Modelling fire probability in the Brazilian Amazon using the maximum entropy method
Marisa G. Fonseca A F , Luiz Eduardo O. C. Aragão A B , André Lima A C , Yosio E. Shimabukuro A , Egidio Arai A and Liana O. Anderson D E
+ Author Affiliations
- Author Affiliations
A Tropical Ecosystems and Environmental Sciences Laboratory (TREES), National Institute for Space Research (INPE), Caixa Postal 515, 12227-010, São José dos Campos, São Paulo, Brazil.
B College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK.
C Department of Geographical Sciences, University of Maryland (UMD), 4321 Hartwick Road, College Park, MD 20740, USA.
D National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), Parque Tecnológico de São José dos Campos, Estrada Dr Altino Bondensan, 500, 12247-016, São José dos Campos, São Paulo, Brazil.
E Environmental Change Institute (ECI), University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.
F Corresponding author. Email: marisa_fonseca@yahoo.com.br
International Journal of Wildland Fire 25(9) 955-969 https://doi.org/10.1071/WF15216
Submitted: 12 December 2015 Accepted: 17 May 2016 Published: 19 July 2016
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.
Additional keywords: anthropogenic ignition, climate, machine learning, MESS analysis, MODIS, tropical forest.
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