Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform
Glacier snow line altitude (SLA) at the end of the ablation season is an indicator of the equilibrium line altitude (ELA), which is a key parameter for calculating and assessing glacier mass balance. Here, we present an automated algorithm to classify bare ice and snow cover on glaciers using Landsa...
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2022
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ftdoajarticles:oai:doaj.org/article:aae2a82a225647beb78cce7c4fd53673 2023-05-15T16:22:31+02:00 Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform Xiang Li Ninglian Wang Yuwei Wu 2022-05-01T00:00:00Z https://doi.org/10.3390/rs14102377 https://doaj.org/article/aae2a82a225647beb78cce7c4fd53673 EN eng MDPI AG https://www.mdpi.com/2072-4292/14/10/2377 https://doaj.org/toc/2072-4292 doi:10.3390/rs14102377 2072-4292 https://doaj.org/article/aae2a82a225647beb78cce7c4fd53673 Remote Sensing, Vol 14, Iss 2377, p 2377 (2022) snow line altitude (SLA) Landsat glacier equilibrium line altitude (ELA) Google Earth Engine (GEE) Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14102377 2022-12-31T02:36:36Z Glacier snow line altitude (SLA) at the end of the ablation season is an indicator of the equilibrium line altitude (ELA), which is a key parameter for calculating and assessing glacier mass balance. Here, we present an automated algorithm to classify bare ice and snow cover on glaciers using Landsat series images and calculate the minimum annual glacier snow cover ratio (SCR) and maximum SLA for reference glaciers during the 1985–2020 period in Google Earth Engine. The calculated SCR and SLA values are verified using the observed glacier accumulation area ratio (AAR) and ELA. We select 14 reference glaciers from High Mountain Asia (HMA), the Caucasus, the Alps, and Western Canada, which represent four mountainous regions with extensive glacial development in the northern hemisphere. The SLA accuracy is ~73%, with a mean uncertainty of ±24 m, for 13 of the reference glaciers. Eight of these glaciers yield R 2 > 0.5, and the other five glaciers yield R 2 > 0.3 for their respective SCR–AAR relationships. Furthermore, 10 of these glaciers yield R 2 > 0.5 and the other three glaciers yield R 2 > 0.3 for their respective SLA–ELA relationships, which indicate that the calculated SLA from this algorithm provides a good fit to the ELA observations. However, Careser Glacier yields a poor fit between the SLA calculations and ELA observations owing to tremendous surface area changes during the analyzed time series; this indicates that glacier surface shape changes due to intense ablation will lead to a misclassification of the glacier surface, resulting in deviations between the SLA and ELA. Furthermore, cloud cover, shadows, and the Otsu method limitation will further affect the SLA calculation. The post-2000 SLA values are better than those obtained before 2000 because merging the Landsat series images reduces the temporal resolution, which allows the date of the calculated SLA to be closer to the date of the observed ELA. From a regional perspective, the glaciers in the Caucasus, HMA and the Alps yield ... Article in Journal/Newspaper glacier* Directory of Open Access Journals: DOAJ Articles Canada Ela ENVELOPE(9.642,9.642,63.170,63.170) Remote Sensing 14 10 2377 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
snow line altitude (SLA) Landsat glacier equilibrium line altitude (ELA) Google Earth Engine (GEE) Science Q |
spellingShingle |
snow line altitude (SLA) Landsat glacier equilibrium line altitude (ELA) Google Earth Engine (GEE) Science Q Xiang Li Ninglian Wang Yuwei Wu Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform |
topic_facet |
snow line altitude (SLA) Landsat glacier equilibrium line altitude (ELA) Google Earth Engine (GEE) Science Q |
description |
Glacier snow line altitude (SLA) at the end of the ablation season is an indicator of the equilibrium line altitude (ELA), which is a key parameter for calculating and assessing glacier mass balance. Here, we present an automated algorithm to classify bare ice and snow cover on glaciers using Landsat series images and calculate the minimum annual glacier snow cover ratio (SCR) and maximum SLA for reference glaciers during the 1985–2020 period in Google Earth Engine. The calculated SCR and SLA values are verified using the observed glacier accumulation area ratio (AAR) and ELA. We select 14 reference glaciers from High Mountain Asia (HMA), the Caucasus, the Alps, and Western Canada, which represent four mountainous regions with extensive glacial development in the northern hemisphere. The SLA accuracy is ~73%, with a mean uncertainty of ±24 m, for 13 of the reference glaciers. Eight of these glaciers yield R 2 > 0.5, and the other five glaciers yield R 2 > 0.3 for their respective SCR–AAR relationships. Furthermore, 10 of these glaciers yield R 2 > 0.5 and the other three glaciers yield R 2 > 0.3 for their respective SLA–ELA relationships, which indicate that the calculated SLA from this algorithm provides a good fit to the ELA observations. However, Careser Glacier yields a poor fit between the SLA calculations and ELA observations owing to tremendous surface area changes during the analyzed time series; this indicates that glacier surface shape changes due to intense ablation will lead to a misclassification of the glacier surface, resulting in deviations between the SLA and ELA. Furthermore, cloud cover, shadows, and the Otsu method limitation will further affect the SLA calculation. The post-2000 SLA values are better than those obtained before 2000 because merging the Landsat series images reduces the temporal resolution, which allows the date of the calculated SLA to be closer to the date of the observed ELA. From a regional perspective, the glaciers in the Caucasus, HMA and the Alps yield ... |
format |
Article in Journal/Newspaper |
author |
Xiang Li Ninglian Wang Yuwei Wu |
author_facet |
Xiang Li Ninglian Wang Yuwei Wu |
author_sort |
Xiang Li |
title |
Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform |
title_short |
Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform |
title_full |
Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform |
title_fullStr |
Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform |
title_full_unstemmed |
Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform |
title_sort |
automated glacier snow line altitude calculation method using landsat series images in the google earth engine platform |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14102377 https://doaj.org/article/aae2a82a225647beb78cce7c4fd53673 |
long_lat |
ENVELOPE(9.642,9.642,63.170,63.170) |
geographic |
Canada Ela |
geographic_facet |
Canada Ela |
genre |
glacier* |
genre_facet |
glacier* |
op_source |
Remote Sensing, Vol 14, Iss 2377, p 2377 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/10/2377 https://doaj.org/toc/2072-4292 doi:10.3390/rs14102377 2072-4292 https://doaj.org/article/aae2a82a225647beb78cce7c4fd53673 |
op_doi |
https://doi.org/10.3390/rs14102377 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
10 |
container_start_page |
2377 |
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1766010490957332480 |