Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys
Current understanding of glacier mass balance changes under changing climate is limited by scarcity of in situ measurements in both time and space, as well as resolution of remote sensing products. Recent innovations in unmanned aerial vehicles (UAVs), as well as structure-from-motion photogrammetry...
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ftmdpi:oai:mdpi.com:/2072-4292/10/10/1547/ 2023-08-20T04:04:24+02:00 Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys Eleanor A. Bash Brian J. Moorman Allison Gunther agris 2018-09-26 application/pdf https://doi.org/10.3390/rs10101547 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs10101547 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 10; Issue 10; Pages: 1547 UAV structure-from-motion photogrammetry change detection glacier melt Canadian Arctic Text 2018 ftmdpi https://doi.org/10.3390/rs10101547 2023-07-31T21:44:55Z Current understanding of glacier mass balance changes under changing climate is limited by scarcity of in situ measurements in both time and space, as well as resolution of remote sensing products. Recent innovations in unmanned aerial vehicles (UAVs), as well as structure-from-motion photogrammetry (SfM), have led to increased use of digital imagery to derive topographic data in great detail in many fields, including glaciology. This study tested the capability of UAV surveys to detect surface changes over glacier ice during a three-day period in July 2016. Three UAV imaging missions were conducted during this time over 0.185 km 2 of the ablation area of Fountain Glacier, NU. These were processed with the SfM algorithms in Agisoft Photoscan Professional and overall accuracies of the resulting point clouds ranged from 0.030 to 0.043 m. The high accuracy of point clouds achieved here is primarily a result of a small ground sampling distance (0.018 m), and is also influenced by GPS precision. Glacier surface change was measured through differencing of point clouds and change was compared to ablation stake measurements. Surface change measured with the UAV-SfM method agreed with the coincident ablation stake measurements in most instances, with RMSE values of 0.033, 0.028, and 0.042 m for one-, two-, and three-day periods, respectively. Total specific melt over the study area measured with the UAV was 0.170 m water equivalent (w.e.), while interpolation of ablation measurements resulted in 0.144 m w.e. Using UAVs to measure small changes in glacier surfaces will allow for new investigations of distribution of mass balance measurements. Text Arctic MDPI Open Access Publishing Arctic Fountain Glacier ENVELOPE(161.633,161.633,-77.683,-77.683) Remote Sensing 10 10 1547 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
UAV structure-from-motion photogrammetry change detection glacier melt Canadian Arctic |
spellingShingle |
UAV structure-from-motion photogrammetry change detection glacier melt Canadian Arctic Eleanor A. Bash Brian J. Moorman Allison Gunther Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys |
topic_facet |
UAV structure-from-motion photogrammetry change detection glacier melt Canadian Arctic |
description |
Current understanding of glacier mass balance changes under changing climate is limited by scarcity of in situ measurements in both time and space, as well as resolution of remote sensing products. Recent innovations in unmanned aerial vehicles (UAVs), as well as structure-from-motion photogrammetry (SfM), have led to increased use of digital imagery to derive topographic data in great detail in many fields, including glaciology. This study tested the capability of UAV surveys to detect surface changes over glacier ice during a three-day period in July 2016. Three UAV imaging missions were conducted during this time over 0.185 km 2 of the ablation area of Fountain Glacier, NU. These were processed with the SfM algorithms in Agisoft Photoscan Professional and overall accuracies of the resulting point clouds ranged from 0.030 to 0.043 m. The high accuracy of point clouds achieved here is primarily a result of a small ground sampling distance (0.018 m), and is also influenced by GPS precision. Glacier surface change was measured through differencing of point clouds and change was compared to ablation stake measurements. Surface change measured with the UAV-SfM method agreed with the coincident ablation stake measurements in most instances, with RMSE values of 0.033, 0.028, and 0.042 m for one-, two-, and three-day periods, respectively. Total specific melt over the study area measured with the UAV was 0.170 m water equivalent (w.e.), while interpolation of ablation measurements resulted in 0.144 m w.e. Using UAVs to measure small changes in glacier surfaces will allow for new investigations of distribution of mass balance measurements. |
format |
Text |
author |
Eleanor A. Bash Brian J. Moorman Allison Gunther |
author_facet |
Eleanor A. Bash Brian J. Moorman Allison Gunther |
author_sort |
Eleanor A. Bash |
title |
Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys |
title_short |
Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys |
title_full |
Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys |
title_fullStr |
Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys |
title_full_unstemmed |
Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys |
title_sort |
detecting short-term surface melt on an arctic glacier using uav surveys |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs10101547 |
op_coverage |
agris |
long_lat |
ENVELOPE(161.633,161.633,-77.683,-77.683) |
geographic |
Arctic Fountain Glacier |
geographic_facet |
Arctic Fountain Glacier |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Remote Sensing; Volume 10; Issue 10; Pages: 1547 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs10101547 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs10101547 |
container_title |
Remote Sensing |
container_volume |
10 |
container_issue |
10 |
container_start_page |
1547 |
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