Land-cover classification using freely available multitemporal sar data (work in progress)

Autores
Rajngewerc, Mariela; Grimson, Rafael; Bali, Juan Lucas; Minotti, Priscila; Kandus, Patricia
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Parana River, and considering different ´ seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.
Fil: Rajngewerc, Mariela. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; Argentina
Fil: Grimson, Rafael. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; Argentina
Fil: Bali, Juan Lucas. YPF - Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Minotti, Priscila. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; Argentina
Fil: Kandus, Patricia. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; Argentina
Materia
CLASSIFICATION
GLCM
SAR
SENTINEL-1
TEXTURES
WETLANDS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/166283
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/166283
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Land-cover classification using freely available multitemporal sar data (work in progress)Rajngewerc, MarielaGrimson, RafaelBali, Juan LucasMinotti, PriscilaKandus, PatriciaCLASSIFICATIONGLCMSARSENTINEL-1TEXTURESWETLANDShttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Parana River, and considering different ´ seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.Fil: Rajngewerc, Mariela. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; ArgentinaFil: Grimson, Rafael. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; ArgentinaFil: Bali, Juan Lucas. YPF - Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Minotti, Priscila. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; ArgentinaFil: Kandus, Patricia. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; ArgentinaCopernicus Publications2021-08-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/166283Rajngewerc, Mariela; Grimson, Rafael; Bali, Juan Lucas; Minotti, Priscila; Kandus, Patricia; Land-cover classification using freely available multitemporal sar data (work in progress); Copernicus Publications; International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; 46; 4/W2-2021; 19-8-2021; 133-1381682-17502194-9034CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W2-2021/133/2021/info:eu-repo/semantics/altIdentifier/doi/10.5194/isprs-archives-XLVI-4-W2-2021-133-2021info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:01:11Zoai:ri.conicet.gov.ar:11336/166283instacron: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 10:01:11.706CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Land-cover classification using freely available multitemporal sar data (work in progress)
title Land-cover classification using freely available multitemporal sar data (work in progress)
spellingShingle Land-cover classification using freely available multitemporal sar data (work in progress)
Rajngewerc, Mariela
CLASSIFICATION
GLCM
SAR
SENTINEL-1
TEXTURES
WETLANDS
title_short Land-cover classification using freely available multitemporal sar data (work in progress)
title_full Land-cover classification using freely available multitemporal sar data (work in progress)
title_fullStr Land-cover classification using freely available multitemporal sar data (work in progress)
title_full_unstemmed Land-cover classification using freely available multitemporal sar data (work in progress)
title_sort Land-cover classification using freely available multitemporal sar data (work in progress)
dc.creator.none.fl_str_mv Rajngewerc, Mariela
Grimson, Rafael
Bali, Juan Lucas
Minotti, Priscila
Kandus, Patricia
author Rajngewerc, Mariela
author_facet Rajngewerc, Mariela
Grimson, Rafael
Bali, Juan Lucas
Minotti, Priscila
Kandus, Patricia
author_role author
author2 Grimson, Rafael
Bali, Juan Lucas
Minotti, Priscila
Kandus, Patricia
author2_role author
author
author
author
dc.subject.none.fl_str_mv CLASSIFICATION
GLCM
SAR
SENTINEL-1
TEXTURES
WETLANDS
topic CLASSIFICATION
GLCM
SAR
SENTINEL-1
TEXTURES
WETLANDS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Parana River, and considering different ´ seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.
Fil: Rajngewerc, Mariela. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; Argentina
Fil: Grimson, Rafael. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; Argentina
Fil: Bali, Juan Lucas. YPF - Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Minotti, Priscila. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; Argentina
Fil: Kandus, Patricia. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; Argentina
description Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Parana River, and considering different ´ seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.
publishDate 2021
dc.date.none.fl_str_mv 2021-08-19
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/11336/166283
Rajngewerc, Mariela; Grimson, Rafael; Bali, Juan Lucas; Minotti, Priscila; Kandus, Patricia; Land-cover classification using freely available multitemporal sar data (work in progress); Copernicus Publications; International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; 46; 4/W2-2021; 19-8-2021; 133-138
1682-1750
2194-9034
CONICET Digital
CONICET
url http://hdl.handle.net/11336/166283
identifier_str_mv Rajngewerc, Mariela; Grimson, Rafael; Bali, Juan Lucas; Minotti, Priscila; Kandus, Patricia; Land-cover classification using freely available multitemporal sar data (work in progress); Copernicus Publications; International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; 46; 4/W2-2021; 19-8-2021; 133-138
1682-1750
2194-9034
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://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W2-2021/133/2021/
info:eu-repo/semantics/altIdentifier/doi/10.5194/isprs-archives-XLVI-4-W2-2021-133-2021
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Copernicus Publications
publisher.none.fl_str_mv Copernicus Publications
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
_version_ 1844613802875682816
score 13.070432

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