Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption

Autores
Naranjo Ariza, Camilo Andres; Euillades, Pablo Andrés; Toyos, Guillermo Pablo; Euillades, Leonardo Daniel; Villarosa, Gustavo
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Volcanic ash deposits affect buildings, vegetation, and population. After a volcanic eruption, it is critical to detect the areas affected by ash deposits to achieve an advisable management of the emergency. Optical sensors have been widely used to carry out this task, but they are limited by solar illumination and weather conditions. As an alternative, Synthetic Aperture Radar (SAR) data are not affected by those limitations. Recently, a Temporal Decorrelation Model (TDM) that uses SAR data was proposed for detecting and mapping ash deposits, but it has only been applied to L-band data. Today there is available a huge quantity of C-band data acquired by the Sentinel-1 constellation. In this study we applied the TDM to Sentinel-1 data in order to assess its performance for detecting volcanic ash deposits after an eruption. We selected the eruption of Taal volcano in The Philippines on January 12, 2020, as our case study. We computed more than 4000 interferometric pairs from a dataset of 93 images acquired before, during, and after the eruption. Our results show that TDM can be applied to C-band data, despite the higher temporal decorrelation suffered by them. Our final probability map is consistent with the field evidence reported by the Philippines Institute of Volcanology and Seismology (PHILVOLCS) and the isopachs map reported in the literature. This new application provides a novel framework for the coherence exploitation of C-Band data. Also, this approach could be applied to detection and monitoring of other natural disasters.
Fil: Naranjo Ariza, Camilo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales; Argentina
Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Fil: Toyos, Guillermo Pablo. Comision Nacional de Actividades Espaciales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Euillades, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Fil: Villarosa, Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales; Argentina
Materia
ASH DEPOSITS
CHANGE DETECTION
INSAR
SENTINEL-1
TEMPORAL DECORRELATION MODEL
VOLCANIC ASH
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/219378
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruptionNaranjo Ariza, Camilo AndresEuillades, Pablo AndrésToyos, Guillermo PabloEuillades, Leonardo DanielVillarosa, GustavoASH DEPOSITSCHANGE DETECTIONINSARSENTINEL-1TEMPORAL DECORRELATION MODELVOLCANIC ASHhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Volcanic ash deposits affect buildings, vegetation, and population. After a volcanic eruption, it is critical to detect the areas affected by ash deposits to achieve an advisable management of the emergency. Optical sensors have been widely used to carry out this task, but they are limited by solar illumination and weather conditions. As an alternative, Synthetic Aperture Radar (SAR) data are not affected by those limitations. Recently, a Temporal Decorrelation Model (TDM) that uses SAR data was proposed for detecting and mapping ash deposits, but it has only been applied to L-band data. Today there is available a huge quantity of C-band data acquired by the Sentinel-1 constellation. In this study we applied the TDM to Sentinel-1 data in order to assess its performance for detecting volcanic ash deposits after an eruption. We selected the eruption of Taal volcano in The Philippines on January 12, 2020, as our case study. We computed more than 4000 interferometric pairs from a dataset of 93 images acquired before, during, and after the eruption. Our results show that TDM can be applied to C-band data, despite the higher temporal decorrelation suffered by them. Our final probability map is consistent with the field evidence reported by the Philippines Institute of Volcanology and Seismology (PHILVOLCS) and the isopachs map reported in the literature. This new application provides a novel framework for the coherence exploitation of C-Band data. Also, this approach could be applied to detection and monitoring of other natural disasters.Fil: Naranjo Ariza, Camilo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales; ArgentinaFil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaFil: Toyos, Guillermo Pablo. Comision Nacional de Actividades Espaciales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Euillades, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaFil: Villarosa, Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales; ArgentinaElsevier2023-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/219378Naranjo Ariza, Camilo Andres; Euillades, Pablo Andrés; Toyos, Guillermo Pablo; Euillades, Leonardo Daniel; Villarosa, Gustavo; Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption; Elsevier; Remote Sensing Applications: Society and Environment; 31; 5-2023; 1-292352-9385CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2352938523000733info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rsase.2023.100991info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:04:37Zoai:ri.conicet.gov.ar:11336/219378instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:04:37.382CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption
title Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption
spellingShingle Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption
Naranjo Ariza, Camilo Andres
ASH DEPOSITS
CHANGE DETECTION
INSAR
SENTINEL-1
TEMPORAL DECORRELATION MODEL
VOLCANIC ASH
title_short Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption
