@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 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.",
doi = "10.3390/rs10091355",
url = "http://dx.doi.org/10.3390/rs10091355",
issn = "2072-4292",
language = "en",
targetfile = "pereira_multifrequency.pdf",
urlaccessdate = "03 jun. 2024"
}