The increase in extreme weather events caused by climate change has become more observable in recent years and can have profound impacts on the forest ecosystem. As climate change fosters more intense storms, we are expecting the cycle of forest damage and regrowth, referred to as the disturbance regime, to occur more frequently, altering forest dynamics and potentially reducing carbon storage. Therefore, it is critical to understand the impacts of these severe storms on carbon dynamics.
Identifying vulnerable regions can significantly aid in swift disaster management following storms, helping preserve our forest resources and the crucial role they play in mitigating climate change through informed and timely interventions.
This project leverages multi-sensor land and atmosphere remote sensing observations, climate reanalysis data, and earth system models combined with statistical methods and machine learning to understand the spatial patterns of ecosystem carbon dynamics affected by extreme storms from local to global scale. This research will improve the coupling of the land and atmosphere carbon dynamics in Earth System Models and is crucial for understanding the weakening of forest carbon sink.
