Automatic retrieval of volcanic SO2 emission source from TROPOMI products
Volcanic sulfur dioxide (SO2) satellite observations are key for monitoring volcanic activity, and for mitigation of the associated risks on both human health and aviation safety. Automatic analysis of this data source, including robust source emission retrieval, is in turn essential for near real-t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.1064171/full |
| _version_ | 1797950922863673344 |
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| author | Balazs Markus Sébastien Valade Manuel Wöllhaf Olaf Hellwich |
| author_facet | Balazs Markus Sébastien Valade Manuel Wöllhaf Olaf Hellwich |
| author_sort | Balazs Markus |
| collection | DOAJ |
| description | Volcanic sulfur dioxide (SO2) satellite observations are key for monitoring volcanic activity, and for mitigation of the associated risks on both human health and aviation safety. Automatic analysis of this data source, including robust source emission retrieval, is in turn essential for near real-time monitoring applications. We have developed fast and accurate SO2 plume classifier and segmentation algorithms using classic clustering, segmentation and image processing techniques. These algorithms, applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform, can help in the accurate source estimation of volcanic SO2 plumes originating from various volcanoes. In this paper, we demonstrate the ability of different pixel classification methodologies to retrieve SO2 source emission with a good accuracy. We compare the algorithms, their strengths and shortcomings, and present plume classification results for various active volcanoes throughout the year 2021, including examples from Etna (Italy), Sangay and Reventador (Ecuador), Sabancaya and Ubinas (Peru), Scheveluch and Klyuchevskoy (Russia), as well as Ibu and Dukono (Indonesia). The developed algorithms, shared as open-source code, contribute to improving analysis and monitoring of volcanic emissions from space. |
| first_indexed | 2024-04-10T22:22:46Z |
| format | Article |
| id | doaj.art-1977d53e623a4a14bd3c674cc50280c4 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T22:22:46Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-1977d53e623a4a14bd3c674cc50280c42023-01-17T14:09:11ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011010.3389/feart.2022.10641711064171Automatic retrieval of volcanic SO2 emission source from TROPOMI productsBalazs Markus0Sébastien Valade1Manuel Wöllhaf2Olaf Hellwich3Fachgebiet Computer Vision & Remote Sensing, Technische Universität Berlin, Berlin, GermanyInstituto de Geofísica, Universidad Nacional Autónoma de México (UNAM), Mexico City, MexicoFachgebiet Computer Vision & Remote Sensing, Technische Universität Berlin, Berlin, GermanyFachgebiet Computer Vision & Remote Sensing, Technische Universität Berlin, Berlin, GermanyVolcanic sulfur dioxide (SO2) satellite observations are key for monitoring volcanic activity, and for mitigation of the associated risks on both human health and aviation safety. Automatic analysis of this data source, including robust source emission retrieval, is in turn essential for near real-time monitoring applications. We have developed fast and accurate SO2 plume classifier and segmentation algorithms using classic clustering, segmentation and image processing techniques. These algorithms, applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform, can help in the accurate source estimation of volcanic SO2 plumes originating from various volcanoes. In this paper, we demonstrate the ability of different pixel classification methodologies to retrieve SO2 source emission with a good accuracy. We compare the algorithms, their strengths and shortcomings, and present plume classification results for various active volcanoes throughout the year 2021, including examples from Etna (Italy), Sangay and Reventador (Ecuador), Sabancaya and Ubinas (Peru), Scheveluch and Klyuchevskoy (Russia), as well as Ibu and Dukono (Indonesia). The developed algorithms, shared as open-source code, contribute to improving analysis and monitoring of volcanic emissions from space.https://www.frontiersin.org/articles/10.3389/feart.2022.1064171/fullclusteringsatellite remote sensingsemantic segmentationSO2 volcanic plumesvolcano monitoring |
| spellingShingle | Balazs Markus Sébastien Valade Manuel Wöllhaf Olaf Hellwich Automatic retrieval of volcanic SO2 emission source from TROPOMI products Frontiers in Earth Science clustering satellite remote sensing semantic segmentation SO2 volcanic plumes volcano monitoring |
| title | Automatic retrieval of volcanic SO2 emission source from TROPOMI products |
| title_full | Automatic retrieval of volcanic SO2 emission source from TROPOMI products |
| title_fullStr | Automatic retrieval of volcanic SO2 emission source from TROPOMI products |
| title_full_unstemmed | Automatic retrieval of volcanic SO2 emission source from TROPOMI products |
| title_short | Automatic retrieval of volcanic SO2 emission source from TROPOMI products |
| title_sort | automatic retrieval of volcanic so2 emission source from tropomi products |
| topic | clustering satellite remote sensing semantic segmentation SO2 volcanic plumes volcano monitoring |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.1064171/full |
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