Abstract:
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.
Keywords:
Antarctica, Google Earth Engine, Land cover, Landsat, Sediment, Vegetation
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
| Access Constraints: | Under embargo until publication of the associated article. |
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| Use Constraints: | This data is covered by a UK Open Government Licence (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) |
| Creation Date: | 2022-07-14 |
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| Dataset Progress: | Complete |
| Dataset Language: | English |
| ISO Topic Categories: |
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| Parameters: |
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| Personnel: | |
| Name | PDC BAS |
| Role(s) | Metadata Author |
| Organisation | British Antarctic Survey |
| Name | Mr Christopher D Stringer |
| Role(s) | Investigator |
| Organisation | School of Geography, University of Leeds |
| Parent Dataset: | N/A |
| Reference: | Contemporary (2016-2020) land cover and 21st-century change across the major proglacial regions of West Antarctica and the McMurdo Dry Valleys, By Christopher D Stringer, Jonathan L Carrivick, Duncan J Qunicey and Daniel Nyvlt. List of Landsat 8 OLI images used for the study: Images acquired by Landsat-8 OLI: 02-02-2016 to 09-02-2020: * James Ross Island: LANDSAT/LC08/C01/T2_TOA/LC08_215105_20170204 LANDSAT/LC08/C01/T2_TOA/LC08_215105_20160202 * Dry Valleys: LANDSAT/LC08/C01/T2_TOA/LC08_056116_20191115 LANDSAT/LC08/C01/T2_TOA/LC08_061115_20191118 LANDSAT/LC08/C01/T2_TOA/LC08_061114_20191102 * Alexander Island LANDSAT/LC08/C01/T2_TOA/LC08_217110_20191209 LANDSAT/LC08/C01/T2_TOA/LC08_217111_20191107 LANDSAT/LC08/C01/T2_TOA/LC08_216110_20191218 * Deception Island LANDSAT/LC08/C01/T2_TOA/LC08_219104_20200209 * Byers Peninsula LANDSAT/LC08/C01/T2_TOA/LC08_219104_20200209 * South Georgia LANDSAT/LC08/C01/T2_TOA/LC08_205098_20180116 LANDSAT/LC08/C01/T2_TOA/LC08_207098_20180404 |
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| 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 %. |
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| Lineage: | 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). |
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| Temporal Coverage: | |
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| Start Date | 2016-02-02 |
| End Date | 2020-02-09 |
| Spatial Coverage: | |
| Latitude | |
| Southernmost | -73.35 |
| Northernmost | -69.39 |
| Longitude | |
| Westernmost | -73.36 |
| Easternmost | -61.14 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Latitude | |
| Southernmost | -63.02 |
| Northernmost | -62.39 |
| Longitude | |
| Westernmost | -61.91 |
| Easternmost | -60.53 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Latitude | |
| Southernmost | -63.2 |
| Northernmost | -62.71 |
| Longitude | |
| Westernmost | -61.17 |
| Easternmost | -60.08 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Latitude | |
| Southernmost | -78.56 |
| Northernmost | -76.22 |
| Longitude | |
| Westernmost | 155.87 |
| Easternmost | 166.68 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Latitude | |
| Southernmost | -65.2 |
| Northernmost | -63.12 |
| Longitude | |
| Westernmost | -59.9 |
| Easternmost | -55.14 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Latitude | |
| Southernmost | -57.58 |
| Northernmost | -51.41 |
| Longitude | |
| Westernmost | -41.85 |
| Easternmost | -31.27 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Data Resolution: | |
| Latitude Resolution | N/A |
| Longitude Resolution | N/A |
| Horizontal Resolution Range | 30 meters - < 100 meters |
| Vertical Resolution | N/A |
| Vertical Resolution Range | N/A |
| Temporal Resolution | N/A |
| Temporal Resolution Range | N/A |
| Location: | |
| Location | Antarctica |
| Detailed Location | South Georgia |
| Location | Antarctica |
| Detailed Location | James Ross Archipelago |
| Location | Antarctica |
| Detailed Location | Byers Peninsula |
| Location | Antarctica |
| Detailed Location | McMurdo Dry Valleys |
| Location | Antarctica |
| Detailed Location | Deception Island |
| Location | Antarctica |
| Detailed Location | Alexander Island |
| Data Collection: | Data was produced and analysed in Google Earth Engine and ESRI ArcGIS Pro 2.6.0 |
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| Distribution: | |
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| Distribution Media | Online Internet (HTTP) |
| Distribution Size | 1 GB |
| Distribution Format | SHP |
| Fees | N/A |
| Data Storage: | 6 tif files containing the land classifications. (994 MB). These files are single band tifs, with the pixel values describing distinct land classes: 0 = No data 1 = Water 2 = Turbid water 3 = Wet ice 4 = Ice 5 = Land (non-differentiated) 6 = Bedrock 7 = Coarse sediment 8 = Fine sediment 9 = Vegetation Shapefiles used in the land classification accuracy assessment are also provided. These include points that show pixels that were accuratetely described (178 kb) and inaccurately described (28 kb). Also provided is a populated grid used to produce a spatially mapped accuracy assessment grid (4.58 mb). The coastlines used for this study were derived from the SCAR Antarctic Digital Database, accessed 2020 (https://doi.org/10.5285/bc71347d-298a-4df3-88b0-cb9a908db166) (5.98 MB) |