@MastersThesis{Pizarro:1999:SeReHi,
author = "Pizarro, Marco Antonio",
title = "Sensoriamento remoto hiperespectral para a
caracteriza{\c{c}}{\~a}o e identifica{\c{c}}{\~a}o mineral em
solos tropicais",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "1999",
address = "Sao Jose dos Campos",
month = "1999-06-21",
keywords = "Espectr{\^o}metro de imageamento, sensoriamento remoto,
espectr{\^o}metro imageador aerotransportado no vis{\'{\i}}vel
e infravermelho (AVIRIS), solos, identifica{\c{c}}{\~a}o
mineral, reflect{\^a}ncia, an{\'a}lise por componente principal,
imaging spectrometer, remote sensing, airborne visible/infrared
imaging spectrometer (AVIRIS), soils, mineral indetification,
reflectance, Principal Components Analysis (PCA).",
abstract = "O objetivo do presente trabalho foi avaliar o uso dos dados
gerados pelo espectr{\^o}metro imageador Airborne
Visible/InfraRed Imaging Spectrometer (AVIRIS) durante a
miss{\~a}o Smoke, Sulfate, Clouds, and Radiation - Brazil
(SCAR-B), no per{\'{\i}}odo de agosto a setembro de 1995, para a
caracteriza{\c{c}}{\~a}o espectral e identifica{\c{c}}{\~a}o
mineral em solos de uma area de estudo localizada pr{\'o}ximo a
cidade de Campo Grande (MS). As imagens foram convertidas de
valores de radi{\^a}ncia para reflect{\^a}ncia de
superf{\'{\i}}cie, atrav{\'e}s de um m{\'e}todo de
corre{\c{c}}{\~a}o atmosf{\'e}rica baseado no modelo MODTRAN.
Para facilitar a an{\'a}lise das caracter{\'{\i}}sticas
espectrais da {\'a}rea de estudo e da discrimina{\c{c}}{\~a}o
dos principais tipos de solos presentes, a An{\'a}lise por
Componentes Principais (ACP) foi aplicada sobre as imagens
Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) para
reduzir a alta dimensionalidade de seu conjunto de bandas. As
rela{\c{c}}{\~o}es entre as curvas espectrais obtidas para 18
amostras de solos em laborat{\'o}rio pelo sensor InfraRed
Intelligent Spectrometer (IRIS) e os espectros AVIRIS de pixels
aproximadamente correspondentes aos locais de amostragem em campo
foram estudadas. Para tal finalidade, a an{\'a}lise de
correla{\c{c}}{\~a}o, a an{\'a}lise derivativa e o calculo do
Normalized Difference Vegetation Index (NDVI), a partir dos
espectros dos pixels e das amostras de solo, foram sequencialmente
aplicados. Finalmente, a t{\'e}cnica Spectral Feature Fitting
(SFF) foi usada para a identifica{\c{c}}{\~a}o na cena de alguns
minerais do grupo dos {\'o}xidos de ferro (apetita e hematita) e
do grupo das argilas (caulinita, montmorilonita e gibbsita). Os
resultados obtidos indicaram que: (a) as classes de solo Podzolico
Vermelho-Amarelo (PV), Latossolo Vermelho-Escuro (LE) e Latossolo
Roxo (LR) podem ser discriminadas, principalmente, em
fun{\c{c}}{\~a}o de seu albedo (primeira Componente Principal -
CP1). A varia{\c{c}}{\~a}o na forma dos espectros, associada a
CP2 e CP3, propicia a discrimina{\c{c}}{\~a}o da PV em relacao
aos LR e LE, (b) as rela{\c{c}}{\~o}es entre os espectros de
laborat{\'o}rio (IRIS) e de aeronave (AVIRIS) refletem as
diferen{\c{c}}as inerentes aos dois ambientes de
aquisi{\c{c}}{\~a}o de dados. A contamina{\c{c}}{\~a}o de
pixel por res{\'{\i}}duos de vegeta{\c{c}}{\~a}o
fotossinteticamente ativa ou inativa afeta as
correla{\c{c}}{\~o}es entre os dois conjuntos de todos,
especialmente, na faixa entre 500 e 1.200 nm; (c) os solos da
{\'a}rea de estudo s{\~a}o espectralmente dominados pela
presen{\c{c}}a de hematita e caulinita, de acordo com os
resultados obtidos com o uso da t{\'e}cnica SFF. ABSTRACT: The
objective of this work was to evaluate the use of the data
generated by the imaging spectrometer Airborne Visible/InfraRed
Imaging Spectrometer (AVIRIS) during the mission Smoke, Sulfate,
Clouds, And Radiation Brazil (SCAR-B), in the period of August to
September of 1995, for the spectral characterization and mineral
identification in soils. The study area was near Campo Grande (MS)
city, Brazil. The AVIRIS data were converted from radiance values
to surface reflectance through a method of atmospheric correction
based on the MODTRAN radiative transfer model. In order to make
the spectral analysis of the soil characteristics and to
discriminate the main types of soils present in the area,
Principal Components Analysis (PCA) was applied on the AVIRIS
data. PCA reduced the high dimensionality of AVIRIS data. AVIRIS
spectral data were compared to laboratory IRIS (InfraRed
Intelligent Spectroradiometer) data of 18 soil samples.
Correlation analysis, derivative analysis and the calculation of
Normalized Difference Vegetation Index (NDVI), were applied for
both laboratory and AVIRIS data. Finally, the Spectral Feature
Fitting (SFF) technique was used for the identification of some
minerals in the AVIRIS scene. The main minerals studied were from
the group of the iron oxides (hematite and goethite) and from the
group of the clays (kaolinite, montmorillonite and gibbsite). The
main results were: (a) the soil classes Podz{\'o}lico
Vermelho-Amarelo (PV, Arenica Abruptic Paleudult), Latossolo
Vermelho-Escuro (LE, Typic Haplorthox) and Latossolo Roxo (LR,
Typic Acrorthox ) could be discriminated, mainly, in function of
their albedo (first Principal Component - PC1). The variation in
the shape of the spectra, associated with PC2 and PC3, allowed the
discrimination of PV in relation to LR and LE; (b) the
relationships among the laboratory (IRIS) and field (AVIRIS)
spectra reflected the inherent differences between both data
sources mainly due to the influence of the atmosphere; c) the
pixel contamination by residues of photosynthetic or
non-photosynthetic vegetation affected the correlations between
the two groups of data, especially in the 500-1,220 nm spectral
region; (d) the soils in the studied area are spectrally dominated
by the presence of hematite and kaolinite, according to the
results obtained from the use of the SFF technique. The results
showed the potential of imaging spectroscopy in characterizing the
albedo variations, shapes of the spectra and mineral absorption.
Thus, by using this approach, it was possible to discriminate
different tropical soil classes and to identify surface materials
on a pixel basis.",
committee = "Epiphanio, Jos{\'e} Carlos Neves (presidente/orientador) and
Galvao, L{\^e}nio Soares (orientador) and Novo, Evlyn Le{\~a}o
de Moraes and Antunes, Mauro Ant{\^o}nio Homem and Accioly,
Luciano Jos{\'e} de Oliveira",
copyholder = "SID/SCD",
englishtitle = "Hyperspectral remote sensing for mineral characterization and
identification in tropical soils",
label = "8602",
language = "pt",
pages = "194",
ibi = "6qtX3pFwXQZ4PKzA/iQQHK",
url = "http://urlib.net/ibi/6qtX3pFwXQZ4PKzA/iQQHK",
targetfile = "publicacao.PDF",
urlaccessdate = "08 maio 2024"
}