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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3S29ARB
Repositorysid.inpe.br/mtc-m21c/2018/10.10.12.15
Last Update2018:10.10.12.15.30 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2018/10.10.12.15.30
Metadata Last Update2020:12.13.19.09.53 (UTC) administrator
DOI10.3390/rs10091355
ISSN2072-4292
Citation KeyPereiraFuNoSaLiSi:2018:MuFuSA
TitleMultifrequency and Full-Polarimetric SAR assessment for estimating above ground biomass and leaf area index in the Amazon Várzea Wetland
Year2018
MonthSept.
Access Date2024, Mar. 28
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size7029 KiB
2. Context
Author1 Pereira, Luciana O.
2 Furtado, Luiz F. A.
3 Novo, Evlyn Márcia Leão de Moraes
4 Sant'Anna, Sidnei João Siqueira
5 Liesenberg, Veraldo
6 Silva, Thiago S. F.
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JH39
4 8JMKD3MGP5W/3C9JJ8N
ORCID1
2
3 0000-0002-1223-9276
4
5 0000-0003-0564-7818
6 0000-0001-8174-0489
Group1
2
3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
4 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 University of Exeter
2 Universidade Federal do Rio de Janeiro (UFRJ)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Universidade Federal de Santa Catarina (UFSC)
6 Universidade Estadual Paulista (UNESP)
Author e-Mail Address1 lp469@exeter.ac.uk
2 chefechefe@gmail.com
3 evlyn.novo@inpe.br
4 sidnei@dpi.inpe.br
5 veraldo@gmail.com
6 thiago.sf.silva@unesp.br
JournalRemote Sensing
Volume10
Number9
Pagese1355
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2018-10-10 12:15:30 :: simone -> administrator ::
2018-10-10 12:15:31 :: administrator -> simone :: 2018
2018-10-10 12:16:34 :: simone -> administrator :: 2018
2020-12-13 19:09:53 :: administrator -> simone :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsSAR data
Above Ground Biomass (AGB)
Leaf Area Index (LAI)
Wetlands Amazon
AbstractThe 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.
AreaSRE
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34R/3S29ARB
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34R/3S29ARB
Languageen
Target Filepereira_multifrequency.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
Citing Item Listsid.inpe.br/bibdigital/2013/09.09.15.05 2
sid.inpe.br/mtc-m21/2012/07.13.15.00.20 1
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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