Early Deforestation Detection in the Tropics using L-band SAR and Optical multi-sensor data and Bayesian Statistics

The growing availability of medium-resolution optical and radar satellite observations has prompted the development of synergistic change detection methodologies. Timely forest change detection, particularly early deforestation, is crucial for preventing illegal activities. This study proposes and evaluates an innovative model that integrates ALOS-2 PALSAR-2 L-band data with optical data from Landsat and Sentinel-2 to detect early deforestation, defined as the initial transition from stable to logged forest. Our model employs a 2-tier approach, combining harmonic curve fitting and z-scores to calculate differences between the time series. Bayesian updating statistics are then used to derive change probabilities. We comprehensively assessed the spatial and temporal detection accuracy of early deforestation maps generated by each sensor type, both individually and in combination. The integrated L-band Synthetic Aperture Radar (SAR) and optical method demonstrated the best performance, achieving a user’s accuracy of 99.19 ± 0.0081% ( ± 95 confidence interval) and a mean detection time lag of just 16 days. For comparison, L-band SAR data alone yielded a user’s accuracy of 93.70% ( ± 0.0333) with a mean time lag of 67 days, primarily due to ALOS-2’s lower repeat frequency. Optical-derived detections achieved a user’s accuracy of 98.39% ( ± 0.0113) and a mean time lag of 20 days. These findings confirm that combining radar and optical datasets significantly improves both detection accuracy and timeliness. Furthermore, detections were consistently captured shortly after logging activities, well before subsequent forest disturbances, underscoring true early deforestation. The high detection accuracies validate that both individual and combined L-band SAR and optical data can reliably detect early deforestation in this tropical region. We anticipate that the longer detection time lags observed with ALOS-2 PALSAR-2 will substantially improve with upcoming L-band SAR missions, such as NISAR and ALOS-4 PALSAR-3, which promise significantly enhanced global temporal sampling. • L-band RFDI time series using harmonic fitting and the Bayesian Updating of Land Cover algorithm generates a highly accurate early deforestation map in a tropical area with a user’s accuracy of 93.70%. • An integrated multi-sensor L-band SAR and optical (Landsat and Sentinel- 2) change detection system produces better spatial and temporal detection results (commission error of 0.81%, and all detections within a time window that represents early deforestation), than change maps created by individual sensor types. • Adding L-band SAR data reduces the mean time lag of early deforestation detection from 20 days, using only optical data, to 16 days if all sensor types are combined.

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