@InProceedings{JesusSetMorCânMel:2015:EfCoAt,
author = "Jesus, Silvia Cristina de and Setzer, Alberto and Morelli, Fabiano
and C{\^a}ndido, Pietro de Almeida and Melchiori, Arturo
Emiliano",
affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}
and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Efeito da corre{\c{c}}{\~a}o atmosf{\'e}rica na
classifica{\c{c}}{\~a}o de {\'{\i}}ndices espectrais para o
mapeamento de {\'a}reas queimadas",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "368--375",
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 = "This paper investigates the impact of atmospheric correction (AC)
for medium-resolution imagery in the mapping of fire scars when
using automatic classification based on the spectral composite
indexes NBR, dNDVI and dNBR. 11 Landsat-5/TM scenes of a same
Cerrado area in 2005-2006 provided 9 time-consecutive pairs in
which a visual analysis provided the reference mapping of burned
areas. Automatic digital classification of the three indexes with
10 output classes, including one specific for burn scars, was
compared with and without the use of the so-called 6S AC
algorithm. Results show that atmospherically corrected and
uncorrected data are highly correlated (R2\≈1). The values
in the contingency tables for both procedures are not
significantly different; considering AC and non-AC values for all
the data, the overall accuracy is above 99% for both, the product
accuracy for scars is 79.6% and 82.6%, and the user accuracy is
92.2% and 94.3%, respectively. In conclusion, the mapping of fire
scars in medium-resolution imagery doesnt require atmospheric
correction when the most common indexes for burned area estimates
are used with automatic classification, what may simplify the
processing chain of large image datasets; however, this may not be
the case in non-automatic classification, when surface reflectance
thresholds are defined for individual scenes and applications.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "76",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM458B",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM458B",
targetfile = "p0076.pdf",
type = "Degrada{\c{c}}{\~a}o de florestas",
urlaccessdate = "08 maio 2024"
}