Monitoring sustainable logging in an Amazon forest under management using deep learning and Planet NICFI imagery

Protected forests in the Brazilian Amazon safeguard remnants of undisturbed forests. However, some of these forests are legally managed by sustainable logging practices and may also be exposed to improper or illegal logging, increasing forest degradation in these areas. This study assesses the potential and limitations of REDD+AI – a deep learning-based approach that uses high-resolution Planet NICFI imagery – to map sustainable forest management in the Jamari National Forest (south-western Brazilian Amazon) and compare its relative performance to other forest disturbance datasets. To validate the satellite-based estimates, we used a stratified sampling design to estimate REDD+AI statistical accuracy of logging detections based on visual interpretation of time series of high spatial resolution imagery. We also used field data provided by the Brazilian Forest Service to estimate the percentage of detections inside management units. Three datasets of forest disturbance and degradation detection were compared: DETER – INPE; GFC – UMD; and TMF – JRC. Our findings showed that REDD+AI had higher overall accuracy (95.65% ± 1 [95% CI]) than DETER (87.40%), TMF (86.77%) and GFC (86.55%) to map logging over the Jamari Forest. Furthermore, a higher spatial agreement with the production units was found for REDD+AI (83.07%), followed by DETER (21.89%), TMF (4.47%) and GFC (2.36%). We estimate that 13.74% of the Jamari forest has undergone some level of logging between 2016 and 2022, of which approximately 71% were located within the management zones and 29% outside the management zones, strongly indicating anthropism-related and illegal logging in this protected area. This study highlights REDD+AI as a tool for monitoring logging in tropical forests under management which can provide vital support for government oversight efforts to combat illegal logging and ensure compliance within sustainable forest management practices using modern remote sensing applications.