Efficient characterisation of forest structure is integral to regional scale biomass and carbon stock estimation, habitat management and forest condition assessment. Key descriptors or data primitives of forest structure, such as Canopy Height (CH) and Canopy Height Profile (CHP) can be used to model indirectly measurable characteristics. Existing methods that utilise Light Detection and Ranging (LiDAR) data to estimate dominant CH and CHP are utilised at a field site in S.E. Australia. Techniques to estimate CH and CHP in the field are also presented using data from three field sites representative of sclerophyll forest in S.E. Australia. The use of different point-cloud components (e.g. first returns, first-and-last returns etc.) has little effect on either derived CH or CHP parameters. On the contrary, choice of method has a significant impact on estimates of dominant height (inter-technique range >4 m). Localised and regional structural variability can also be determined from traditional field inventory. Finally, suggestions of future research directions are presented including utilising different point cloud components; fitting multi-modal distribution function to vertical profiles; landscape scale measurements of CH and CHP; and incorporation of landscape estimates in regional modelling.