Date (Creation)

2022-07-14

Date (Revision)

2022-07-14

Date (Publication)

2022-07-14

Date (released)

2022-07-14

Edition

1.0

Unique resource identifier

https://doi.org/10.5285/5a5ee38c-e296-48a2-85d2-e29db66e5e24

Codespace

doi

Unique resource identifier

GB/NERC/BAS/PDC/01652

Codespace

https://data.bas.ac.uk/

Unique resource identifier

NE/S007458/1

Codespace

award

Other citation details

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

Credit

No credit.

Status

Completed

Point of contact

Organisation name	Individual name	Electronic mail address	Role
School of Geography, University of Leeds	Stringer, Christopher		Author
NERC EDS UK Polar Data Centre		PDCServiceDesk@bas.ac.uk	Point of contact

Maintenance and update frequency

As needed

Maintenance note

Completed

Global Change Master Directory (GCMD) Science Keywords

• 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

• Antarctica

• Google Earth Engine

• Land cover

• Landsat

• Sediment

• Vegetation

Place

• South Georgia Antarctica

• James Ross Archipelago Antarctica

• Byers Peninsula Antarctica

• McMurdo Dry Valleys Antarctica

• Deception Island Antarctica

• Alexander Island Antarctica

GEMET - INSPIRE themes, version 1.0

• Land cover
• Habitats and biotopes

Access constraints

Other restrictions

Other constraints

no limitations to public access

Access constraints

Other restrictions

Other constraints

no limitations

Use constraints

License

Other constraints

Open Government Licence v3.0

Use constraints

Other restrictions

Other constraints

No restrictions apply.

Unique resource identifier

doi

Codespace

doi

Association Type

dependency

Unique resource identifier

url

Codespace

url

Association Type

Cross reference

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Unique resource identifier

doi

Codespace

doi

Association Type

Cross reference

Spatial representation type

Text, table

Language

English

Character set

UTF8

Topic category

• Environment
• Geoscientific information
• Imagery base maps earth cover

Begin date

2016-02-02

End date

2020-02-09

Supplemental Information

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.

Title

European Petroleum Survey Group (EPSG) Geodetic Parameter Registry

Date (Publication)

2008-11-12

Cited responsible party

Organisation name	Individual name	Electronic mail address	Role
European Petroleum Survey Group		EPSGadministrator@iogp.org	Publisher

Unique resource identifier

urn:ogc:def:crs:EPSG::3031

Version

6.18.3

Hierarchy level

Dataset

Statement

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 %.

Metadata

File identifier

5a5ee38c-e296-48a2-85d2-e29db66e5e24 XML

Metadata language

English

Character set

UTF8

Hierarchy level

Dataset

Hierarchy level name

dataset

Date stamp

2022-07-14

Metadata standard name

ISO 19115 Geographic Information - Metadata

Metadata standard version

ISO 19115:2003(E)

Metadata author

Organisation name	Individual name	Electronic mail address	Role
NERC EDS UK Polar Data Centre		polardatacentre@bas.ac.uk	Point of contact