Improving Stand Structural, Volume, and Species Information at Local and Regional Scales for an Enhanced Forest Inventory

Overarching research question: How can advanced remote sensing technologies improve stand-level attribute estimation for strategic forest management?
With the increasing availability of LiDAR and high spatial resolution optical data, forest managers have seen increasing opportunities for using these data to meet a wider range of forest inventory information needs. To date however, the number of attributes derived has been limited. Moreover, predictions linking tree, stand, and terrain indices with wood attributes are a relatively nascent field of inquiry. Key attributes derived from airborne LiDAR include height, volume, biomass, and crown closure. Advances in LiDAR technology have resulted in a new generation of LiDAR structural metrics with a focus on the vertical distribution of foliage within the canopy, which is a strong indicator of stand age, height, density, successional status, stand developmental stage, and competition (shading and crowding), all of which are expected to influence forest structure and stand growth. Likewise, the cumulative foliage area on a per-tree basis has been found to correlate with sapwood cross-sectional area.