A Novel Integrated ML Approach Utilizing Radar & Satellite Imagery for Selective Logging Detection

Illegal selective logging is devastating to the world’s biodiversity and forests, and many countries face challenges in monitoring these forests. In the Amazon, 94\% of deforestation is attributed to illegal activities, primarily conducted through selective logging—a practice that has received far less research attention than clear-cut deforestation. This study introduces a novel machine learning approach that enhances the detection of selective logging by integrating Sentinel-1 Synthetic Aperture Radar (SAR) data with Sentinel-2 optical imagery, moving beyond conventional methods that rely on a single data source. Requisite data was collected through Google Earth Engine for Sentinel-1 and Sentinel-2 and used to create three new datasets of 134,606 samples (5x other studies). These datasets were then used to train and evaluate three different classification architectures: Convolutional Neural Networks (CNN), Random Forest, and Gradient Boosted Trees. The CNN model trained on both modalities outperformed CNNs trained on individual Sentinel datasets, achieving an accuracy of 95.08\%, compared to 57.51% for Sentinel-1 and 91.95% for Sentinel-2. The Gradient Boosted Trees model, while slightly less accurate with a 94.39% accuracy and a 94.03 F1 score, required significantly less computational power. Our findings demonstrate that the integration of optical and radar data substantially improves the prediction of selective logging activities, achieving up to a 7.08% improvement over previous selective logging detection research and offering a scalable solution for forestry monitoring in resource-limited settings.