Detecting seasonal ice dynamics in satellite images
Fully understanding how glaciers respond to environmental change will require new methods to help us identify the onset of ice acceleration events and observe how dynamic signals propagate within glaciers. In particular, observations of ice dynamics on seasonal timescales may offer insights into how...
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ftcopernicus:oai:publications.copernicus.org:tcd85367 2023-05-15T13:55:28+02:00 Detecting seasonal ice dynamics in satellite images Greene, Chad A. Gardner, Alex S. Andrews, Lauren C. 2020-05-26 application/pdf https://doi.org/10.5194/tc-2020-122 https://tc.copernicus.org/preprints/tc-2020-122/ eng eng doi:10.5194/tc-2020-122 https://tc.copernicus.org/preprints/tc-2020-122/ eISSN: 1994-0424 Text 2020 ftcopernicus https://doi.org/10.5194/tc-2020-122 2020-07-20T16:22:09Z Fully understanding how glaciers respond to environmental change will require new methods to help us identify the onset of ice acceleration events and observe how dynamic signals propagate within glaciers. In particular, observations of ice dynamics on seasonal timescales may offer insights into how a glacier interacts with various forcing mechanisms throughout the year. The task of generating continuous ice velocity time series that resolve seasonal variability is made difficult by the finite integration time over which ice velocities are measured from optical and repeat SAR imagery, and by a spotty satellite record that contains no optical observations throughout dark, polar winters. In this paper, we describe a method of analyzing feature-tracked velocities to characterize the magnitude and timing of seasonal ice dynamic variability. Our method is agnostic to data gaps and is able to recover climatological average winter velocities regardless of the availability of direct observations during winter. Using characteristic image acquisition times and error distributions from Antarctic image pairs in the ITS_LIVE dataset, we generate synthetic ice velocity time series, then apply our method to recover imposed magnitudes of seasonal variability within ±1.4 m yr −1 . We then validate the techniques by comparing our results to GPS data collected on Russell Glacier in Greenland. The methods presented here may be applied to better understand how ice dynamic signals propagate on seasonal timescales, and what mechanisms control the flow of the world’s ice. Text Antarc* Antarctic glacier Greenland Copernicus Publications: E-Journals Antarctic Greenland |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
Fully understanding how glaciers respond to environmental change will require new methods to help us identify the onset of ice acceleration events and observe how dynamic signals propagate within glaciers. In particular, observations of ice dynamics on seasonal timescales may offer insights into how a glacier interacts with various forcing mechanisms throughout the year. The task of generating continuous ice velocity time series that resolve seasonal variability is made difficult by the finite integration time over which ice velocities are measured from optical and repeat SAR imagery, and by a spotty satellite record that contains no optical observations throughout dark, polar winters. In this paper, we describe a method of analyzing feature-tracked velocities to characterize the magnitude and timing of seasonal ice dynamic variability. Our method is agnostic to data gaps and is able to recover climatological average winter velocities regardless of the availability of direct observations during winter. Using characteristic image acquisition times and error distributions from Antarctic image pairs in the ITS_LIVE dataset, we generate synthetic ice velocity time series, then apply our method to recover imposed magnitudes of seasonal variability within ±1.4 m yr −1 . We then validate the techniques by comparing our results to GPS data collected on Russell Glacier in Greenland. The methods presented here may be applied to better understand how ice dynamic signals propagate on seasonal timescales, and what mechanisms control the flow of the world’s ice. |
format |
Text |
author |
Greene, Chad A. Gardner, Alex S. Andrews, Lauren C. |
spellingShingle |
Greene, Chad A. Gardner, Alex S. Andrews, Lauren C. Detecting seasonal ice dynamics in satellite images |
author_facet |
Greene, Chad A. Gardner, Alex S. Andrews, Lauren C. |
author_sort |
Greene, Chad A. |
title |
Detecting seasonal ice dynamics in satellite images |
title_short |
Detecting seasonal ice dynamics in satellite images |
title_full |
Detecting seasonal ice dynamics in satellite images |
title_fullStr |
Detecting seasonal ice dynamics in satellite images |
title_full_unstemmed |
Detecting seasonal ice dynamics in satellite images |
title_sort |
detecting seasonal ice dynamics in satellite images |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-2020-122 https://tc.copernicus.org/preprints/tc-2020-122/ |
geographic |
Antarctic Greenland |
geographic_facet |
Antarctic Greenland |
genre |
Antarc* Antarctic glacier Greenland |
genre_facet |
Antarc* Antarctic glacier Greenland |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2020-122 https://tc.copernicus.org/preprints/tc-2020-122/ |
op_doi |
https://doi.org/10.5194/tc-2020-122 |
_version_ |
1766262088701837312 |