Automatic deforestation driver attribution using deep learning on satellite imagery

Abstract

Deforestation is a leading contributor to greenhouse gas emissions globally. Understanding the direct drivers of forest loss is essential for developing targeted forest conservation and management policies. However, this data is hard to collect at scale due to the complexity of forest loss drivers and expertise required for accurately identifying them. To address this challenge, we developed a deep learning model called ForestNet which uses publicly available satellite imagery to automatically classify the drivers of primary forest loss. We validated ForestNet on a test set of expert-annotated forest loss events and showed that ForestNet achieved high performance across four major driver classes. We used ForestNet to identify these drivers on over 2 million forest loss events in Indonesia between 2012 and 2019, with significant improvement in spatial and temporal resolution over previously available data. We found that plantations and smallholder agriculture were the primary direct drivers of deforestation in Indonesia during this period, accounting for 64 % of total forest loss. Deforestation has decreased steadily since 2012 after increasing steadily from 2001 to 2009 and peaking from 2009 to 2012, trends that we found are primarily due to changes in plantation-driven deforestation. Our approach can serve as a general framework for scalably attributing deforestation to specific drivers and can be extended to other regions of interest, providing a flexible and cost-effective way for countries to regularly monitor, understand, and address their unique and dynamic drivers of deforestation.