@InProceedings{PletshPeSiKöArAnMo:2019:CoSpIn,
author = "Pletsh, Mikhaela Alo{\'{\i}}sia J{\'e}ssie Santos and Penha,
Thales Vaz and Silva J{\'u}nior, Celso Henrique Leite and
K{\"o}rting, Thales Sehn and Arag{\~a}o, Luiz Eduardo Oliveira e
Cruz de and Anderson, Liana Oighenstein and Morelli, Fabiano",
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 {Centro Nacional de Monitoramento e Alertas de
Desastres Naturais (CEMADEN)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Combination of spectral indices for burned area detection in the
brazilian amazonia",
booktitle = "Anais...",
year = "2019",
editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco
and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
pages = "1248--1251",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
keywords = "Rainforest, Landsat-8, Spectral Index, Forest Fires, Burnt Area.",
abstract = "Spectral Indices (SI) are widely used for remote sensing
application because they enhance targeted features in optical
images through the algebraic combination of spectral bands. There
is a large variety of SI, in which the performance varies
depending on the user's application. Considering the different
emphases that spectral indices may offer, here we present a
test-case based on the combination of 10 SI in a three channels
remote sensing image (Red; Green; Blue - RGB) aiming to highlight
burned areas from other targets such as vegetation and water. This
process generated 120 possible combinations without repetition.
With spatial resolution of 30m, the proposed method was able to
achieve an accuracy between 0,21 and 0.86, according to Cohen's
Kappa coefficient. The two groups of indices MIRBI, NBR2, EVI,
MNDWI and CSI; and BAI, NBR and NDVI were the most inaccurate and
accurate indices, respectively, identified for the study site.",
conference-location = "Santos",
conference-year = "14-17 abril 2019",
isbn = "978-85-17-00097-3",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3TUPK42",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3TUPK42",
targetfile = "97276.pdf",
type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
urlaccessdate = "2024, Apr. 28"
}