title_full Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption
title_fullStr Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption
title_full_unstemmed Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption
title_sort Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption
dc.creator.none.fl_str_mv Naranjo Ariza, Camilo Andres
Euillades, Pablo Andrés
Toyos, Guillermo Pablo
Euillades, Leonardo Daniel
Villarosa, Gustavo
author Naranjo Ariza, Camilo Andres
author_facet Naranjo Ariza, Camilo Andres
Euillades, Pablo Andrés
Toyos, Guillermo Pablo
Euillades, Leonardo Daniel
Villarosa, Gustavo
author_role author
author2 Euillades, Pablo Andrés
Toyos, Guillermo Pablo
Euillades, Leonardo Daniel
Villarosa, Gustavo
author2_role author
author
author
author
dc.subject.none.fl_str_mv ASH DEPOSITS
CHANGE DETECTION
INSAR
SENTINEL-1
TEMPORAL DECORRELATION MODEL
VOLCANIC ASH
topic ASH DEPOSITS
CHANGE DETECTION
INSAR
SENTINEL-1
TEMPORAL DECORRELATION MODEL
VOLCANIC ASH
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Volcanic ash deposits affect buildings, vegetation, and population. After a volcanic eruption, it is critical to detect the areas affected by ash deposits to achieve an advisable management of the emergency. Optical sensors have been widely used to carry out this task, but they are limited by solar illumination and weather conditions. As an alternative, Synthetic Aperture Radar (SAR) data are not affected by those limitations. Recently, a Temporal Decorrelation Model (TDM) that uses SAR data was proposed for detecting and mapping ash deposits, but it has only been applied to L-band data. Today there is available a huge quantity of C-band data acquired by the Sentinel-1 constellation. In this study we applied the TDM to Sentinel-1 data in order to assess its performance for detecting volcanic ash deposits after an eruption. We selected the eruption of Taal volcano in The Philippines on January 12, 2020, as our case study. We computed more than 4000 interferometric pairs from a dataset of 93 images acquired before, during, and after the eruption. Our results show that TDM can be applied to C-band data, despite the higher temporal decorrelation suffered by them. Our final probability map is consistent with the field evidence reported by the Philippines Institute of Volcanology and Seismology (PHILVOLCS) and the isopachs map reported in the literature. This new application provides a novel framework for the coherence exploitation of C-Band data. Also, this approach could be applied to detection and monitoring of other natural disasters.
Fil: Naranjo Ariza, Camilo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales; Argentina
Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Fil: Toyos, Guillermo Pablo. Comision Nacional de Actividades Espaciales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Euillades, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Fil: Villarosa, Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales. Universidad Nacional del Comahue. Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales; Argentina
description Volcanic ash deposits affect buildings, vegetation, and population. After a volcanic eruption, it is critical to detect the areas affected by ash deposits to achieve an advisable management of the emergency. Optical sensors have been widely used to carry out this task, but they are limited by solar illumination and weather conditions. As an alternative, Synthetic Aperture Radar (SAR) data are not affected by those limitations. Recently, a Temporal Decorrelation Model (TDM) that uses SAR data was proposed for detecting and mapping ash deposits, but it has only been applied to L-band data. Today there is available a huge quantity of C-band data acquired by the Sentinel-1 constellation. In this study we applied the TDM to Sentinel-1 data in order to assess its performance for detecting volcanic ash deposits after an eruption. We selected the eruption of Taal volcano in The Philippines on January 12, 2020, as our case study. We computed more than 4000 interferometric pairs from a dataset of 93 images acquired before, during, and after the eruption. Our results show that TDM can be applied to C-band data, despite the higher temporal decorrelation suffered by them. Our final probability map is consistent with the field evidence reported by the Philippines Institute of Volcanology and Seismology (PHILVOLCS) and the isopachs map reported in the literature. This new application provides a novel framework for the coherence exploitation of C-Band data. Also, this approach could be applied to detection and monitoring of other natural disasters.
publishDate 2023
dc.date.none.fl_str_mv 2023-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/219378
Naranjo Ariza, Camilo Andres; Euillades, Pablo Andrés; Toyos, Guillermo Pablo; Euillades, Leonardo Daniel; Villarosa, Gustavo; Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption; Elsevier; Remote Sensing Applications: Society and Environment; 31; 5-2023; 1-29
2352-9385
CONICET Digital
CONICET
url http://hdl.handle.net/11336/219378
identifier_str_mv Naranjo Ariza, Camilo Andres; Euillades, Pablo Andrés; Toyos, Guillermo Pablo; Euillades, Leonardo Daniel; Villarosa, Gustavo; Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption; Elsevier; Remote Sensing Applications: Society and Environment; 31; 5-2023; 1-29
2352-9385
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2352938523000733
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rsase.2023.100991
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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