensive ground-truthing. The data used to derive the map cover the period 1982 to 1993.

The map extent is the Arctic, defined as the Arctic Bioclimate Zone, the area of the Earth with tundra vegetation and an Arctic climate and Arctic flora. It excludes tundra regions that lack an Arctic flora, such as the boreal oceanic areas of Iceland, the Aleutian Island, and the alpine tundra regions south of latitudinal tree line. Tundra is a physiognomic descriptor of low-growing vegetation beyond the cold limit of tree growth, both at high elevation (alpine tundra) and at high latitude (arctic tundra). Tundra vegetation types are composed of various combinations of herbaceous plants, shrubs, mosses and lichens. Tree line defines the southern limit of the Arctic Bioclimate Zone. In some regions of the Arctic, especially Canada and Chukotka, the forest tundra transition is gradual and interpretation of treeline directly from the AVHRR imagery was not possible. Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Elvebakk, A. 1999. Bioclimate delimitation and subdivisions of the Arctic. Pages 81-112 in I. Nordal and V. Y. Razzhivin, editors. The Species Concept in the High North - A Panarctic Flora Initiative. The Norwegian Academy of Science and Letters, Oslo. Yurtsev, B. A. 1994. Floristic divisions of the Arctic. Journal of Vegetation Science 5:765-776.

Mapped polygons at 1:7.5 million scale contain many vegetation types. The map portrays the zonal vegetation within each mapped polygon. Zonal sites are areas where the vegetation develops under the prevailing climate, uninfluenced by extremes of soil moisture, snow, soil chemistry, or disturbance, and are generally flat or gently sloping, moderately drained sites, with fine-grained soils (Vysotsky 1927). Large areas of azonal vegetation that are dependent on specific soil or hydrological conditions, such as mountain ranges and large wetlands, were also mapped. The legend contains five broad physiognomic categories: B — barrens, G — graminoid-dominated tundras, P — prostrate-shrub-dominated tundras, S — erect-shrub-dominated tundras, and W — wetlands. These are subdivided into 15 vegetation mapping units with numeric codes added to the alphabetic codes. The mapping units are named according to dominant plant functional types except in the mountains where complexes of vegetation are named according to the dominant bedrock (carbonate and noncarbonate mountain complexes). The coloring scheme of the map is suggestive of the physiognomy of the vegetation. The plant functional types are based on a variety of criteria including growth form (e.g., graminoids, shrubs), size (e.g., dwarf and low shrubs), and taxonomical status (e.g., sedges, rushes, grasses). The legend takes into special consideration the stature of woody shrubs, which is a major diagnostic feature of zonal vegetation in the Arctic (Edlund and Alt 1989, Walker et al. 2002, Yurtsev 1994). Very steep bioclimate gradients occur in mountains, so these areas are mapped as complexes of elevation belts. Mountainous areas of the map are shown with hachures; the background color indicates the nature of the bedrock, and the color of the hachures indicate the bioclimate subzone at the base of the mountains. Back to Alaska Arctic Tundra Vegetation Map (Raynolds et al. 2006) Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI , Bioclimate Subzone, Elevation, False Color-Infrared CIR, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Edlund, S. A. and B. T. Alt. 1989. Regional congruence of vegetation and summer climate patterns in the Queen Elizabeth Islands, Northwest Territories, Canada. Arctic 42:3-23. Vysotsky, G.N. 1927. Theses on soil and moisture (conspectus and terminology). Lesovedenie (eds.) Sbornik Lesnogo Obschestva v Leningrade. Leningrad. pp. 67-79 (In Russian). Walker, D. A., W. A. Gould, and M. K. Raynolds. 2002. The Circumpolar Arctic Vegetation Map: Environmental controls, AVHRR-derived base maps, and integrated mapping procedures. International Journal of Remote Sensing 23:2551-2570. Yurtsev, B. A. 1994. Floristic divisions of the Arctic. Journal of Vegetation Science 5:765-776.