Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies
Finely resolved geodetic data provide an opportunity to assess the extent and morphology of crevasses and their change over time. Crevasses have the potential to bias geodetic measurements of elevation and mass change unless they are properly accounted for. We developed a framework that automaticall...
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ftdoajarticles:oai:doaj.org/article:3c47d8fdb05b42b4b2595c7b4c850201 2023-05-15T16:57:35+02:00 Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies Marzieh Foroutan Shawn J. Marshall Brian Menounos 2019-12-01T00:00:00Z https://doi.org/10.1017/jog.2019.71 https://doaj.org/article/3c47d8fdb05b42b4b2595c7b4c850201 EN eng Cambridge University Press https://www.cambridge.org/core/product/identifier/S0022143019000716/type/journal_article https://doaj.org/toc/0022-1430 https://doaj.org/toc/1727-5652 doi:10.1017/jog.2019.71 0022-1430 1727-5652 https://doaj.org/article/3c47d8fdb05b42b4b2595c7b4c850201 Journal of Glaciology, Vol 65, Pp 971-982 (2019) Crevasses glaciological instruments and methods remote sensing Environmental sciences GE1-350 Meteorology. Climatology QC851-999 article 2019 ftdoajarticles https://doi.org/10.1017/jog.2019.71 2023-03-12T01:30:57Z Finely resolved geodetic data provide an opportunity to assess the extent and morphology of crevasses and their change over time. Crevasses have the potential to bias geodetic measurements of elevation and mass change unless they are properly accounted for. We developed a framework that automatically maps and extracts crevasse geometry and masks them where they interfere with surface mass-balance assessment. Our study examines airborne light detection and ranging digital elevation models (LiDAR DEMs) from Haig Glacier, which is experiencing a transient response in its crevassed upper regions as the glacier thins, using a self-organizing map algorithm. This method successfully extracts and characterizes ~1000 crevasses, with an overall accuracy of 94%. The resulting map provides insight into stress and flow conditions. The crevasse mask also enables refined geodetic estimates of summer mass balance. From differencing of September and April LiDAR DEMs, the raw LiDAR DEM gives a 9% overestimate in the magnitude of glacier thinning over the summer: −5.48 m compared with a mean elevation change of −5.02 m when crevasses are masked out. Without identification and removal of crevasses, the LiDAR-derived summer mass balance therefore has a negative bias relative to the glaciological surface mass balance. Article in Journal/Newspaper Journal of Glaciology Directory of Open Access Journals: DOAJ Articles Journal of Glaciology 65 254 971 982 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Crevasses glaciological instruments and methods remote sensing Environmental sciences GE1-350 Meteorology. Climatology QC851-999 |
spellingShingle |
Crevasses glaciological instruments and methods remote sensing Environmental sciences GE1-350 Meteorology. Climatology QC851-999 Marzieh Foroutan Shawn J. Marshall Brian Menounos Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies |
topic_facet |
Crevasses glaciological instruments and methods remote sensing Environmental sciences GE1-350 Meteorology. Climatology QC851-999 |
description |
Finely resolved geodetic data provide an opportunity to assess the extent and morphology of crevasses and their change over time. Crevasses have the potential to bias geodetic measurements of elevation and mass change unless they are properly accounted for. We developed a framework that automatically maps and extracts crevasse geometry and masks them where they interfere with surface mass-balance assessment. Our study examines airborne light detection and ranging digital elevation models (LiDAR DEMs) from Haig Glacier, which is experiencing a transient response in its crevassed upper regions as the glacier thins, using a self-organizing map algorithm. This method successfully extracts and characterizes ~1000 crevasses, with an overall accuracy of 94%. The resulting map provides insight into stress and flow conditions. The crevasse mask also enables refined geodetic estimates of summer mass balance. From differencing of September and April LiDAR DEMs, the raw LiDAR DEM gives a 9% overestimate in the magnitude of glacier thinning over the summer: −5.48 m compared with a mean elevation change of −5.02 m when crevasses are masked out. Without identification and removal of crevasses, the LiDAR-derived summer mass balance therefore has a negative bias relative to the glaciological surface mass balance. |
format |
Article in Journal/Newspaper |
author |
Marzieh Foroutan Shawn J. Marshall Brian Menounos |
author_facet |
Marzieh Foroutan Shawn J. Marshall Brian Menounos |
author_sort |
Marzieh Foroutan |
title |
Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies |
title_short |
Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies |
title_full |
Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies |
title_fullStr |
Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies |
title_full_unstemmed |
Automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at Haig Glacier, Canadian Rockies |
title_sort |
automatic mapping and geomorphometry extraction technique for crevasses in geodetic mass-balance calculations at haig glacier, canadian rockies |
publisher |
Cambridge University Press |
publishDate |
2019 |
url |
https://doi.org/10.1017/jog.2019.71 https://doaj.org/article/3c47d8fdb05b42b4b2595c7b4c850201 |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Journal of Glaciology, Vol 65, Pp 971-982 (2019) |
op_relation |
https://www.cambridge.org/core/product/identifier/S0022143019000716/type/journal_article https://doaj.org/toc/0022-1430 https://doaj.org/toc/1727-5652 doi:10.1017/jog.2019.71 0022-1430 1727-5652 https://doaj.org/article/3c47d8fdb05b42b4b2595c7b4c850201 |
op_doi |
https://doi.org/10.1017/jog.2019.71 |
container_title |
Journal of Glaciology |
container_volume |
65 |
container_issue |
254 |
container_start_page |
971 |
op_container_end_page |
982 |
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1766049152262733824 |