Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges
A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using DInSAR processing technique was developed at the Canada Center for Remote Sensing. Ground deformation rates from 2017 to 2023 were computed over a large territory of...
Published in: | Canadian Journal of Remote Sensing |
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2023
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ftdoajarticles:oai:doaj.org/article:25fbda15970348d8a598ce407c0f77d5 2024-02-04T09:58:18+01:00 Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges Sergey V. Samsonov Wanpeng Feng 2023-08-01T00:00:00Z https://doi.org/10.1080/07038992.2023.2247095 https://doaj.org/article/25fbda15970348d8a598ce407c0f77d5 EN FR eng fre Taylor & Francis Group http://dx.doi.org/10.1080/07038992.2023.2247095 https://doaj.org/toc/1712-7971 1712-7971 doi:10.1080/07038992.2023.2247095 https://doaj.org/article/25fbda15970348d8a598ce407c0f77d5 Canadian Journal of Remote Sensing, Vol 49, Iss 1 (2023) Environmental sciences GE1-350 Technology T article 2023 ftdoajarticles https://doi.org/10.1080/07038992.2023.2247095 2024-01-07T01:41:03Z A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using DInSAR processing technique was developed at the Canada Center for Remote Sensing. Ground deformation rates from 2017 to 2023 were computed over a large territory of North America and Eurasia from more than 220,000 readily available Sentinel-1 images, and the performance and shortcomings of the developed processing system were analyzed. Here, we present the processing methodology and several examples of deformation rate maps and time series produced with this automated system. Examples include the deformation of slow- moving deep-seated landslides in two regions of Canada, subsidence at the Komsomolskoe oil field in the Russian Arctic, the Tengiz oil field in Kazakhstan, multiple large subsiding regions and landslides in northwestern Iran, and two large subsiding regions in the Yellow River Delta and Xinjiang, China. Many deformation processes observed in these deformation rate maps, including large landslides, have previously been unknown to the research community. Systematic radar penetration depth changes were observed in multiple regions and were investigate in detail for 1 Eurasian region. Computed deformation rates for North America and Eurasia are available to the research community and can be downloaded from the data repository. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Canada Canadian Journal of Remote Sensing 49 1 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English French |
topic |
Environmental sciences GE1-350 Technology T |
spellingShingle |
Environmental sciences GE1-350 Technology T Sergey V. Samsonov Wanpeng Feng Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges |
topic_facet |
Environmental sciences GE1-350 Technology T |
description |
A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using DInSAR processing technique was developed at the Canada Center for Remote Sensing. Ground deformation rates from 2017 to 2023 were computed over a large territory of North America and Eurasia from more than 220,000 readily available Sentinel-1 images, and the performance and shortcomings of the developed processing system were analyzed. Here, we present the processing methodology and several examples of deformation rate maps and time series produced with this automated system. Examples include the deformation of slow- moving deep-seated landslides in two regions of Canada, subsidence at the Komsomolskoe oil field in the Russian Arctic, the Tengiz oil field in Kazakhstan, multiple large subsiding regions and landslides in northwestern Iran, and two large subsiding regions in the Yellow River Delta and Xinjiang, China. Many deformation processes observed in these deformation rate maps, including large landslides, have previously been unknown to the research community. Systematic radar penetration depth changes were observed in multiple regions and were investigate in detail for 1 Eurasian region. Computed deformation rates for North America and Eurasia are available to the research community and can be downloaded from the data repository. |
format |
Article in Journal/Newspaper |
author |
Sergey V. Samsonov Wanpeng Feng |
author_facet |
Sergey V. Samsonov Wanpeng Feng |
author_sort |
Sergey V. Samsonov |
title |
Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges |
title_short |
Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges |
title_full |
Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges |
title_fullStr |
Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges |
title_full_unstemmed |
Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges |
title_sort |
deformation retrievals for north america and eurasia from sentinel-1 dinsar: big data approach, processing methodology and challenges |
publisher |
Taylor & Francis Group |
publishDate |
2023 |
url |
https://doi.org/10.1080/07038992.2023.2247095 https://doaj.org/article/25fbda15970348d8a598ce407c0f77d5 |
geographic |
Arctic Canada |
geographic_facet |
Arctic Canada |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Canadian Journal of Remote Sensing, Vol 49, Iss 1 (2023) |
op_relation |
http://dx.doi.org/10.1080/07038992.2023.2247095 https://doaj.org/toc/1712-7971 1712-7971 doi:10.1080/07038992.2023.2247095 https://doaj.org/article/25fbda15970348d8a598ce407c0f77d5 |
op_doi |
https://doi.org/10.1080/07038992.2023.2247095 |
container_title |
Canadian Journal of Remote Sensing |
container_volume |
49 |
container_issue |
1 |
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1789962738152243200 |