@MastersThesis{Neves:2017:MiDaSe,
author = "Neves, Alana Kasahara",
title = "Minera{\c{c}}{\~a}o de dados de sensoriamento remoto para
detec{\c{c}}{\~a}o e classifica{\c{c}}{\~a}o de {\'a}reas de
pastagem na Amaz{\^o}nia Legal",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2017",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2017-02-15",
keywords = "pasto limpo, pasto sujo, {\'a}rvore de decis{\~a}o, s{\'e}ries
temporais, TerraClass, herbaceous pasture, shrubby pasture,
decision tree, time series.",
abstract = "Aproximadamente 60\% das {\'a}reas desflorestadas na
Amaz{\^o}nia Legal s{\~a}o ocupadas por pastagens. A
expans{\~a}o das {\'a}reas de pastagem sobre {\'a}reas de
floresta pode ser associada a fatores como o mercado de terras e a
perda de produtividade da pastagem ao longo do tempo. De toda
forma, essa expans{\~a}o permanece como um obst{\'a}culo ao
combate ao desflorestamento. A detec{\c{c}}{\~a}o e
avalia{\c{c}}{\~a}o das condi{\c{c}}{\~o}es das pastagens
permitem melhores monitoramento e controle, assim como a
identifica{\c{c}}{\~a}o de {\'a}reas prop{\'{\i}}cias para a
recupera{\c{c}}{\~a}o. Dentro deste contexto, o objetivo deste
trabalho {\'e} desenvolver uma metodologia de reconhecimento de
{\'a}reas de pastagem na Amaz{\^o}nia, com base na
detec{\c{c}}{\~a}o e classifica{\c{c}}{\~a}o por meio de
atributos de s{\'e}ries temporais e t{\'e}cnicas de
minera{\c{c}}{\~a}o de dados, de acordo com as
condi{\c{c}}{\~o}es da cobertura vegetal. A {\'a}rea de estudo
consiste em tr{\^e}s {\'o}rbitas/ponto do sat{\'e}lite Landsat
8 distribu{\'{\i}}das em quatro estados (AC, MT, RO e AM):
001/067, 226/068 e 231/067. Foram utilizadas imagens de
reflect{\^a}ncia de superf{\'{\i}}cie do sensor OLI do Landsat
8, referentes ao per{\'{\i}}odo entre abril de 2013 e dezembro
de 2015. As nuvens e sombras de nuvens foram detectadas pelo
algoritmo FMask e exclu{\'{\i}}das. A classifica{\c{c}}{\~a}o
foi feita em duas etapas: detec{\c{c}}{\~a}o de pastagem
(diferenci{\'a}-las de outros alvos da cena) e, posteriormente, a
classifica{\c{c}}{\~a}o das pastagens em Pasto Limpo e Pasto
Sujo. Para a caracteriza{\c{c}}{\~a}o destas classes, os
seguintes atributos foram usados: {\'{\i}}ndices de
vegeta{\c{c}}{\~a}o (NDVI, EVI, EVI2, SAVI e NDII),
fra{\c{c}}{\~o}es do Modelo Linear de Mistura Espectral
(fra{\c{c}}{\~o}es vegeta{\c{c}}{\~a}o, NPV e solo),
componentes da transforma{\c{c}}{\~a}o \emph{Tasselled Cap
(greenness, brightness e wetness)}, outros atributos espectrais e
atributos texturais. As duas etapas da classifica{\c{c}}{\~a}o
foram realizadas utilizando tr{\^e}s classificadores ({\'a}rvore
de decis{\~a}o, \emph{random forest} e rede neural) e duas
abordagens: por pixel e baseada em objetos. A
avalia{\c{c}}{\~a}o dos resultados de classifica{\c{c}}{\~a}o
baseou-se em trabalho de campo e interpreta{\c{c}}{\~a}o visual
de imagens do sat{\'e}lite RapidEye. Os resultados mostraram
melhoras nas taxas de acerto quando houve a utiliza{\c{c}}{\~a}o
de segmentos, uma vez que as pastagens possuem uma grande
quantidade de mistura de elementos em sua composi{\c{c}}{\~a}o.
