@Article{AlmeidaGAOJPSLGSFL:2019:CoLiHy,
author = "Almeida, Catherine Torres de and Galv{\~a}o, L{\^e}nio Soares
and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and Ometto, Jean
Pierre Henry Balbaud and Jacon, Aline Daniele and Pereira,
Francisca Rocha de Souza and Sato, Luciane Yumie and Lopes, Aline
Pontes and Gra{\c{c}}a, Paulo Maur{\'{\i}}cio Lima de
Alencastro and Silva, Camila Val{\'e}ria de Jesus and
Ferreira-Ferreira, Jefferson and Longo, Marcos",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas da Amaz{\^o}nia (INPA)} and {Lancaster University} and
{Instituto de Desenvolvimento Sustent{\'a}vel Mamirau{\'a}} and
{California Institute of Technology}",
title = "Combining LiDAR and hyperspectral data for aboveground biomass
modeling in the Brazilian Amazon using different regression
algorithms",
journal = "Remote Sensing of Environment",
year = "2019",
volume = "232",
pages = "e111323",
month = "Oct.",
note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
keywords = "Hyperspectral remote sensing, Laser scanning, Data integration,
Tropical forest, Carbon stock.",
abstract = "Accurate estimates of aboveground biomass (AGB) in tropical
forests are critical for supporting strategies of ecosystem
functioning conservation and climate change mitigation. However,
such estimates at regional and local scales are still highly
uncertain. Airborne Light Detection And Ranging (LiDAR) and
Hyperspectral Imaging (HSI) can characterize the structural and
functional diversity of forests with high accuracy at a sub-meter
resolution, and potentially improve the AGB estimations. In this
study, we compared the ability of different data sources (airborne
LiDAR and HSI, and their combination) and regression methods
(linear model - LM, linear model with ridge regularization - LMR,
Support Vector Regression - SVR, Random Forest - RF, Stochastic
Gradient Boosting - SGB, and Cubist - CB) to improve AGB
predictions in the Brazilian Amazon. We used georeferenced
inventory data from 132 sample plots to obtain a reference field
AGB and calculated 333 metrics (45 from LiDAR and 288 from HSI)
that could be used as predictors for statistical AGB models. We
submitted the metrics to a correlation filtering followed by a
feature selection procedure (recursive feature elimination) to
optimize the performance of the models and to reduce their
complexity. Results showed that both LiDAR and HSI data used alone
provided relatively high accurate models if adequate metrics and
algorithms are chosen (RMSE = 67.6 Mg.ha\−1 , RMSE% = 36%,
R2 = 0.58, for the best LiDAR model; RMSE = 68.1 Mg.ha\−1 ,
RMSE % = 36%, R2 = 0.58, for the best HSI model). However,
HSI-only models required more metrics (512) than LiDAR-only models
(25). Models combining metrics from both datasets resulted in more
accurate AGB estimates, regardless of the regression method (RMSE
= 57.7 Mg.ha\−1 , RMSE% = 31%, R2 = 0.70, for the best
model). The most important LiDAR metrics for estimating AGB were
related to the upper canopy cover and tree height percentiles,
while the most important HSI metrics were associated with the near
infrared and shortwave infrared spectral regions, particularly the
leaf/canopy water and lignin-cellulose absorption bands. Finally,
an analysis of variance (ANOVA) showed that the remote sensing
data source (LiDAR, HSI, or their combination) had a greater
effect size than the regression algorithms. Thus, no single
algorithm outperformed the others, although the LM method was less
suitable when applied to the HSI and hybrid datasets. Results show
that the synergistic use of LiDAR and hyperspectral data has great
potential for improving the accuracy of the biomass estimates in
the Brazilian Amazon.",
doi = "10.1016/j.rse.2019.111323",
url = "http://dx.doi.org/10.1016/j.rse.2019.111323",
issn = "0034-4257",
language = "en",
targetfile = "almeida_combining.pdf",
urlaccessdate = "19 maio 2024"
}