@InProceedings{AlmeidaGaArOmJaPeSa:2019:SeHyVa,
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",
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)}",
title = "Selection of hyperspectral variables for aboveground biomass
estimation in the Brazilian Amazon",
year = "2019",
organization = "Congresso Mundial da IUFRO",
abstract = "Due to the limited coverage of field Aboveground Biomass (AGB),
remote sensing becomes an alternative for monitoring carbon stocks
at the landscape scale. However, the most commonly used sensors
have limited spectral resolution. Hyperspectral imaging (HSI)
provides high-resolution information, although its high data
dimensionality becomes a challenge for modeling. In this context,
selection of suitable variables is a critical step for estimating
AGB from HSI data. Support Vector Regression coupled with the
Recursive Feature Elimination approach (SVR-RFE) can produce
parsimonious models from a reduced subset of features. We applied
the SVR-RFE in a 5-fold cross-validation strategy with 5
repetitions to determine which hyperspectral variables were most
effective to estimate AGB. We used field AGB from 147 inventory
plots across the Brazilian Amazon and 64 plot-level HSI metrics,
including 14 reflectance bands, 30 vegetation indices,
continuum-removal absorption features at five wavelengths (495,
670, 980, 1200, and 2100 nm), and endmember fractions (green
vegetation, shade, and non-photosynthetic vegetation/soil) from
Spectral Mixture Analysis. The SVR-RFE explained 67% of the AGB
variation, by selecting eight HSI variables. The three most
effective variables came from the shortwave infrared region (width
and depth of the 2100-nm absorption band and the NDNI index),
related to canopy moisture and lignin-cellulose-nitrogen
absorption bands. Four metrics were retrieved from the water
absorption band centered at 980 nm (depth, asymmetry, and the
indices PWI and LWVI1). The width of the band placed at 495 nm was
also selected. SVR-RFE proved to be an efficient technique for
estimating AGB from HSI data.",
conference-location = "Curitiba, PR",
conference-year = "29 set. - 05 out.",
language = "en",
urlaccessdate = "21 maio 2024"
}