Utilising Sentinel-1 imagery to provide accurate detection of selective logging in the tropics

Abstract

Forest degradation due to illegal logging and sub-optimal logging practices is one of the primary sources of carbon emissions in the LULUCF sector in Gabon. Space-based radar and change detection can provide an affordable and timely forest monitoring tool to assist in verifying the effectiveness of sustainable forest management. The detection method employed in this study made use of Sentinel-1 radar and the BFAST change detection method to monitor a period of change, from December 2021 to July 2022. Field data obtained from a Gabonese forestry company allowed for effective accuracy assessment, the results of which indicated that partial success was achieved, particularly in detecting larger scale changes, with an overall accuracy of 53.2%. When the same accuracy assessment was applied to the deforestation alerts produced by Global Forest Watch's RADD system, the method developed in this study performed comparatively well.