@InProceedings{VedovatoJacPesLimAra:2015:DeBuFo,
author = "Vedovato, Laura Barbosa and Jacon, Aline Daniele and Pess{\^o}a,
Ana Carolina Moreira and Lima, Andr{\'e} and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Detection of burned forests in Amazonia using the Normalized Burn
Ratio (NBR) and Linear Spectral Mixture Model from Landsat 8
images",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "2984--2991",
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 = "Wildfires represent a major disturbance factor leading to
environmental changes with local or regional impacts. In the
Amazon, although fire is associated with several land-practices,
the long dry season in some regions, especially during extreme
droughts, makes vegetation more susceptible to uncontrolled fires.
Furthermore,the burn of biomass is a considerable source of
atmospheric pollution, including carbon dioxide, a major
greenhouse gas. Due to the large geographical extent of fires at
regional and global scales, remote sensing approaches became
relevant in the last decades. In this paper, we compare two
different methodologies of fire detection for Amazon region and
evaluate possible spectral confusions these two methods can
generate on the analysis. We compared the Normalized Burn Ratio
(NBR) and the Linear Spectral Mixture Model data extracted from
high resolution satellite Landsat 8 image. Our results indicate
that the detection of burned forests areas using the LSMM index
performs better over fragmented landscapes, with a index Kappa
of0.68 and 0.66 against a index Kappa for NBR of 0.52 and 0.52, in
the study sites B and C respectively. Considering areas less
fragmented as study site A in this study, both methodologies
showed the same Kappa value (0.88). Thus, considering the
complexity of Amazonian landscapes, which encompass both high and
low fragmentation areas, the LSMM index is likely to perform
better in the detection of burnt forests than the NBR index.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "594",
language = "en",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM4AJN",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4AJN",
targetfile = "p0594.pdf",
type = "Monitoramento e modelagem ambiental",
urlaccessdate = "03 jun. 2024"
}