Detection of glaciers displacement time-series using SAR

Autores
Euillades, Pablo Andrés; Euillades, Pablo Andrés; Riveros, Natalia Cecilia; Masiokas, Mariano Hugo; Ruiz, Lucas Ernesto; Pitte, Pedro Miguel; Elefante, Stefano; Casu, Francesco; Balbarani, Sebastian
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Glaciers are sensitive indicators of climate change. Particularly, glacier surface velocity constitutes a key parameter for estimating ice volume variations as response to global warming and its incidence in sea level rise. Several methodologies based in remotely sensed data have been employed for estimating ice velocity fields. They are mostly based in cross-correlating pairs of images in order to track features displacement between two dates. High ice flux velocity, which can reach more than 1 km/year, constitute a challenge for the existing methodologies, in practice limiting to a few days the time span between useful data. In this work we present an extension of the known Pixel Offset – Small Baseline Subsets (PO-SBAS) technique, that profit a set of successive Synthetic Aperture Radar (SAR) scenes for computing displacement time series and ice velocity fields. The algorithm is guided by a preliminary ice velocity model estimated from the data itself, which significantly improves the results reliability and reduces the overall computational cost. Furthermore, it implements a processing scheme that considers the displacement estimations (PO) quality in order to decide which pixels are included in the time-series inversion. The proposed technique is applied to 22 COSMO-Skymed SAR images of Viedma Glacier (Southern Patagonian Icefield, Argentina) spanning roughly a year. The results obtained are robust and make profit of the whole available dataset. Resulting mean velocity field and displacement time series show the algorithm suitability for retrieving and characterizing complex ice motion patterns.
Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Fil: Riveros, Natalia Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Fil: Masiokas, Mariano Hugo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Fil: Ruiz, Lucas Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Fil: Pitte, Pedro Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Fil: Elefante, Stefano. Istituto per il Rilevamento Elettromagnetico dell'Ambiente; Italia
Fil: Casu, Francesco. Istituto per il Rilevamento Elettromagnetico dell'Ambiente; Italia
Fil: Balbarani, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Materia
Glaciers
Ice Surface Velocity
Pixel Offset &Ndash; Small Baseline Subsets (Po-Sbas)
Synthetic Aperture Radar (Sar)
Time-Series
Viedma Glacier
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:11336/59687
Acceder
id CONICETDig_5ad962583ae0fbae15b74f7e0de2c53a
oai_identifier_str oai:ri.conicet.gov.ar:11336/59687
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Detection of glaciers displacement time-series using SAREuillades, Pablo AndrésEuillades, Pablo AndrésRiveros, Natalia CeciliaMasiokas, Mariano HugoRuiz, Lucas ErnestoPitte, Pedro MiguelElefante, StefanoCasu, FrancescoBalbarani, SebastianGlaciersIce Surface VelocityPixel Offset &Ndash; Small Baseline Subsets (Po-Sbas)Synthetic Aperture Radar (Sar)Time-SeriesViedma Glacierhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Glaciers are sensitive indicators of climate change. Particularly, glacier surface velocity constitutes a key parameter for estimating ice volume variations as response to global warming and its incidence in sea level rise. Several methodologies based in remotely sensed data have been employed for estimating ice velocity fields. They are mostly based in cross-correlating pairs of images in order to track features displacement between two dates. High ice flux velocity, which can reach more than 1 km/year, constitute a challenge for the existing methodologies, in practice limiting to a few days the time span between useful data. In this work we present an extension of the known Pixel Offset – Small Baseline Subsets (PO-SBAS) technique, that profit a set of successive Synthetic Aperture Radar (SAR) scenes for computing displacement time series and ice velocity fields. The algorithm is guided by a preliminary ice velocity model estimated from the data itself, which significantly improves the results reliability and reduces the overall computational cost. Furthermore, it implements a processing scheme that considers the displacement estimations (PO) quality in order to decide which pixels are included in the time-series inversion. The proposed technique is applied to 22 COSMO-Skymed SAR images of Viedma Glacier (Southern Patagonian Icefield, Argentina) spanning roughly a year. The results obtained are robust and make profit of the whole available dataset. Resulting mean velocity field and displacement time series show the algorithm suitability for retrieving and characterizing complex ice motion patterns.Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; ArgentinaFil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; ArgentinaFil: Riveros, Natalia Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; ArgentinaFil: Masiokas, Mariano Hugo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Ruiz, Lucas Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Pitte, Pedro Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Elefante, Stefano. Istituto per il Rilevamento Elettromagnetico dell'Ambiente; ItaliaFil: Casu, Francesco. Istituto per il Rilevamento Elettromagnetico dell'Ambiente; ItaliaFil: Balbarani, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; ArgentinaElsevier Science Inc2016-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59687Euillades, Pablo Andrés; Euillades, Pablo Andrés; Riveros, Natalia Cecilia; Masiokas, Mariano Hugo; Ruiz, Lucas Ernesto; et al.; Detection of glaciers displacement time-series using SAR; Elsevier Science Inc; Remote Sensing of Environment; 184; 10-2016; 188-1980034-4257CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2016.07.003info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0034425716302607info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:23:48Zoai:ri.conicet.gov.ar:11336/59687instacron: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:34982025-10-15 14:23:48.791CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Detection of glaciers displacement time-series using SAR
title Detection of glaciers displacement time-series using SAR
spellingShingle Detection of glaciers displacement time-series using SAR