Os modelos criados e avaliados na mesma cena obtiveram altas taxas
de acerto (pr{\'o}ximas a 90\%), entretanto n{\~a}o foram
capazes de classificar outras cenas com a mesma efici{\^e}ncia.
Quando amostras de duas cenas diferentes foram combinadas para a
gera{\c{c}}{\~a}o do modelo, as taxas de acerto ficaram
parecidas entre as imagens, por volta de 80\%. A maior
dificuldade esteve na separa{\c{c}}{\~a}o entre Pasto Limpo e
Pasto Sujo, uma vez que as pastagens na Amaz{\^o}nia variam de
acordo com muitos fatores: manejo adotado, tipo de solo, regime de
chuvas, tipo de gram{\'{\i}}nea utilizada e outros. ABSTRACT:
The highest percentage of deforested areas in the Legal Amazon is
occupied by pastures. The expansion of pasture areas over the
forest may be associated with factors such as land speculation and
loss of productivity over time. In any case, this expansion
remains an obstacle to fight against deforestation. The detection
and evaluation of pasture conditions allow better monitoring and
control it, as well as the identification of suitable areas for
recovery. In this context, the objective of this work is to
develop a methodology for the recognition of pasture areas in the
Amazon, based on the detection and classification by time series
attributes and data mining techniques, according to the vegetation
conditions. The study area consists of three path/rows of Landsat
8 satellite distributed in four Brazilian states (AC, MT, RO and
AM): 001/067, 226/068 and 231/067. Surface reflectance images from
OLI sensor (Landsat 8 satellite) were used for the period between
April 2013 and December 2015. Clouds and cloud shadows were
detected by the FMask algorithm and excluded from the dataset. The
classification was carried out in two steps: pasture detection
(differentiate them from other targets in the scene) and, later,
the classification of pastures between Herbaceous Pasture and
Shrubby Pasture. For the characterization of these classes, the
following attributes were used: vegetation indices (NDVI, EVI,
EVI2, SAVI and NDII), fractions of the Linear Spectral Mixture
Model (vegetation, NPV and soil), bands of the Tasselled Cap
Transformation (Greenness, Brightness and Wetness), other spectral
attributes and textured attributes. The two steps of
classification were performed using three classifiers (decision
tree, random forest and neural network) and two different
approaches: per pixel and object-based. The evaluation of the
classification results was based on fieldwork and visual
interpretation of RapidEye satellite images. The results showed
improvements in the accuracy when segments were used instead of
pixels, since pastures have a large amount of mixture of elements
in their composition. The models created and evaluated in the same
scene obtained high accuracy (close to 90\%), but they were not
able to classify other scenes with the same efficiency. When
samples from two different scenes were combined for model
generation, the accuracy was similar between the images, around
80\%. The greatest difficulty was in the separation between
Herbaceous Pasture and Shrubby Pasture, since pastures in Amazon
may vary according to some factors, such as: adopted management,
soil type, rainfall regime and type of grass used.",
committee = "Escada, Maria Isabel Sobral (presidente) and K{\"o}rting, Thales
Sehn (orientador) and Fonseca, Leila Maria Garcia (orientador) and
Adami, Marcos and Esquerdo, J{\'u}lio C{\'e}sar Dalla Mora",
copyholder = "SID/SCD",
englishtitle = "Remote sensing data mining to detect and classify pasture lands in
the Legal Amazon",
language = "pt",
pages = "101",
ibi = "8JMKD3MGP3W34P/3NAADAS",
url = "http://urlib.net/ibi/8JMKD3MGP3W34P/3NAADAS",
targetfile = "publicacao.pdf",
urlaccessdate = "19 abr. 2024"
}