Correlation dispersion as a measure to better estimate uncertainty of 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 fast processing of large data volumes. The metadata of such measurements are often highly simplified when the measureme...
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ftcopernicus:oai:publications.copernicus.org:tcd96175 2023-05-15T16:20:33+02:00 Correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements Altena, Bas Kääb, Andreas Wouters, Bert 2021-09-01 application/pdf https://doi.org/10.5194/tc-2021-202 https://tc.copernicus.org/preprints/tc-2021-202/ eng eng doi:10.5194/tc-2021-202 https://tc.copernicus.org/preprints/tc-2021-202/ eISSN: 1994-0424 Text 2021 ftcopernicus https://doi.org/10.5194/tc-2021-202 2021-09-06T16:22:29Z 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 fast processing of large data volumes. 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 not isotropic and constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image content and the non-uniform flow of the ice. Precise dispersion estimates for each individual velocity measurement can be important for 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 errors. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focussing 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, 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 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. Text glacier glacier Greenland Kujalleq Sermeq Kujalleq Alaska Copernicus Publications: E-Journals Greenland Kujalleq ENVELOPE(-46.037,-46.037,60.719,60.719) |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
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 fast processing of large data volumes. 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 not isotropic and constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image content and the non-uniform flow of the ice. Precise dispersion estimates for each individual velocity measurement can be important for 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 errors. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focussing 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, 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 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. |
format |
Text |
author |
Altena, Bas Kääb, Andreas Wouters, Bert |
spellingShingle |
Altena, Bas Kääb, Andreas Wouters, Bert Correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements |
author_facet |
Altena, Bas Kääb, Andreas Wouters, Bert |
author_sort |
Altena, Bas |
title |
Correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements |
title_short |
Correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements |
title_full |
Correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements |
title_fullStr |
Correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements |
title_full_unstemmed |
Correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements |
title_sort |
correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements |
publishDate |
2021 |
url |
https://doi.org/10.5194/tc-2021-202 https://tc.copernicus.org/preprints/tc-2021-202/ |
long_lat |
ENVELOPE(-46.037,-46.037,60.719,60.719) |
geographic |
Greenland Kujalleq |
geographic_facet |
Greenland Kujalleq |
genre |
glacier glacier Greenland Kujalleq Sermeq Kujalleq Alaska |
genre_facet |
glacier glacier Greenland Kujalleq Sermeq Kujalleq Alaska |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2021-202 https://tc.copernicus.org/preprints/tc-2021-202/ |
op_doi |
https://doi.org/10.5194/tc-2021-202 |
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1766008485923782656 |