Towards the automatic monitoring of deforestation in Brazilian rainforest

Deforestation is considered one of the main environmental threats to the ecological balance on the planet. At the same time, monitoring changes in forest cover is a major challenge, especially in Brazil, a country with continental dimensions, a vast coverage of tropical forests, and an accelerated ongoing process of illegal deforestation. This study aims to propose and present an integrated automatic methodology for monitoring changes in forest cover, to enable “near real-time” monitoring of vast territorial extensions. Based on the application of Fully Convolutional Neural Networks (FCNs) combined with a logistic growth model, the methodology is aimed at allowing accurate detection of changes in forest cover based on the multitemporal assessment of satellite images. The results show that the combination of the two approaches make the methodology able to pinpoint deforestation processes. The applicability of the methodology is demonstrated for the Amazon and Atlantic Rainforest biomes, which are important areas of tropical forests in Brazil. In addition to enabling the agile and accurate identification of forest cover losses and providing efficient computing, the comparative results show that the methodology can be applied to issue alerts of suspected deforestation activity in standalone automatic monitoring systems, and also as a complementary tool to existing systems currently under operation.