I am currently a PhD candidate (!) here at SEAL, focusing on how we can both quickly and accurately identify deforestation from multi-source satellite data in north-central Myanmar. Detecting forest disturbances is a hot topic within satellite remote sensing, and many papers have proposed different methods of change detection, with some applied operationally (e.g. CCDC, Global Forest Watch, DETER-B, etc). While these are all great, few studies have looked into the inherent tradeoffs between fast and slow detectors, and still fewer present disturbance alerts as anything other than binary. My research goal is to shed light on these tradeoffs and the implications for users, as well as develop a novel change detection method to map daily disturbance probability at landscape scale and at 10 m resolution. Ultimately the method will incorporate Bayesian statistics to include biophysical information and detection verification. Please see my personal website for more information.