Land and Climate Seminar - Deep Learning for Predicting Counterfactual Deforestation in Protected Areas in the Amazon

Speaker: Kathy Baylis

Deep Learning for Predicting Counterfactual Deforestation in Protected Areas in the Amazon

Pratyush Tripathy, Kathy Baylisa, Catharina Latka, Robert Heilmayr, Ryan Ashraf

Abstract

The Amazon rainforest plays a critical role in absorbing billions of tons of carbon and is home to rich biodiversity. Yet, alarming rates of deforestation threaten its survival, with one-fifth already lost and more expected by 2030. Conservation efforts, such as establishing protected areas, aim to slow this loss, but assessing their true impact has been challenging due to differences in the locations chosen to be protected versus other forests, and the highly non-linear drivers of deforestation. In our research, we use a neural network trained on unprotected forest areas, to forecast deforestation. We then use these forecasts to predict what deforestation would have occurred without protection. We first predict whether the area would be deforested at all, and for those areas where we predict positive deforestation, we predict the amount of deforestation. Our findings provide new insights for evaluating conservation strategies, helping policymakers and environmentalists target interventions more effectively to preserve the Amazon and combat climate change.