A spatio-temporal Bayesian Network approach for deforestation prediction in an Amazon rainforest expansion frontier

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 Para 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. (C) 2019 Elsevier B.V. All rights reserved.