Imagery from medium resolution satellites, such as Landsat, have long been used to map forest disturbances in the tropics. However, the Landsat spatial resolution (30 m) has often been considered too coarse for reliably mapping small-scale selective logging. Imagery from the recently launched Sentinel-2 sensor, with a resampled 10 m spatial resolution, may improve the detection of forest disturbances. This study compared the performance of Landsat 8 and Sentinel-2 data for the detection of selective logging in an area located in the Brazilian Amazon. Logging impacts in seven areas, which had governmental authorization for harvesting timber, were mapped by calculating the difference of a self-referenced normalized burn ratio (ΔrNBR) index over corresponding time periods (2016–2017) for imagery of both satellite sensors. A robust reference dataset was built using both high- and very-high-resolution imagery. It was used to define optimum ΔrNBR thresholds for forest disturbance maps, via a bootstrapping procedure, and for estimating accuracies and areas. A further assessment of our approach was also performed in three unlogged areas. Additionally, field data regarding logging infrastructure were collected in the seven study sites where logging occurred. Both satellites showed the same performance in terms of accuracy, with area-adjusted overall accuracies of 96.7% and 95.7% for Sentinel-2 and Landsat 8, respectively. However, Landsat 8 mapped 36.9% more area of selective logging compared to Sentinel-2 data. Logging infrastructure was better detected from Sentinel-2 (43.2%) than Landsat 8 (35.5%) data, confirming its potential for mapping small-scale logging. We assessed the impacted area by selective logging with a regular 300 m × 300 m grid over the pixel-based results, leading to 1143 ha and 1197 ha of disturbed forest on Sentinel-2 and Landsat 8 data, respectively. No substantial differences in terms of accuracy were found by adding three unlogged areas to the original seven study sites.

Context: Selective logging has been commonly mapped using binary maps, representing logged and unlogged forests. However, binary maps may fall short regarding the optimum representation of this type of disturbance, as tree harvest in tropical forests can be highly heterogeneous.Objectives: The objective of this study is to map forest disturbance intensities in areas of selective logging located in the Brazilian Amazon.Methods: Selective logging activities were mapped in ten forest management units using Sentinel-2 data at 10 m resolution. A spatial pattern analysis was applied to the logging map, using a moving window approach with different window sizes. Two landscape metrics were used to derive a forest disturbance intensity map. This map was then compared with actual disturbances using field data and a post-harvest forest recovery analysis.Results: Disturbed areas were grouped into five distinct disturbance intensity classes, from very low to very high. Classes high and very high were found to be related to log landings and large felling gaps, while very low intensities were mainly related to isolated disturbance types. The post-harvest forest recovery analysis showed that the five classes can be clearly distinguished from one another, with the clearest differences in the year of logging and one year after it.Conclusions: The approach described represents an important step towards a better mapping of selectively logged areas, when compared to the use of binary maps. The disturbance intensity classes could be used as indicators for forest monitoring as well as for further evaluation of areas under forest management.