Monitoring forest disturbance using change detection on synthetic aperture radar imagery

摘要

Although monitoring forest disturbance is crucial to understanding atmospheric carbon accumulation andbiodiversity loss, persistent cloud cover, especially in tropical areas, makes detecting forest disturbances usingoptical remotely sensed imagery difficult. In Sentinel-1 synthetic aperture radar (SAR) images, forest clearingsexhibit reduced backscatter as well as increased interferometric coherence. We combined SAR and InterferometricSAR metrics from Sentinel-1 data collected in Borneo between in 2017 and 2018 and applied unsupervised changedetection methods to the time series. The results show that a simple log-ratio based detector performs similarly to amore sophisticated anomalous change detection algorithm. The log-ratio detector was deployed to compare a 2017mean Sentinel-1 composite with a 2018 mean composite. Approximately 20000 newly deforested areas wereidentified in 2018, for a total of 3000 km~2. The findings suggest that leveraging SAR data to monitor deforestationhas the potential to achieve better performance than Global Forest Watch, the current Landsat based gold standard.Future work will leverage the short revisit time (6-12 days) of Sentinel-1 as an opportunity for continuousmonitoring of deforestation. The improved time resolution associated with SAR observations in cloudy regionsmight enable the identification of areas at risk of deforestation early enough in the clearing process to allowpreventive actions to be taken.