Monitoring forest disturbance using change detection on synthetic aperture radar imagery

Alice M. S. Durieux; Matthew T. Calef; Scott Arko; Rick Chartrand; Caitlin Kontgis; Ryan Keisler; Michael S. Warren

doi:10.1117/12.2528945

6 September 2019 Monitoring forest disturbance using change detection on synthetic aperture radar imagery

Alice M. S. Durieux, Matthew T. Calef, Scott Arko, Rick Chartrand, Caitlin Kontgis, Ryan Keisler, Michael S. Warren

Proceedings Volume 11139, Applications of Machine Learning; 1113916 (2019) https://doi.org/10.1117/12.2528945
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Although monitoring forest disturbance is crucial to understanding atmospheric carbon accumulation and biodiversity loss, persistent cloud cover, especially in tropical areas, makes detecting forest disturbances using optical remotely sensed imagery difficult. In Sentinel-1 synthetic aperture radar (SAR) images, forest clearings exhibit reduced backscatter as well as increased interferometric coherence. We combined SAR and Interferometric SAR metrics from Sentinel-1 data collected in Borneo between in 2017 and 2018 and applied unsupervised change detection methods to the time series. The results show that a simple log-ratio based detector performs similarly to a more sophisticated anomalous change detection algorithm. The log-ratio detector was deployed to compare a 2017 mean Sentinel-1 composite with a 2018 mean composite. Approximately 20000 newly deforested areas were identified in 2018, for a total of 3000 km2 . The findings suggest that leveraging SAR data to monitor deforestation has 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 continuous monitoring of deforestation. The improved time resolution associated with SAR observations in cloudy regions might enable the identification of areas at risk of deforestation early enough in the clearing process to allow preventive actions to be taken.

Citation Download Citation

Alice M. S. Durieux, Matthew T. Calef, Scott Arko, Rick Chartrand, Caitlin Kontgis, Ryan Keisler, and Michael S. Warren "Monitoring forest disturbance using change detection on synthetic aperture radar imagery", Proc. SPIE 11139, Applications of Machine Learning, 1113916 (6 September 2019); https://doi.org/10.1117/12.2528945

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