@InProceedings{CostaFonsKort:2015:ClPaCu,
author = "Costa, Wanderson Santos and Fonseca, Leila Maria Garcia and
Korting, Thales Sehn",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Classifica{\c{c}}{\~a}o de pastagens cultivadas e
forma{\c{c}}{\~o}es campestres nativas no Cerrado brasileiro a
partir da an{\'a}lise de s{\'e}ries temporais extra{\'{\i}}das
de {\'{\i}}ndices EVI do sensor MODIS",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "1516--1523",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "One of the most biodiverse regions on the planet, the Brazilian
Cerrado has an area of approximately 2 million km2 and it is the
second largest biome in Brazil. Among the land cover modifications
in the biome, over one fourth of its area has been changed into
cultivated pastures in the last few years. Categorizing types of
land use and cover in the Cerrado and its native formations is
important for protection policy and monitoring of the Brazilian
Cerrado. Based on remote sensing techniques, this work aims at
developing a methodology to map pasture and native grassland areas
in the biome. Data related to EVI vegetation indices obtained by
MODIS images were used to perform image classification. This study
encompasses a Cerrado area that comprises a region of Serra da
Canastra National Park and neighboring regions, that contains all
targets of interest. Support Vector Machines, Decision Trees and
Random Forests algorithms were compared, and the results showed
that the analysis of different attributes extracted from EVI
indices can aid in the classification process. As a means to
distinguish grassland and cultivated pasture zones, we obtained
accuracies up to 85,96% in the study area, identifying data and
attributes required to identify these areas by remote sensing
images.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "284",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM487R",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM487R",
targetfile = "p0284.pdf",
type = "An{\'a}lise de s{\'e}ries de tempo de imagens de sat{\'e}lite",
urlaccessdate = "27 abr. 2024"
}