Monitoring fine-scale natural and logging-related tropical forest degradation using Sentinel-1

Highlights

• We develop a physical radar-based method to monitor tropical forest degradation.
• We demonstrate detection of natural and logging disturbances as small as 100 m2.
• Validation uses spatially exhaustive, multitemporal and high-resolution drone data.
• Detection accuracies exceed 80% for natural and logging disturbances above 300 m2.
• Our method outperforms existing alternatives.

Abstract
Tropical forest degradation results in severe biomass loss and biodiversity decline. However, fine-scale natural and logging-related forest disturbances remain difficult to trace, both from the ground as well as remotely. Comprehensive, landscape scale characterization of anthropogenic forest degradation requires accurate accounting of baseline canopy disturbance rates and patterns. This paper has evaluated the feasibility of radar data for detecting canopy gaps created by natural and anthropogenic mechanisms at large spatial scale by assessing the extent to which the Sentinel-1 C-band radar signal can be used to map fine-scale disturbances in both naturally disturbed and logged landscapes. Our physical-based method detects disturbances based on changes in backscatter resulting from radar shadow and/or layover. We apply various detection thresholds to explore the trade-off between detection and false detection and validate our method in study areas for which spatially exhaustive drone-based canopy gap maps are available for validation, namely Barro Colorado Island nature reserve (median gap area: 39 m2) and five logging concessions in the Congo Basin (median gap area: 237 m2). With a moderate threshold (2.5 dB backscatter reduction), we reach detection rates above 65 percent for disturbances above 200 m2 in both naturally disturbed and logged areas. Detection rates were primarily driven by gap area; gap depth had a smaller, yet significant, influence. These results significantly improve on operational forest disturbance products and previous studies on fine-scale disturbance detection using Sentinel-1 radar. Moreover, the improved insight in detection accuracies of anthropogenic disturbances fosters a move towards monitoring forest dynamics across large scales at which we cannot be certain whether the disturbance driver is anthropogenic or natural.

Keywords: Selective logging; Natural disturbance; Radar remote sensing; Forest degradation.

• Monitoring fine-scale natural and logging-related tropical forest degradation using Sentinel-1
  Welsink, A. , Dupuis, Chloé , Cué la Rosa, L.E. , Weghorst, S.H. , van der Zee, J.J. , van der Woude, S.P. , Pena Claros, M. , Herold, M. , Fesenmyer, Kurt , Reiche, J. (2025) Remote Sensing of Environment (2025), Volume: 328 - ISSN 0034-4257