Due Diligence for Deforestation-Free Supply Chains with Copernicus Sentinel-2 Imagery and Machine Learning

Abstract

At COP26, the Glasgow Leaders Declaration committed to ending deforestation by 2030. Implementing deforestation-free supply chains is of growing importance to importers and exporters but challenging due to the complexity of supply chains for agricultural commodities which are driving tropical deforestation. Monitoring tools are needed that alert companies of forest losses around their source farms. ForestMind has developed compliance monitoring tools for deforestation-free supply chains. The system delivers reports to companies based on automated satellite image analysis of forest loss around farms. We describe an algorithm based on the Python for Earth Observation (PyEO) package to deliver near-real-time forest alerts from Sentinel-2 imagery and machine learning. A Forest Analyst interprets the multi-layer raster analyst report and creates company reports for monitoring supply chains. We conclude that the ForestMind extension of PyEO with its hybrid change detection from a random forest model and NDVI differencing produces actionable farm-scale reports in support of the EU Deforestation Regulation. The user accuracy of the random forest model was 96.5% in Guatemala and 93.5% in Brazil. The system provides operational insights into forest loss around source farms in countries from which commodities are imported.