A Recurrent Deep Learning-Based Monthly Deforestation Prediction Model in Eight Areas for Brazilian Amazon: A Pilot Study

Deforestation rates in Brazilian Amazon continue to increase and addressing deforestation requires coordinated action at multiple levels, as well as international cooperation despite various strategies and action at multi-levels. To promote such strategies and action, this paper proposed a recurrent deep learning-based model, which predicts monthly deforestation events (will occur or not) at 1 km × 1 km meshes. The model was trained and evaluated by using about seven-year deforestation data (August 2016–September 2023) of eight areas in the Brazilian Amazon from the Real-Time Deforestation Detection System. By using the most recent two-year data, it was clear that the monthly prediction has a reasonable performance for recall, but very low precision. Thus, future work in the monthly prediction of deforestation should focus on refining model accuracy by incorporating more diverse data sources and improving temporal resolution.