Next generation forest dynamics modelling using remote sensing data

Ecosystems are threatened globally by climate change and biodiversity loss. Forest ecosystems are seen as a mitigator of climate change, even though these ecosystems are significantly threatened by climate change itself. How forests will absorb and store carbon in the future depends critically on individual species’ responses to climate change, therefore predicting the impact of climate change on forests is a priority.

The project seeks to develop wholly novel approaches to measuring and modelling forest dynamics by integrating existing information with data from new, cutting-edge remote sensing technologies using techniques drawn from machine learning and artificial intelligence. Forest models that include diversity and ecological detail capture long-term succession dynamics and diversity shifts, and can predict changes in carbon storage, but are spatially limited because they require detailed ground data not widely available. By doing so, it aims to create a new conceptual framework for modelling forest dynamics, parameterised and tested with forest data from across Europe, in an effort to dissect how to best respond to climate change. This approach will enable robust and updatable predictions of climate change impacts on forest diversity and dynamics, with flexibility to incorporate future data streams, that could inform climate change mitigation policy across the continent.