Creating near real-time alerts of illegal gold mining in the Peruvian Amazon using Synthetic Aperture Radar

Abstract

Abstract Peru’s Southeastern Amazon deforestation trends can be attributed to alluvial gold mining. Illegal mining occurring in forestry concessions, national parks, and the territories of Indigenous People Organizations is of particular concern. We present a methodology to create near real-time alerts of deforestation caused by alluvial gold mining. A time series of Sentinel-1 Synthetic Aperture Radar (SAR) data from February to December 2022 is created in Google Earth Engine (GEE) and assessed using Morton Canty’s Omnibus Q-test change detection algorithm. Resulting detections are validated with high-resolution optical imagery from Planet NICFI’s monthly basemaps and Planet Scope daily imagery. The alerts identify the location and timing of large areas (group pixels of <1 ha) of forest loss due to gold mining activities within buffer zones of indigenous territories and protected areas. The overall accuracy of the forest loss analysis conducted with this change detection method was 99.98%, based on an independent accuracy assessment. This effort has resulted in a public web platform that displays the location of near real time alerts, so Peruvian enforcement agencies can more effectively allocate resources and staff to addressing active illegal mining operations. These results demonstrate the applicability of open-source SAR data to monitor forest loss over areas where cloud cover is more persistent and to improve tools that deliver timely, critical information to decision-makers. Future applications of our method could expand this approach to other drivers of deforestation.