Forest Resilience and Compound Events (FoRCE)

This PhD project aims to advance our understanding of forest disturbances and their association with compound extreme weather events, focusing on forest margins as biodiversity hotspots. Open and proprietary Earth observation (EO) data, including airborne remote sensing and machine learning techniques, to detect and monitor forest damage will be used. The project will explore the potential climate drivers, including both singular and interlinked extremes, that contribute to these disturbances. It will also use models such as ForestGALES and 3PG-SoNWaL to predict the risk impacts on forest function, including wind risk and climate impacts on productivity. This research has the potential to provide valuable insights for informed stand-level management decisions in the productive forestry sector, in light of future climate shifts.