Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-Series
Time-series glacier change analysis that is closely linked to global climate patterns has drawn increasing attention. However, few studies focused on regional glacier mapping with high spatial resolution and accuracy. This study combined a lightweight DeeplabV3+ model and activation thres...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10969556/ |
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| author | Lihao Chen Wei Xu Zhen Cao Jiaqi Zhang Zhizhong Kang |
| author_facet | Lihao Chen Wei Xu Zhen Cao Jiaqi Zhang Zhizhong Kang |
| author_sort | Lihao Chen |
| collection | DOAJ |
| description | Time-series glacier change analysis that is closely linked to global climate patterns has drawn increasing attention. However, few studies focused on regional glacier mapping with high spatial resolution and accuracy. This study combined a lightweight DeeplabV3+ model and activation threshold optimization strategy for regional glacier mapping and change analysis in the Gangshika region of Qilian Mountain, mainly using high-resolution satellite images from 2012 to 2023. For the extraction of glaciers with different scales, multiscale contextual information was represented through Atrous spatial pyramid pooling module. The predicted probability maps were first obtained. And the optimized activation thresholds were selected to determine which areas were glaciers. The accuracies of extracted glacier boundaries from 2012 to 2023 fluctuated between 82% and 96%, with an averaged value of 93%. Considering that the quality of images in 2012–2014 and corresponding glacier boundary accuracy were not very good, the change analysis was just conducted from 2015 to 2023. As of 2023, the Gangshika region hosts 45 glaciers, with a total area of 20.652 km<sup>2</sup> and a total perimeter of 148.8 km. From 2015 to 2023, they reduce by 9.2% and 1.7% respectively. Glaciers were predominantly concentrated in medium elevation zones (4200–4500 m) with a proportion of 46.6% and largest ablation rate of 1.68% between 2021 and 2023. The thickness and ice reserves were calculated by an empirical formula. From 2015 to 2023, the Gangshika glaciers showed reduction rates in thickness and ice reserves by 1.15% and 12.5%, respectively. |
| format | Article |
| id | doaj-art-6639f76959664e4bae85582276521fbb |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-6639f76959664e4bae85582276521fbb2025-08-20T01:54:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118121031211510.1109/JSTARS.2025.356227310969556Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-SeriesLihao Chen0https://orcid.org/0009-0004-9138-3971Wei Xu1Zhen Cao2Jiaqi Zhang3Zhizhong Kang4https://orcid.org/0000-0002-9728-4702School of Land Science and Geomatics, China University of Geosciences, Beijing, ChinaQinghai Remote Sensing Center for Natural Resources, Xining, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin, ChinaNatural Resources Satellite Application Technology Center, Xinjiang Production and Construction Corps, Xinjiang, ChinaSchool of Land Science and Geomatics, China University of Geosciences, Beijing, ChinaTime-series glacier change analysis that is closely linked to global climate patterns has drawn increasing attention. However, few studies focused on regional glacier mapping with high spatial resolution and accuracy. This study combined a lightweight DeeplabV3+ model and activation threshold optimization strategy for regional glacier mapping and change analysis in the Gangshika region of Qilian Mountain, mainly using high-resolution satellite images from 2012 to 2023. For the extraction of glaciers with different scales, multiscale contextual information was represented through Atrous spatial pyramid pooling module. The predicted probability maps were first obtained. And the optimized activation thresholds were selected to determine which areas were glaciers. The accuracies of extracted glacier boundaries from 2012 to 2023 fluctuated between 82% and 96%, with an averaged value of 93%. Considering that the quality of images in 2012–2014 and corresponding glacier boundary accuracy were not very good, the change analysis was just conducted from 2015 to 2023. As of 2023, the Gangshika region hosts 45 glaciers, with a total area of 20.652 km<sup>2</sup> and a total perimeter of 148.8 km. From 2015 to 2023, they reduce by 9.2% and 1.7% respectively. Glaciers were predominantly concentrated in medium elevation zones (4200–4500 m) with a proportion of 46.6% and largest ablation rate of 1.68% between 2021 and 2023. The thickness and ice reserves were calculated by an empirical formula. From 2015 to 2023, the Gangshika glaciers showed reduction rates in thickness and ice reserves by 1.15% and 12.5%, respectively.https://ieeexplore.ieee.org/document/10969556/Change analysisdeep learningglacier mappingqilian mountainremote sensingtime-series |
| spellingShingle | Lihao Chen Wei Xu Zhen Cao Jiaqi Zhang Zhizhong Kang Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-Series IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change analysis deep learning glacier mapping qilian mountain remote sensing time-series |
| title | Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-Series |
| title_full | Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-Series |
| title_fullStr | Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-Series |
| title_full_unstemmed | Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-Series |
| title_short | Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-Series |
| title_sort | regional glacier mapping and change analysis in the gangshika region of qilian mountain based on deep learning and satellite data time series |
| topic | Change analysis deep learning glacier mapping qilian mountain remote sensing time-series |
| url | https://ieeexplore.ieee.org/document/10969556/ |
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