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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ftmdpi:oai:mdpi.com:/2072-4292/14/10/2377/ 2023-08-20T04:06:44+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 agris 2022-05-14 application/pdf https://doi.org/10.3390/rs14102377 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs14102377 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 10; Pages: 2377 snow line altitude (SLA) Landsat glacier equilibrium line altitude (ELA) Google Earth Engine (GEE) Text 2022 ftmdpi https://doi.org/10.3390/rs14102377 2023-08-01T05:03:20Z 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 R2 > 0.5, and the other five glaciers yield R2 > 0.3 for their respective SCR–AAR relationships. Furthermore, 10 of these glaciers yield R2 > 0.5 and the other three glaciers yield R2 > 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 better ... Text glacier* MDPI Open Access Publishing Canada Ela ENVELOPE(9.642,9.642,63.170,63.170) Remote Sensing 14 10 2377 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
snow line altitude (SLA) Landsat glacier equilibrium line altitude (ELA) Google Earth Engine (GEE) |
spellingShingle |
snow line altitude (SLA) Landsat glacier equilibrium line altitude (ELA) Google Earth Engine (GEE) 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) |
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 R2 > 0.5, and the other five glaciers yield R2 > 0.3 for their respective SCR–AAR relationships. Furthermore, 10 of these glaciers yield R2 > 0.5 and the other three glaciers yield R2 > 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 better ... |
format |
Text |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14102377 |
op_coverage |
agris |
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; Volume 14; Issue 10; Pages: 2377 |
op_relation |
https://dx.doi.org/10.3390/rs14102377 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
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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