Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series
The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of a...
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2021
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ftdoajarticles:oai:doaj.org/article:c59e1b62ad84433284d01967e50388ee 2024-01-07T09:43:23+01:00 Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series Philipp Hochreuther Niklas Neckel Nathalie Reimann Angelika Humbert Matthias Braun 2021-01-01T00:00:00Z https://doi.org/10.3390/rs13020205 https://doaj.org/article/c59e1b62ad84433284d01967e50388ee EN eng MDPI AG https://www.mdpi.com/2072-4292/13/2/205 https://doaj.org/toc/2072-4292 doi:10.3390/rs13020205 2072-4292 https://doaj.org/article/c59e1b62ad84433284d01967e50388ee Remote Sensing, Vol 13, Iss 2, p 205 (2021) supraglacial lakes 79 °N Sentinel-2 lake area automated detection Greenland Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13020205 2023-12-10T01:47:33Z The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km 2 area at the 79 °N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m 2 . Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km 2 and a maximum individual lake size of 30 km 2 . With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks. Article in Journal/Newspaper glacier Greenland Directory of Open Access Journals: DOAJ Articles Greenland Remote Sensing 13 2 205 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
supraglacial lakes 79 °N Sentinel-2 lake area automated detection Greenland Science Q |
spellingShingle |
supraglacial lakes 79 °N Sentinel-2 lake area automated detection Greenland Science Q Philipp Hochreuther Niklas Neckel Nathalie Reimann Angelika Humbert Matthias Braun Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series |
topic_facet |
supraglacial lakes 79 °N Sentinel-2 lake area automated detection Greenland Science Q |
description |
The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km 2 area at the 79 °N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m 2 . Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km 2 and a maximum individual lake size of 30 km 2 . With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks. |
format |
Article in Journal/Newspaper |
author |
Philipp Hochreuther Niklas Neckel Nathalie Reimann Angelika Humbert Matthias Braun |
author_facet |
Philipp Hochreuther Niklas Neckel Nathalie Reimann Angelika Humbert Matthias Braun |
author_sort |
Philipp Hochreuther |
title |
Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series |
title_short |
Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series |
title_full |
Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series |
title_fullStr |
Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series |
title_full_unstemmed |
Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series |
title_sort |
fully automated detection of supraglacial lake area for northeast greenland using sentinel-2 time-series |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13020205 https://doaj.org/article/c59e1b62ad84433284d01967e50388ee |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
glacier Greenland |
genre_facet |
glacier Greenland |
op_source |
Remote Sensing, Vol 13, Iss 2, p 205 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/2/205 https://doaj.org/toc/2072-4292 doi:10.3390/rs13020205 2072-4292 https://doaj.org/article/c59e1b62ad84433284d01967e50388ee |
op_doi |
https://doi.org/10.3390/rs13020205 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
2 |
container_start_page |
205 |
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1787424643998547968 |