%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2020/01.03.17.03
%2 sid.inpe.br/mtc-m21c/2020/01.03.17.03.24
%@doi 10.1016/j.spasta.2019.100393
%@issn 2211-6753
%T A spatio-temporal Bayesian Network approach for deforestation prediction in an Amazon rainforest expansion frontier
%D 2020
%8 Mar.
%9 journal article
%A Silva, Alexsandro Cândido de Oliveira,
%A Fonseca, Leila Maria Garcia,
%A Körting, Thales Sehn,
%A Escada, Maria Isabel Sobral,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress alexsandro.silva@inpe.br
%@electronicmailaddress leila.fonseca@inpe.br
%@electronicmailaddress thales.korting@inpe.br
%@electronicmailaddress isabel.escada@inpe.br
%B Spatial Statistics
%V 35
%P e100393
%K Bayesian Networks, Spatio-temporal modeling, Environmental modeling, Deforestation, Brazilian Amazon forest.
%X In the last decade, Brazil has successfully managed to reduce deforestation in the Amazon forest. However, continued increases in annual deforestation rates call for environmental modeling to support short-term decision-making. This paper presents the functioning of a stepwise spatio-temporal Bayesian Network approach for spatially explicit analysis of deforestation risk based on observation data. The study area comprises a deforestation expansion frontier located in the southwest of Pará state, Brazil. The proposed approach has been successful in estimating deforestation risk over the years. Among the selected variables to compose the Bayesian Network model, distance from hot spots and distance from degraded areas present the highest contribution, while protected areas variable present a significant mitigation effect on the phenomenon. Accuracy assessment indices corroborate the agreement between deforestation events and predictions.
%@language en
%3 silva_spatio.pdf