PhD Scholar

Utilising airborne scanning laser (LiDAR) to improve the estimation of Australian forest structure & biomass.

E-mail: Alex.Lee@anu.edu.au

Improving forest measurement is required to provide a better understanding of forest stocks and dynamics, assist with sustainable forest management decisions, and meet national and international reporting needs, including those surrounding climate change. In Australia, the National Forest Inventory has initiated the Continental Forest Monitoring Framework (CFMF) as a way of meeting these requirements, through a multi-scale approach utilising a range of data, sourced from both field and remote sensing. Given the multi-scale approach of the CFMF, it is important to understand how scale potentially affects the interpretation and reporting of forest from a range of data.

Therefore this research has developed a multi-scale strategy for utilising fine scale (~1m) airborne LiDAR for remotely sensed data calibration, at two study sites (1,125ha in central Queensland, and 60,000ha in NE Victoria). The strategy is used to investigate how forest structure is defined through 3D modelling combined with empirical relationships. This allows enhanced calibration of a range of coarser scale data (e.g. Landsat, radar, and ICESat laser), which may form part of a national monitoring strategy. This research has concluded that LiDAR data can provide calibration information just as detailed and possibly more accurately than field measurements for many required forest attributes.