Selective Logging Detection Via Time-Series Satellite Images

Selective logging represents a primary driver of forest degradation, being early germs of the deforestation process. It negatively impacts the remaining forests, leading to biodiversity losses, and catalyzing in the long term the climate changes. It is a process related to spatial-temporal changes in the forest areas, and consequently can be monitored by means of satellite images. In this work, we evaluated the potential of image time series of both Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data to detect selective logging using several variations of the Long ShortTerm Memory (LSTM) network. Particularly, we exploited complex-valued SAR images that contain both intensity and phase information to capture small changes caused by selective logging. The experiments using optical data achieved the highest accuracy of 98.21%, while those using SAR data reached a maximum accuracy of 72.68%. The results demonstrated the effectiveness of optical images and the potential of complex-valued SAR data for selective logging detection.