Abstract

The major uncertainty identified through simulations of atmospheric carbon dioxide levels over the next century can be ascribed principally to the carbon flux associated with forests. This uncertainty must be addressed through the application of effective forest dynamic models. Predicting the quantity of carbon that is locked up in forests is most accurately achieved using data at the level of the stand, but this form of data is often limited to plots and so requires extrapolation. My research aims to create a link model enabling recently developed canopy dynamics models to be employed to infer forest stand structure, given various canopy metrics derived from LiDAR data. The resultant model will therefore provide a tool for predicting stand structure across a large forested area, from which current carbon stocks can be measured and future stocks can be predicted. This talk will provide an introduction to LiDAR, in the context of the data being implemented in this model development, and the current stage of the model will subsequently be discussed.