@InProceedings{FonsecaLimAndShiAra:2015:AvPrMo,
author = "Fonseca, Marisa Gesteira and Lima, Andr{\'e} and Anderson, Liana
Oighenstein and Shimabukuro, Yosio Edemir and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
affiliation = "{} and {} and {} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Avalia{\c{c}}{\~a}o preliminar da modelagem de queimadas na
Amaz{\^o}nia brasileira utilizando o princ{\'{\i}}pio de
M{\'a}xima Entropia",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "1868--1875",
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 = "Climate change, forest fragmentation, and the increase in
secondary vegetation cover are expected to amplify fire incidence
in the Amazon. The negative impacts of forest fires on
biodiversity, precipitation and the dynamics of atmospheric
circulation, human health, forest structure, biomass and carbon
stock have been recognized in the literature. The development and
implementation of better fire management practices and
firefighting strategies are therefore important steps to reduce
forest degradation and carbon emissions from land use change in
the region. Here we extend the application of Maximum Entropy
method (Maxent) to model fire risk in the Brazilian Amazon using
an innovative combination of climatic variables (sea surface
temperature anomalies, precipitation and accumulated water
deficit), inhabited and uninhabited protected areas and land use
(deforestation, pasture, and forest regeneration) maps. The model
was calibrated to forecast hot pixels occurrence in September 2008
and September 2010, two years of contrasting fire incidence. Tests
were carried out to determine the regularization multiplier (a
user defined parameter that influences model complexity) that
maximizes model fit. Model fit was assessed using the AUC value
(threshold independent analysis), binomial tests and model
sensitivity and specificity (threshold dependent analysis). Both
threshold dependent and independent model evaluations showed that
Maxent can be successfully used in operational routines for
monthly hot pixels occurrence prediction and hence applied in
prevention programs and firefighting planning.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "370",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM49D9",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM49D9",
targetfile = "p0370.pdf",
type = "Monitoramento e modelagem ambiental",
urlaccessdate = "27 abr. 2024"
}