@Article{CassolCaMoArSiQuSh:2019:ReSeFo,
author = "Cassol, Henrique Luis Godinho and Carreiras, Jo{\~a}o Manuel de
Brito and Moraes, Elisabete Caria and Arag{\~a}o, Luiz Eduardo
Oliveira e Cruz de and Silva, Camila Val{\'e}ria de Jesus and
Quegan, Shaun and Shimabukuro, Yosio Edemir",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {National
Centre for Earth Observation (NCEO)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Lancaster University} and {National Centre
for Earth Observation (NCEO)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Retrieving secondary forest aboveground biomass from polarimetric
ALOS-2 PALSAR-2 data in the Brazilian Amazon",
journal = "Remote Sensing",
year = "2019",
volume = "11",
number = "1",
month = "jan.",
keywords = "backscattering, L-band, SAR polarimetry, microwave,
Chapman-Richards model, tropical forest.",
abstract = "Secondary forests (SF) are important carbon sinks, removing CO2
from the atmosphere through the photosynthesis process and storing
photosynthates in their aboveground live biomass (AGB). This
process occurring at large-scales partially counteracts C
emissions from land-use change, playing, hence, an important role
in the global carbon cycle. The absorption rates of carbon in
these forests depend on forest physiology, controlled by
environmental and climatic conditions, as well as on the past land
use, which is rarely considered for retrieving AGB from remotely
sensed data. In this context, the main goal of this study is to
evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2
data for estimating AGB in a SF area. Land-use was assessed
through Landsat time-series to extract the SF age, period of
active land-use (PALU), and frequency of clear cuts (FC) to
randomly select the SF plots. A chronosequence of 42 SF plots
ranging 328 years (20 ha) near the Tapaj{\'o}s National Forest in
Par{\'a} state was surveyed to quantifying AGB growth. The
quad-pol data was explored by testing two regression methods,
including non-linear (NL) and multiple linear regression models
(MLR).We also evaluated the influence of the past land-use in the
retrieving AGB through correlation analysis. The results showed
that the biophysical variables were positively correlated with the
volumetric scattering, meaning that SF areas presented greater
volumetric scattering contribution with increasing forest age.
Mean diameter, mean tree height, basal area, species density, and
AGB were significant and had the highest Pearson coefficients with
the Cloude decomposition (3), which in turn, refers to the
volumetric contribution backscattering from cross-polarization
(HV) ( = 0.570.66, p-value < 0.001). On the other hand, the
historical use (PALU and FC) showed the highest correlation with
angular decompositions, being the Touzi target phase angle the
highest correlation (Fs) ( = 0.37 and = 0.38, respectively). The
combination of multiple prediction variables with MLR improved the
AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51;
RMSE = 38.7 Mg ha\1) bias = 2.1 37.9 Mg ha\1
by incorporate the angular decompositions, related to historical
use, and the contribution volumetric scattering, related to forest
structure, in the model. The MLR uses six variables, whose
selected polarimetric attributes were strongly related with
different structural parameters such as the mean forest diameter,
basal area, and the mean forest tree height, and not with the AGB
as was expected. The uncertainty was estimated to be 18.6%
considered all methodological steps of the MLR model. This
approach helped us to better understand the relationship between
parameters derived from SAR data and the forest structure and its
relation to the growth of the secondary forest after deforestation
events.",
doi = "10.3390/rs11010059",
url = "http://dx.doi.org/10.3390/rs11010059",
issn = "2072-4292",
language = "en",
targetfile = "cassol_retrieving.pdf",
urlaccessdate = "27 abr. 2024"
}