Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements
In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on the fast processing of large data volumes and products with complete spatial coverage. The metadata of such measurement...
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Copernicus Publications
2022
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Online Access: | https://doi.org/10.5194/tc-16-2285-2022 https://tc.copernicus.org/articles/16/2285/2022/tc-16-2285-2022.pdf https://doaj.org/article/8754b3c42a86436285031e4b2dcdb58f |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:8754b3c42a86436285031e4b2dcdb58f 2023-05-15T16:20:34+02:00 Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements B. Altena A. Kääb B. Wouters 2022-06-01 https://doi.org/10.5194/tc-16-2285-2022 https://tc.copernicus.org/articles/16/2285/2022/tc-16-2285-2022.pdf https://doaj.org/article/8754b3c42a86436285031e4b2dcdb58f en eng Copernicus Publications doi:10.5194/tc-16-2285-2022 1994-0416 1994-0424 https://tc.copernicus.org/articles/16/2285/2022/tc-16-2285-2022.pdf https://doaj.org/article/8754b3c42a86436285031e4b2dcdb58f undefined The Cryosphere, Vol 16, Pp 2285-2300 (2022) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2022 fttriple https://doi.org/10.5194/tc-16-2285-2022 2023-01-22T18:19:13Z In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on the fast processing of large data volumes and products with complete spatial coverage. The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality neither isotropic nor constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image structure and the non-uniform flow of the ice and is used here to extract a proxy for measurement uncertainty. A quantification of estimation error or dispersion for each individual velocity measurement can be important for the inversion of, for instance, rheology, ice thickness and/or bedrock friction. Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and outliers. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focusing on satellite data. The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least-squares estimation, making its implementation into current pipelines straightforward. The methodology is demonstrated for Sermeq Kujalleq (Jakobshavn Isbræ), Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska. Directionality within an image seems to be the dominant factor influencing the correlation dispersion. In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced on the correlation spread. Article in Journal/Newspaper glacier glacier Greenland Jakobshavn Jakobshavn isbræ Kujalleq Sermeq Kujalleq The Cryosphere Alaska Unknown Greenland Jakobshavn Isbræ ENVELOPE(-49.917,-49.917,69.167,69.167) Kujalleq ENVELOPE(-46.037,-46.037,60.719,60.719) The Cryosphere 16 6 2285 2300 |
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English |
topic |
geo envir |
spellingShingle |
geo envir B. Altena A. Kääb B. Wouters Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements |
topic_facet |
geo envir |
description |
In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on the fast processing of large data volumes and products with complete spatial coverage. The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality neither isotropic nor constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image structure and the non-uniform flow of the ice and is used here to extract a proxy for measurement uncertainty. A quantification of estimation error or dispersion for each individual velocity measurement can be important for the inversion of, for instance, rheology, ice thickness and/or bedrock friction. Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and outliers. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focusing on satellite data. The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least-squares estimation, making its implementation into current pipelines straightforward. The methodology is demonstrated for Sermeq Kujalleq (Jakobshavn Isbræ), Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska. Directionality within an image seems to be the dominant factor influencing the correlation dispersion. In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced on the correlation spread. |
format |
Article in Journal/Newspaper |
author |
B. Altena A. Kääb B. Wouters |
author_facet |
B. Altena A. Kääb B. Wouters |
author_sort |
B. Altena |
title |
Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements |
title_short |
Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements |
title_full |
Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements |
title_fullStr |
Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements |
title_full_unstemmed |
Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements |
title_sort |
correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-16-2285-2022 https://tc.copernicus.org/articles/16/2285/2022/tc-16-2285-2022.pdf https://doaj.org/article/8754b3c42a86436285031e4b2dcdb58f |
long_lat |
ENVELOPE(-49.917,-49.917,69.167,69.167) ENVELOPE(-46.037,-46.037,60.719,60.719) |
geographic |
Greenland Jakobshavn Isbræ Kujalleq |
geographic_facet |
Greenland Jakobshavn Isbræ Kujalleq |
genre |
glacier glacier Greenland Jakobshavn Jakobshavn isbræ Kujalleq Sermeq Kujalleq The Cryosphere Alaska |
genre_facet |
glacier glacier Greenland Jakobshavn Jakobshavn isbræ Kujalleq Sermeq Kujalleq The Cryosphere Alaska |
op_source |
The Cryosphere, Vol 16, Pp 2285-2300 (2022) |
op_relation |
doi:10.5194/tc-16-2285-2022 1994-0416 1994-0424 https://tc.copernicus.org/articles/16/2285/2022/tc-16-2285-2022.pdf https://doaj.org/article/8754b3c42a86436285031e4b2dcdb58f |
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op_doi |
https://doi.org/10.5194/tc-16-2285-2022 |
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The Cryosphere |
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16 |
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