Euillades, Pablo Andrés
Glaciers
Ice Surface Velocity
Pixel Offset &Ndash; Small Baseline Subsets (Po-Sbas)
Synthetic Aperture Radar (Sar)
Time-Series
Viedma Glacier
title_short Detection of glaciers displacement time-series using SAR
title_full Detection of glaciers displacement time-series using SAR
title_fullStr Detection of glaciers displacement time-series using SAR
title_full_unstemmed Detection of glaciers displacement time-series using SAR
title_sort Detection of glaciers displacement time-series using SAR
dc.creator.none.fl_str_mv Euillades, Pablo Andrés
Euillades, Pablo Andrés
Riveros, Natalia Cecilia
Masiokas, Mariano Hugo
Ruiz, Lucas Ernesto
Pitte, Pedro Miguel
Elefante, Stefano
Casu, Francesco
Balbarani, Sebastian
author Euillades, Pablo Andrés
author_facet Euillades, Pablo Andrés
Riveros, Natalia Cecilia
Masiokas, Mariano Hugo
Ruiz, Lucas Ernesto
Pitte, Pedro Miguel
Elefante, Stefano
Casu, Francesco
Balbarani, Sebastian
author_role author
author2 Riveros, Natalia Cecilia
Masiokas, Mariano Hugo
Ruiz, Lucas Ernesto
Pitte, Pedro Miguel
Elefante, Stefano
Casu, Francesco
Balbarani, Sebastian
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Glaciers
Ice Surface Velocity
Pixel Offset &Ndash; Small Baseline Subsets (Po-Sbas)
Synthetic Aperture Radar (Sar)
Time-Series
Viedma Glacier
topic Glaciers
Ice Surface Velocity
Pixel Offset &Ndash; Small Baseline Subsets (Po-Sbas)
Synthetic Aperture Radar (Sar)
Time-Series
Viedma Glacier
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Glaciers are sensitive indicators of climate change. Particularly, glacier surface velocity constitutes a key parameter for estimating ice volume variations as response to global warming and its incidence in sea level rise. Several methodologies based in remotely sensed data have been employed for estimating ice velocity fields. They are mostly based in cross-correlating pairs of images in order to track features displacement between two dates. High ice flux velocity, which can reach more than 1 km/year, constitute a challenge for the existing methodologies, in practice limiting to a few days the time span between useful data. In this work we present an extension of the known Pixel Offset – Small Baseline Subsets (PO-SBAS) technique, that profit a set of successive Synthetic Aperture Radar (SAR) scenes for computing displacement time series and ice velocity fields. The algorithm is guided by a preliminary ice velocity model estimated from the data itself, which significantly improves the results reliability and reduces the overall computational cost. Furthermore, it implements a processing scheme that considers the displacement estimations (PO) quality in order to decide which pixels are included in the time-series inversion. The proposed technique is applied to 22 COSMO-Skymed SAR images of Viedma Glacier (Southern Patagonian Icefield, Argentina) spanning roughly a year. The results obtained are robust and make profit of the whole available dataset. Resulting mean velocity field and displacement time series show the algorithm suitability for retrieving and characterizing complex ice motion patterns.
Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Fil: Riveros, Natalia Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Fil: Masiokas, Mariano Hugo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Fil: Ruiz, Lucas Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Fil: Pitte, Pedro Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Fil: Elefante, Stefano. Istituto per il Rilevamento Elettromagnetico dell'Ambiente; Italia
Fil: Casu, Francesco. Istituto per il Rilevamento Elettromagnetico dell'Ambiente; Italia
Fil: Balbarani, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
description Glaciers are sensitive indicators of climate change. Particularly, glacier surface velocity constitutes a key parameter for estimating ice volume variations as response to global warming and its incidence in sea level rise. Several methodologies based in remotely sensed data have been employed for estimating ice velocity fields. They are mostly based in cross-correlating pairs of images in order to track features displacement between two dates. High ice flux velocity, which can reach more than 1 km/year, constitute a challenge for the existing methodologies, in practice limiting to a few days the time span between useful data. In this work we present an extension of the known Pixel Offset – Small Baseline Subsets (PO-SBAS) technique, that profit a set of successive Synthetic Aperture Radar (SAR) scenes for computing displacement time series and ice velocity fields. The algorithm is guided by a preliminary ice velocity model estimated from the data itself, which significantly improves the results reliability and reduces the overall computational cost. Furthermore, it implements a processing scheme that considers the displacement estimations (PO) quality in order to decide which pixels are included in the time-series inversion. The proposed technique is applied to 22 COSMO-Skymed SAR images of Viedma Glacier (Southern Patagonian Icefield, Argentina) spanning roughly a year. The results obtained are robust and make profit of the whole available dataset. Resulting mean velocity field and displacement time series show the algorithm suitability for retrieving and characterizing complex ice motion patterns.
publishDate 2016
dc.date.none.fl_str_mv 2016-10
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/11336/59687
Euillades, Pablo Andrés; Euillades, Pablo Andrés; Riveros, Natalia Cecilia; Masiokas, Mariano Hugo; Ruiz, Lucas Ernesto; et al.; Detection of glaciers displacement time-series using SAR; Elsevier Science Inc; Remote Sensing of Environment; 184; 10-2016; 188-198
0034-4257
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59687
identifier_str_mv Euillades, Pablo Andrés; Euillades, Pablo Andrés; Riveros, Natalia Cecilia; Masiokas, Mariano Hugo; Ruiz, Lucas Ernesto; et al.; Detection of glaciers displacement time-series using SAR; Elsevier Science Inc; Remote Sensing of Environment; 184; 10-2016; 188-198
0034-4257
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2016.07.003
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0034425716302607
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
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Inc
publisher.none.fl_str_mv Elsevier Science Inc
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 13.22299

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