5a5ee38c-e296-48a2-85d2-e29db66e5e24 eng utf8 dataset dataset NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom polardatacentre@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact 2022-07-14 ISO 19115 Geographic Information - Metadata ISO 19115:2003(E) European Petroleum Survey Group (EPSG) Geodetic Parameter Registry 2008-11-12 publication European Petroleum Survey Group EPSGadministrator@iogp.org https://www.epsg-registry.org/ EPSG Geodetic Parameter Dataset The EPSG Geodetic Parameter Dataset is a structured dataset of Coordinate Reference Systems and Coordinate Transformations, accessible through this online registry. information publisher urn:ogc:def:crs:EPSG::3031 6.18.3 Contemporary (2016 - 2020) land cover classification across West Antarctica and the McMurdo Dry Valleys 2022-07-14 creation 2022-07-14 revision 2022-07-14 publication 2022-07-14 released 1.0 https://doi.org/10.5285/5a5ee38c-e296-48a2-85d2-e29db66e5e24 doi GB/NERC/BAS/PDC/01652 https://data.bas.ac.uk/ NE/S007458/1 award Please cite this item as: Stringer, C. (2022). Contemporary (2016 - 2020) land cover classification across West Antarctica and the McMurdo Dry Valleys (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/5a5ee38c-e296-48a2-85d2-e29db66e5e24 We present here the land cover classification across West Antarctica and the McMurdo Dry Valley produced from Landsat-8 Operational Land Imager (OLI) images of six proglacial regions of Antarctica at 30 m resolution, with an overall accuracy of 77.0 % for proglacial land classes. We conducted this classification using an unsupervised K-means clustering approach, which circumvented the need for training data and was highly effective at picking up key land classes, such as vegetation, water, and different sedimentary surfaces. This work is supported by the Leeds-York-Hull Natural Environment Research Council (NERC) Doctoral Training Partnership (DTP) Panorama under grant NE/S007458/1. The Ministry of Education, Youth and Sports of the Czech Republic project VAN 1/2022 and the Czech Antarctic Foundation funded fieldwork that contributed to part of this work. No credit. completed Stringer, Christopher School of Geography, University of Leeds Researcher author NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact asNeeded completed EARTH SCIENCE > Land Surface > Land Use/Land Cover > Land Cover EARTH SCIENCE > Biosphere > Vegetation EARTH SCIENCE > Land Surface > Erosion/Sedimentation EARTH SCIENCE > Land Surface > Land Use/Land Cover theme Global Change Master Directory (GCMD) Science Keywords 2026-01-27 publication 23 Global Change Data Center, Science and Exploration Directorate, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA) Greenbelt MD United States of America https://earthdata.nasa.gov/about/gcmd/global-change-master-directory-gcmd-keywords Global Change Master Directory (GCMD) Keywords The information provided on this page seeks to define how the GCMD Keywords are structured, used and accessed. It also provides information on how users can participate in the further development of the keywords. information publisher Antarctica Google Earth Engine Land cover Landsat Sediment Vegetation theme South Georgia Antarctica James Ross Archipelago Antarctica Byers Peninsula Antarctica McMurdo Dry Valleys Antarctica Deception Island Antarctica Alexander Island Antarctica place Land cover Habitats and biotopes theme GEMET - INSPIRE themes, version 1.0 2021-01-06 revision otherRestrictions no limitations to public access otherRestrictions no limitations license Open Government Licence v3.0 otherRestrictions No restrictions apply. doi doi dependency url url crossReference doi doi crossReference doi doi crossReference doi doi crossReference textTable eng utf8 environment geoscientificInformation imageryBaseMapsEarthCover -73.36 -61.14 -73.35 -69.39 2016-02-02 2020-02-09 It is recommended that careful attention be paid to the contents of any data, and that the author be contacted with any questions regarding appropriate use. If you find any errors or omissions, please report them to polardatacentre@bas.ac.uk. NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information distributor text/plain application/octet-stream application/vnd.shp image/tiff bytes 1073741824 https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=5a5ee38c-e296-48a2-85d2-e29db66e5e24 WWW:LINK-1.0-http--link Get Data Download data download bytes 1073741824 https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=5a5ee38c-e296-48a2-85d2-e29db66e5e24 WWW:LINK-1.0-http--link Get Data Download data download bytes 1073741824 https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=5a5ee38c-e296-48a2-85d2-e29db66e5e24 WWW:LINK-1.0-http--link Get Data Download data download bytes 1073741824 https://ramadda.data.bas.ac.uk/repository/entry/show?entryid=5a5ee38c-e296-48a2-85d2-e29db66e5e24 WWW:LINK-1.0-http--link Get Data Download data download dataset Methodology: We classified Landsat-8 OLI images acquired between 2016 and 2020 in Google Earth Engine (GEE) and ESRI ArcGIS Pro 2.6.0, primarily using K-means clustering. Suitable images had low cloud cover and limited snow cover. We selected six bands representing the visual and infrared wavelengths from the images for classification. We added three further bands to the image in the form of normalised difference snow index (NDSI), normalised difference vegetation index (NDVI), and normalised difference water index (NDWI). We topographically corrected the images. We conducted a principal component analysis of the images in GEE and the first three components. We used a hierarchical approach to image classification. A first order land classification of "land", "ice", and "water" informed the subdivision of each of these classes in a second, more detailed, analysis of the dominant land cover types. To produce these broad land classes, we used a K-means clustering algorithm in GEE to split each image into 75 (K value = 75) discrete clusters. The clusters are determined using the spectral information of each image, based on 500,000 randomly selected sampling points. We assigned each of these sections a first order class in ArcGIS by visually inspecting the image they were derived from. In some cases, we could not easily assign a cluster a first order class. This was usually because a cluster had conflated shadow with dark seawater. To address this, we split these clusters using a slope threshold of 3o, with pixels <3o being assigned as water. Where this process resulted in obvious misclassification we used a random forest classifier to differentiate between water, land and ice. Some pixels were covered entirely by very dark shadows or clouds and, therefore, we could not classify them; these were assigned "No data". We used this first order land classification to subset each image in GEE accordingly, and then to cluster these resulting images into 40 discrete groups (K = 40). We interpreted these clusters to manually assign each of them a final land classification. Our first-order land class was subset into five classes "Bedrock", "Coarse/wet sediment", "Fine & dry sediment", "Vegetation". The water class subset into. "Water" and "Turbid water", while the ice class subset into "Ice" and "Wet ice". In cases where clouds partially obscured land, we assigned pixels to the more general class of "Land (non-differentiated)". We produced ten land classes that describe eight distinct surface types, plus partially obscured land (Land (non-differentiated)), and surfaces totally obscured by clouds or shadows (No data). Data collection: Data was produced and analysed in Google Earth Engine and ESRI ArcGIS Pro 2.6.0 Data quality: Accuracy assessed using the methods of: Olofsson, P., Foody, G. M., Stehman, S. V, and Woodcock, C. E.: Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation, Remote Sens. Environ., 129, 122-131, https://doi.org/10.1016/j.rse.2012.10.031, 2013. Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V, Woodcock, C. E., and Wulder, M. A.: Good practices for estimating area and assessing accuracy of land change, Remote Sens. Environ., 148, 42-57, https://doi.org/10.1016/j.rse.2014.02.015, 2014. We found our classification to have an overall accuracy of 95.9 % overall. When just the proglacial land classes are considered, we have an accuracy of 77.0 %. asNeeded completed