Fechar
Metadados

@Article{PereiraFuNoSaLiSi:2018:MuFuSA,
               author = "Pereira, Luciana O. and Furtado, Luiz F. A. and Novo, Evlyn 
                         M{\'a}rcia Le{\~a}o de Moraes and Sant'Anna, Sidnei Jo{\~a}o 
                         Siqueira and Liesenberg, Veraldo and Silva, Thiago S. F.",
          affiliation = "{University of Exeter} and {Universidade Federal do Rio de Janeiro 
                         (UFRJ)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Santa Catarina (UFSC)} and {Universidade 
                         Estadual Paulista (UNESP)}",
                title = "Multifrequency and Full-Polarimetric SAR assessment for estimating 
                         above ground biomass and leaf area index in the Amazon V{\'a}rzea 
                         Wetland",
              journal = "Remote Sensing",
                 year = "2018",
               volume = "10",
               number = "9",
                pages = "e1355",
                month = "Sept.",
             keywords = "SAR data, Above Ground Biomass (AGB), Leaf Area Index (LAI), 
                         Wetlands Amazon.",
             abstract = "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 v{\'a}rzea forests?; and (b) Are quad-pol SAR data 
                         more efficient than single- and dual-pol SAR data in estimating 
                         LAI and AGB of v{\'a}rzea 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 tĚha\−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.",
                  doi = "10.3390/rs10091355",
                  url = "http://dx.doi.org/10.3390/rs10091355",
                 issn = "2072-4292",
             language = "en",
           targetfile = "pereira_multifrequency.pdf",
        urlaccessdate = "24 nov. 2020"
}


Fechar