Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands

Detalhes bibliográficos
Autor(a) principal: Pereira, Luciana O.
Data de Publicação: 2018
Outros Autores: Furtado, Luiz F.A., Novo, Evlyn M.L.M., Sant'Anna, Sidnei J.S., Liesenberg, Veraldo, Silva, Thiago S.F. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.3390/rs10091355
http://hdl.handle.net/11449/171465
Resumo: The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha-1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.
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spelling Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea WetlandsAbove Ground Biomass (AGB)Leaf Area Index (LAI)SAR dataWetlands AmazonThe aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha-1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)University of ExeterBrazilian National Institute for Space Research-INPEDepartment of Geography Federal University of Rio de JaneiroDepartment of Forest Engineering Santa Catarina State University (UDESC)Ecosystem Dynamics Observatory Institute of Geosciences and Exact Sciences São Paulo State University (UNESP)Ecosystem Dynamics Observatory Institute of Geosciences and Exact Sciences São Paulo State University (UNESP)CNPq: #301118/2017-5University of ExeterBrazilian National Institute for Space Research-INPEFederal University of Rio de JaneiroSanta Catarina State University (UDESC)Universidade Estadual Paulista (Unesp)Pereira, Luciana O.Furtado, Luiz F.A.Novo, Evlyn M.L.M.Sant'Anna, Sidnei J.S.Liesenberg, VeraldoSilva, Thiago S.F. [UNESP]2018-12-11T16:55:26Z2018-12-11T16:55:26Z2018-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.3390/rs10091355Remote Sensing, v. 10, n. 9, 2018.2072-4292http://hdl.handle.net/11449/17146510.3390/rs100913552-s2.0-850536365602-s2.0-85053636560.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensing1,386info:eu-repo/semantics/openAccess2023-12-20T06:23:32Zoai:repositorio.unesp.br:11449/171465Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:49:33.269162Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands
title Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands
spellingShingle Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands
Pereira, Luciana O.
Above Ground Biomass (AGB)
Leaf Area Index (LAI)
SAR data
Wetlands Amazon
title_short Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands
title_full Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands
title_fullStr Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands
title_full_unstemmed Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands
title_sort Multifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetlands
author Pereira, Luciana O.
author_facet Pereira, Luciana O.
Furtado, Luiz F.A.
Novo, Evlyn M.L.M.
Sant'Anna, Sidnei J.S.
Liesenberg, Veraldo
Silva, Thiago S.F. [UNESP]
author_role author
author2 Furtado, Luiz F.A.
Novo, Evlyn M.L.M.
Sant'Anna, Sidnei J.S.
Liesenberg, Veraldo
Silva, Thiago S.F. [UNESP]
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv University of Exeter
Brazilian National Institute for Space Research-INPE
Federal University of Rio de Janeiro
Santa Catarina State University (UDESC)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Pereira, Luciana O.
Furtado, Luiz F.A.
Novo, Evlyn M.L.M.
Sant'Anna, Sidnei J.S.
Liesenberg, Veraldo
Silva, Thiago S.F. [UNESP]
dc.subject.por.fl_str_mv Above Ground Biomass (AGB)
Leaf Area Index (LAI)
SAR data
Wetlands Amazon
topic Above Ground Biomass (AGB)
Leaf Area Index (LAI)
SAR data
Wetlands Amazon
description The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha-1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-11T16:55:26Z
2018-12-11T16:55:26Z
2018-09-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.3390/rs10091355
Remote Sensing, v. 10, n. 9, 2018.
2072-4292
http://hdl.handle.net/11449/171465
10.3390/rs10091355
2-s2.0-85053636560
2-s2.0-85053636560.pdf
url http://dx.doi.org/10.3390/rs10091355
http://hdl.handle.net/11449/171465
identifier_str_mv Remote Sensing, v. 10, n. 9, 2018.
2072-4292
10.3390/rs10091355
2-s2.0-85053636560
2-s2.0-85053636560.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Remote Sensing
1,386
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eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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