Improving Individual Tree Structural Measurements At The Plot Level And Scaling For Stand Level Characterization
There is consensus in the LiDAR remote sensing community that future advances in these technologies will come as a result of the merging of airborne and ground-based data with other datasets to exploit their inherent strengths and reduce any potential weaknesses. The combining of an airborne and ground based perspective with other remote sensing and geospatial data offers significant promise as individual tree-level measurements, which are correlated with wood properties, can be predicted using ground-based and then potentially extrapolated over larger areas using airborne and satellite data collection.
Theme 3 Questions:
What individual tree attributes can be accurately measured from ground-based LiDAR and how can this data be used to scale-up and validate stand level assessments?
Ground-based LiDAR individual tree attribute extraction and validation
The extremely detailed 3-D point cloud produced by ground-based LiDAR provides opportunities to model stem volume and taper (Maas et al. 2008), branching structures (Bucksch et al., 2010), and when combined with tree modelling techniques such as L-systems (Prusinkiewicz and Lindemayer, 1990), the complete reconstruction of individual trees at very high levels of detail (Côté et al., 2009; 2011; 2012). A number of approaches have been developed, with Canada leading much of this research within the CFS and universities. To date, however, developments have been made within specific forest types, often at a small number of sites for select species. This CRD provides an opportunity to further develop these methods, but at the same time to validate these methods by applying them to different species and site conditions across Canada. Methods will be compared and contrasted and validated using field data collected at each of the sites.
Prediction of individual tree growth using ground-based LiDAR approaches
An important advancement is yet to be made through the application of LiDAR remote sensing derived stand and tree-level attributes to parameterize wood quality and tree growth models. Research is needed to link these detailed individual tree measurements to growth models used by forest managers across Canada to ensure the measured attributes can be utilized by these approaches.