Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data
Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset co...
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ftmdpi:oai:mdpi.com:/2072-4292/15/3/690/ 2023-08-20T04:06:42+02:00 Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data Barbara Barzycka Mariusz Grabiec Jacek Jania Małgorzata Błaszczyk Finnur Pálsson Michał Laska Dariusz Ignatiuk Guðfinna Aðalgeirsdóttir agris 2023-01-24 application/pdf https://doi.org/10.3390/rs15030690 EN eng Multidisciplinary Digital Publishing Institute Earth Observation for Emergency Management https://dx.doi.org/10.3390/rs15030690 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 3; Pages: 690 glacier facies polarimetry PolSAR sigma0 Pauli H/alpha Wishart ground penetrating radar Hornsund Langjökull Text 2023 ftmdpi https://doi.org/10.3390/rs15030690 2023-08-01T08:27:15Z Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset collected—four C- and L-band quad-pol satellite SAR images, Ground Penetrating Radar data, shallow glacier cores—and the number of land ice bodies analyzed, namely, three tidewater glaciers in Svalbard and one ice cap in Iceland. The main aim is to assess how well popular methods of SAR analysis perform in distinguishing glacier zones, regardless of factors such as the morphologic differences of the ice bodies, or differences in SAR data. We test and validate three methods of glacier zone detection: (1) Gaussian Mixture Model–Expectation Maximization (GMM-EM) clustering of dual-pol backscattering coefficient (sigma0); (2) GMM-EM of quad-pol Pauli decomposition; and (3) quad-pol H/α Wishart segmentation. The main findings are that the unsupervised classification of both sigma0 and Pauli decomposition are promising methods for distinguishing glacier zones. The former performs better at detecting the firn zone on SAR images, and the latter in the superimposed ice zone. Additionally, C-band SAR data perform better than L-band at detecting firn, but the latter can potentially separate crevasses via the classification of sigma0 or Pauli decomposition. H/α Wishart segmentation resulted in inconsistent results across the tested cases and did not detect crevasses on L-band SAR data. Text glacier glacier Hornsund Ice cap Iceland Langjökull Svalbard Tidewater MDPI Open Access Publishing Svalbard Hornsund ENVELOPE(15.865,15.865,76.979,76.979) Langjökull ENVELOPE(-20.145,-20.145,64.654,64.654) Remote Sensing 15 3 690 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
glacier facies polarimetry PolSAR sigma0 Pauli H/alpha Wishart ground penetrating radar Hornsund Langjökull |
spellingShingle |
glacier facies polarimetry PolSAR sigma0 Pauli H/alpha Wishart ground penetrating radar Hornsund Langjökull Barbara Barzycka Mariusz Grabiec Jacek Jania Małgorzata Błaszczyk Finnur Pálsson Michał Laska Dariusz Ignatiuk Guðfinna Aðalgeirsdóttir Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data |
topic_facet |
glacier facies polarimetry PolSAR sigma0 Pauli H/alpha Wishart ground penetrating radar Hornsund Langjökull |
description |
Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset collected—four C- and L-band quad-pol satellite SAR images, Ground Penetrating Radar data, shallow glacier cores—and the number of land ice bodies analyzed, namely, three tidewater glaciers in Svalbard and one ice cap in Iceland. The main aim is to assess how well popular methods of SAR analysis perform in distinguishing glacier zones, regardless of factors such as the morphologic differences of the ice bodies, or differences in SAR data. We test and validate three methods of glacier zone detection: (1) Gaussian Mixture Model–Expectation Maximization (GMM-EM) clustering of dual-pol backscattering coefficient (sigma0); (2) GMM-EM of quad-pol Pauli decomposition; and (3) quad-pol H/α Wishart segmentation. The main findings are that the unsupervised classification of both sigma0 and Pauli decomposition are promising methods for distinguishing glacier zones. The former performs better at detecting the firn zone on SAR images, and the latter in the superimposed ice zone. Additionally, C-band SAR data perform better than L-band at detecting firn, but the latter can potentially separate crevasses via the classification of sigma0 or Pauli decomposition. H/α Wishart segmentation resulted in inconsistent results across the tested cases and did not detect crevasses on L-band SAR data. |
format |
Text |
author |
Barbara Barzycka Mariusz Grabiec Jacek Jania Małgorzata Błaszczyk Finnur Pálsson Michał Laska Dariusz Ignatiuk Guðfinna Aðalgeirsdóttir |
author_facet |
Barbara Barzycka Mariusz Grabiec Jacek Jania Małgorzata Błaszczyk Finnur Pálsson Michał Laska Dariusz Ignatiuk Guðfinna Aðalgeirsdóttir |
author_sort |
Barbara Barzycka |
title |
Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data |
title_short |
Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data |
title_full |
Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data |
title_fullStr |
Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data |
title_full_unstemmed |
Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data |
title_sort |
comparison of three methods for distinguishing glacier zones using satellite sar data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15030690 |
op_coverage |
agris |
long_lat |
ENVELOPE(15.865,15.865,76.979,76.979) ENVELOPE(-20.145,-20.145,64.654,64.654) |
geographic |
Svalbard Hornsund Langjökull |
geographic_facet |
Svalbard Hornsund Langjökull |
genre |
glacier glacier Hornsund Ice cap Iceland Langjökull Svalbard Tidewater |
genre_facet |
glacier glacier Hornsund Ice cap Iceland Langjökull Svalbard Tidewater |
op_source |
Remote Sensing; Volume 15; Issue 3; Pages: 690 |
op_relation |
Earth Observation for Emergency Management https://dx.doi.org/10.3390/rs15030690 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs15030690 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
3 |
container_start_page |
690 |
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1774717989310955520 |