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Article

Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform

by 1,2, 1,2,3,* and 1,2
1
Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi’an 710127, China
2
Institute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
3
Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Academic Editors: Anshuman Bhardwaj, Lydia Sam and Saeideh Gharehchahi
Remote Sens. 2022, 14(10), 2377; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102377
Received: 3 May 2022 / Accepted: 12 May 2022 / Published: 14 May 2022
(This article belongs to the Topic Cryosphere: Changes, Impacts and Adaptation)
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 results than those in Western Canada. This algorithm can be applied to large regions, such as HMA, to obtain snow line estimates where manual approaches are exhaustive and/or unfeasible. Furthermore, new optical data, such as that from Sentinel-2, can be incorporated to further improve the algorithm results. View Full-Text
Keywords: snow line altitude (SLA); Landsat; glacier; equilibrium line altitude (ELA); Google Earth Engine (GEE) snow line altitude (SLA); Landsat; glacier; equilibrium line altitude (ELA); Google Earth Engine (GEE)
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MDPI and ACS Style

Li, X.; Wang, N.; Wu, Y. Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform. Remote Sens. 2022, 14, 2377. https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102377

AMA Style

Li X, Wang N, Wu Y. Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform. Remote Sensing. 2022; 14(10):2377. https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102377

Chicago/Turabian Style

Li, Xiang, Ninglian Wang, and Yuwei Wu. 2022. "Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform" Remote Sensing 14, no. 10: 2377. https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102377

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