Bushfire is a conspicuous force in Australian forested landscapes. It shapes terrestrial ecosystems and represents a major threat to human life and assets. Predicting and understanding fire in Australian forests is important to managing fire for environmental conservation and the minimisation of risk to human life and assets. In this context, there is a need for detailed characterisation of fire fuel over large areas – a task that is often beyond field-based campaigns. Light Detection and Ranging (LiDAR) has demonstrated potential in deriving structural characteristics of forests, and there is currently a wealth of airborne LiDAR data covering south eastern Australia. However, the utility of this data in characterising important attributes of forest fuel remains relatively untested, particularly regarding more difficult to measure though highly relevant fuel components such as understorey, surface and bark fuel. Through statistical modelling methods, this project aims to investigate the accuracy to which airborne LiDAR, in conjunction with other spatial datasets, can be used to predict fuel attributes that are important to fire behaviour in Australian forests. It is hoped that such research can assist management agencies and researchers in better predicting fire behaviour, informing fuel reduction treatment and firefighting operations, and provide insights into fuel dynamics in forested environments.