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Article

Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany
2
Institute of Geography and Geology, University Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany
*
Author to whom correspondence should be addressed.
Received: 5 March 2020 / Revised: 4 April 2020 / Accepted: 6 April 2020 / Published: 8 April 2020
Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km2). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions. View Full-Text
Keywords: Antarctica; Antarctic ice sheet; supraglacial lakes; surface melt; hydrology; ice sheet dynamics; sentinel-2; remote sensing; random forest; machine learning Antarctica; Antarctic ice sheet; supraglacial lakes; surface melt; hydrology; ice sheet dynamics; sentinel-2; remote sensing; random forest; machine learning
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MDPI and ACS Style

Dirscherl, M.; Dietz, A.J.; Kneisel, C.; Kuenzer, C. Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach. Remote Sens. 2020, 12, 1203. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071203

AMA Style

Dirscherl M, Dietz AJ, Kneisel C, Kuenzer C. Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach. Remote Sensing. 2020; 12(7):1203. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071203

Chicago/Turabian Style

Dirscherl, Mariel, Andreas J. Dietz, Christof Kneisel, and Claudia Kuenzer. 2020. "Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach" Remote Sensing 12, no. 7: 1203. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071203

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