@InProceedings{AdamiGBCVRNBOSE:2017:EsAcMa,
author = "Adami, Marcos and Gomes, Alessandra Rodrigues and Belluzzo, Amanda
Pinoti and Coelho, Andr{\'e}a dos Santos and Valeriano, Dalton de
Morisson and Ramos, Felipe de Souza and Narvaes, Igor da Silva and
Brown, Irving Foster and Oliveira, Ivanilson Dias de and Santos,
Lucyana Barros and Eduardo, Luis",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {} and {} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "A confiabilidade do PRODES: estimativa da acur{\'a}cia do
mapeamento do desmatamento no estado Mato Grosso",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "4189--4196",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "PRODES is completing almost thirty years of uninterrupted
monitoring of clear-cut deforestation over the Brazilian Amazon.
Until now, no estimate of its mapping accuracy has been made. In
this sense, this article brings a first approximation of mapping
accuracy estimation of PRODES deforested areas, taking as example
the state of Mato Grosso for the year 2014. For this, a random
sampling panel was constructed, stratified with two strata, the
deforestation of 2014 and the remaining forest. The sample size
was calculated using the binomial function. In addition, a web
platform was built to evaluate the points drawn by three
independent evaluators. The global accuracy of the mapping of
deforestation for the state of Mato Grosso, for the year 2014 was
94.5%, and may vary between 92.4% and 96.5%, in the evaluated
scenario there was no class discordance to be found. Regarding the
Forest class, the user accuracy was 90.5% and the producer''s
accuracy was 88.4%, this imbalance between user accuracy and
producer accuracy indicates that there is a tendency for the
forest class area to be underestimated for this mapping, in this
year.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "59299",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSM2LF",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM2LF",
targetfile = "59299.pdf",
type = "Floresta e outros tipos de vegeta{\c{c}}{\~a}o",
urlaccessdate = "27 abr. 2024"
}