Selective Logging-Related Land-Cover Class Discrimination in the Brazilian Amazon with Landsat-8 and Sentinel-2 Products

Selective logging is an important component of forest degradation in the Brazilian Amazon. The detection and mapping of selective logging via satellite imagery remains challenging because spatial features associated with selective logging are generally small-scale, spatially heterogeneous, and short-lived disturbances in the forest. This study evaluated the potential of Sentinel-2 MSI imagery at 10 m and 20 m, and Landsat-8 OLI imagery at 30 m and pansharpened 15 m, to discriminate land-cover classes associated with selective logging in the state of Mato Grosso in the Brazilian Amazon for 2017 using the Random Forest algorithm. The resulting maps were used to characterize selective logging alerts from the Deter system and areas under Sustainable Forest Management Plans (SFMP). Sentinel-2 at 10 m achieved the highest overall accuracy, while Landsat-based products tended to estimate larger areas of exposed soil and, in some cases, regeneration. Deter polygons showed higher proportions of exposed soil and degradation and lower remaining forest cover than SFMP areas, suggesting that Deter alerts tend to capture more advanced stages of visible forest disturbance. Overall, the results indicate that differences in overall accuracy among the evaluated products were small, but class-specific performance and spatial representation patterns remain important for interpreting selective logging-related disturbance in the Amazon.