@Article{FonsecaArLiShArAn:2016:MoFiPr,
author = "Fonseca, Marisa Gesteira and Arag{\~a}o, Luiz Eduardo Oliveira e
Cruz de and Lima, Andr{\'e} and Shimabukuro, Yosio Edemir and
Arai, Eg{\'{\i}}dio and Anderson, Liana O.",
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)}",
title = "Modelling fire probability in the Brazilian Amazon using the
maximum entropy method",
journal = "International Journal of Wildland Fire",
year = "2016",
volume = "25",
number = "9",
pages = "955--969",
keywords = "anthropogenic ignition, climate, machine learning, MESS analysis,
MODIS, tropical forest.",
abstract = "Fires are both a cause and consequence of important changes in the
Amazon region. The development and implementation of better fire
management practices and firefighting strategies are important
steps to reduce the Amazon ecosystems' degradation and carbon
emissions from land-use change in the region. We extended the
application of the maximum entropy method (MaxEnt) to model fire
occurrence probability in the Brazilian Amazon on a monthly basis
during the 2008 and 2010 fire seasons using fire detection data
derived from satellite images. Predictor variables included
climatic variables, inhabited and uninhabited protected areas and
land-use change maps. Model fit was assessed using the area under
the curve (AUC) value (threshold-independent analysis), binomial
tests and model sensitivity and specificity (threshold-dependent
analysis). Both threshold-independent (AUC\≤0.919±0.004)
and threshold-dependent evaluation indicate satisfactory model
performance. Pasture, annual deforestation and secondary
vegetation are the most effective variables for predicting the
distribution of the occurrence data. Our results show that MaxEnt
may become an important tool to guide on-the-ground decisions on
fire prevention actions and firefighting planning more effectively
and thus to minimise forest degradation and carbon loss from
forest fires in Amazonian ecosystems.",
doi = "10.1071/WF15216",
url = "http://dx.doi.org/10.1071/WF15216",
issn = "1049-8001",
language = "en",
urlaccessdate = "28 abr. 2024"
}