Detecting tropical selective logging with C-band SAR data may require a time series approach

Abstract Selective logging is the primary driver of forest degradation in the tropics and reduces the capacity of forests to harbour biodiversity, maintain key ecosystem processes, sequester carbon, and support human livelihoods. While the preceding decade has seen a tremendous improvement in the ability to monitor forest disturbances from space, large-scale (spatial and temporal) forest monitoring systems have almost universally relied on optical satellite data from the Landsat program, whose effectiveness is limited in tropical regions with frequent cloud cover. Synthetic aperture radar (SAR) data can penetrate clouds and have been utilized in forest mapping applications since the early 1990s, but only recently has SAR data been widely available on a scale sufficient to facilitate pan-tropical selective logging detection systems. Here, a detailed selective logging dataset from three lowland tropical forest regions in the Brazilian Amazon was used to assess the effectiveness of SAR data from Sentinel-1, RADARSAT-2, and Advanced Land Observing Satellite-2 Phased Arrayed L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) for monitoring tropical selective logging. We built Random Forests models aimed at classifying pixel-based differences between logged and unlogged areas. In addition, we used the Breaks For Additive Season and Trend (BFAST) algorithm to assess if a dense time series of Sentinel-1 imagery displayed recognizable shifts in pixel values after selective logging. In general, Random Forests classification with SAR data (Sentinel-1, RADARSAT-2, and ALOS-2 PALSAR-2) performed poorly, having high commission and omission errors for logged observations. This suggests little to no difference in pixel-based metrics between logged and unlogged areas for these sensors, particularly at lower logging intensities. In contrast, the Sentinel-1 time series analyses indicated that areas under higher intensity selective logging (> 20 m3 ha−1) show a distinct spike in the number of pixels that included a breakpoint during the logging season. BFAST detected breakpoints in 50% of logged pixels and exhibited a false alarm rate of approximately 20 m3 ha−1) within the Amazon.