Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery
Debris-covered glaciers play an important role in the high-altitude water cycle in the Himalaya, yet their dynamics are poorly understood, partly because of the difficult fieldwork conditions. In this study we therefore deploy an unmanned aerial vehicle (UAV) three times (May 2013, October 2013 and...
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ftdoajarticles:oai:doaj.org/article:f190f782865a4d50b87b0b24791193ac 2023-05-15T13:29:28+02:00 Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery Philip Kraaijenbrink Sander W. Meijer Joseph M. Shea Francesca Pellicciotti Steven M. De Jong Walter W. Immerzeel 2016-01-01T00:00:00Z https://doi.org/10.3189/2016AoG71A072 https://doaj.org/article/f190f782865a4d50b87b0b24791193ac EN eng Cambridge University Press https://www.cambridge.org/core/product/identifier/S0260305500000124/type/journal_article https://doaj.org/toc/0260-3055 https://doaj.org/toc/1727-5644 doi:10.3189/2016AoG71A072 0260-3055 1727-5644 https://doaj.org/article/f190f782865a4d50b87b0b24791193ac Annals of Glaciology, Vol 57, Pp 103-113 (2016) debris-covered glaciers glacier flow glacier mapping glaciological instruments and methods remote sensing Meteorology. Climatology QC851-999 article 2016 ftdoajarticles https://doi.org/10.3189/2016AoG71A072 2023-03-12T01:31:57Z Debris-covered glaciers play an important role in the high-altitude water cycle in the Himalaya, yet their dynamics are poorly understood, partly because of the difficult fieldwork conditions. In this study we therefore deploy an unmanned aerial vehicle (UAV) three times (May 2013, October 2013 and May 2014) over the debris-covered Lirung Glacier in Nepal. The acquired data are processed into orthomosaics and elevation models by a Structure from Motion workflow, and seasonal surface velocity is derived using frequency cross-correlation. In order to obtain optimal surface velocity products, the effects of different input data and correlator configurations are evaluated, which reveals that the orthomosaic as input paired with moderate correlator settings provides the best results. The glacier has considerable spatial and seasonal differences in surface velocity, with maximum summer and winter velocities 6 and 2.5 m a-1, respectively, in the upper part of the tongue, while the lower part is nearly stagnant. It is hypothesized that the higher velocities during summer are caused by basal sliding due to increased lubrication of the bed. We conclude that UAVs have great potential to quantify seasonal and annual variations in flow and can help to further our understanding of debris-covered glaciers. Article in Journal/Newspaper Annals of Glaciology Directory of Open Access Journals: DOAJ Articles Annals of Glaciology 57 71 103 113 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
debris-covered glaciers glacier flow glacier mapping glaciological instruments and methods remote sensing Meteorology. Climatology QC851-999 |
spellingShingle |
debris-covered glaciers glacier flow glacier mapping glaciological instruments and methods remote sensing Meteorology. Climatology QC851-999 Philip Kraaijenbrink Sander W. Meijer Joseph M. Shea Francesca Pellicciotti Steven M. De Jong Walter W. Immerzeel Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery |
topic_facet |
debris-covered glaciers glacier flow glacier mapping glaciological instruments and methods remote sensing Meteorology. Climatology QC851-999 |
description |
Debris-covered glaciers play an important role in the high-altitude water cycle in the Himalaya, yet their dynamics are poorly understood, partly because of the difficult fieldwork conditions. In this study we therefore deploy an unmanned aerial vehicle (UAV) three times (May 2013, October 2013 and May 2014) over the debris-covered Lirung Glacier in Nepal. The acquired data are processed into orthomosaics and elevation models by a Structure from Motion workflow, and seasonal surface velocity is derived using frequency cross-correlation. In order to obtain optimal surface velocity products, the effects of different input data and correlator configurations are evaluated, which reveals that the orthomosaic as input paired with moderate correlator settings provides the best results. The glacier has considerable spatial and seasonal differences in surface velocity, with maximum summer and winter velocities 6 and 2.5 m a-1, respectively, in the upper part of the tongue, while the lower part is nearly stagnant. It is hypothesized that the higher velocities during summer are caused by basal sliding due to increased lubrication of the bed. We conclude that UAVs have great potential to quantify seasonal and annual variations in flow and can help to further our understanding of debris-covered glaciers. |
format |
Article in Journal/Newspaper |
author |
Philip Kraaijenbrink Sander W. Meijer Joseph M. Shea Francesca Pellicciotti Steven M. De Jong Walter W. Immerzeel |
author_facet |
Philip Kraaijenbrink Sander W. Meijer Joseph M. Shea Francesca Pellicciotti Steven M. De Jong Walter W. Immerzeel |
author_sort |
Philip Kraaijenbrink |
title |
Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery |
title_short |
Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery |
title_full |
Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery |
title_fullStr |
Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery |
title_full_unstemmed |
Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery |
title_sort |
seasonal surface velocities of a himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery |
publisher |
Cambridge University Press |
publishDate |
2016 |
url |
https://doi.org/10.3189/2016AoG71A072 https://doaj.org/article/f190f782865a4d50b87b0b24791193ac |
genre |
Annals of Glaciology |
genre_facet |
Annals of Glaciology |
op_source |
Annals of Glaciology, Vol 57, Pp 103-113 (2016) |
op_relation |
https://www.cambridge.org/core/product/identifier/S0260305500000124/type/journal_article https://doaj.org/toc/0260-3055 https://doaj.org/toc/1727-5644 doi:10.3189/2016AoG71A072 0260-3055 1727-5644 https://doaj.org/article/f190f782865a4d50b87b0b24791193ac |
op_doi |
https://doi.org/10.3189/2016AoG71A072 |
container_title |
Annals of Glaciology |
container_volume |
57 |
container_issue |
71 |
container_start_page |
103 |
op_container_end_page |
113 |
_version_ |
1766000832427327488 |