Automated characterization of forest canopy vertical layering for predicting forest inventory attributes by layer using airborne LiDAR data

Forest canopy vertical layering influences stand development and yield and is critical information for forest management planning and wood supply analysis. It is also relevant for other applications including habitat modelling, forest fuels management and assessing forest resilience. Forest inventories that use coincident airborne Light Detection and Ranging (LiDAR) data and field plots (i.e. area-based approach) to predict forest attributes generally do not consider the multi-layer canopy structure that may be found in many natural and managed forest stands. With airborne LiDAR, it is possible to separate single-layer and multi-layer stands. This information can be used to allocate predictions of forest attributes such as timber volume (m(3) ha(-1)), by canopy layer. In this study, we used single-photon LiDAR data to automate the mapping of vertical stand layering in a temperate mixedwood forest with a variety of forest types and vertical complexities. We first predicted whether each 25 x 25 m grid cell had one or two canopy layers, and then partitioned inventory attributes (e.g. basal area (BA), gross total stem volume (GTV)) by canopy layer. We compared two methods for estimating attributes by layer at the stand level using nine independent validation stands. Overall agreement between the reference and predicted structure for the calibration plots was 74% (n = 266). At the grid-cell level, attributes were generally underestimated for the upper layer and overestimated for the lower layer. For the validation stands, the relative height of the lower layer was under-predicted compared to the reference data (46-52% versus 57%), while the proportion of BA and GTV in the lower layer were very similar to the reference values (17-19% versus 18% for BA and 12-15% versus 12% for GTV). Overall, the approach showed promise in distinguishing single- and two-layered stand conditions and partitioning estimates of inventory attributes such as BA and GTV by layer-both for grid cells and at the stand level. The inclusion of forest information by canopy layer enhances the utility of LiDAR-derived forest inventories for forest management in forest areas with complex, multi-layer stand conditions.