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The Canada lynx is listed as a threatened species, and as such, the identification and conservation of lynx habitats is of significant concern. Lynxes require areas with high amounts of horizontal cover made up of ground vegetation. Lidar offers a robust method of quantifying vegetation structure, and airborne lidar has been acquired across large areas of potential lynx habitat. Unfortunately, airborne lidar is often not able to directly measure understory horizontal cover due to occlusion from the upper branches. Terrestrial lidar does directly measure understory horizontal cover and can be used as training data for larger area models using airborne lidar. In this study, we acquired 168 individual terrestrial lidar scans (TLS) across 42 sites in north-central Washington state. We generated metrics from the single-scan TLS plots using depth maps, a digital cover board, and voxels. Using our TLS metrics as the training data for the airborne lidar acquired for the entire Loomis State Forest, we were able to produce a model using xgboost with 85% accuracy. We believe our study shows that single-scan TLS plots can be used effectively to quantify fine-scale forest structure elements relevant to species habitat, to then inform larger area models using airborne lidar.

Research Highlights: The present study case investigates the differences occurring when tree’s biophysical parameters are extracted through single and multiple scans. Scan sessions covered mountainous and hill regions of the Carpathian forests. Background and Objectives: We focused on analyzing stems, as a function of diameter at breast height (DBH) and the total height (H), at sample plot level for natural forests, with the purpose of assessing the potential for transitioning available methodology to field work in Romania. Materials and Methods: We performed single and multiple scans using a FARO Focus 3D X130 phase shift terrestrial laser scanner at 122 kpts and 0.3:0.15 mm noise compression ratio, resulting in an average point density of 6pts at 10m. The point cloud we obtained underpinned the DBH and heights analysis. In order to reach values similar to those measured in the field, we used both the original and the segmented point clouds, postprocessed in subsamples of different radii. Results: Pearson’s correlation coefficient above 0.8 for diameters showed high correlation with the field measurements. Diameter averages displayed differences within tolerances (0.02 m) for 10 out of 12 plots. Height analysis led to poorer results. For both acquisition methods, the values of the correlation coefficient peaked at 0.6. The initial hypothesis that trees positioned at a distance equivalent to their height can be measured more precise, was not valid; no increase in correlation strength was visible for either heights or diameters as the distance from scanner varied (r = 0.52). Conclusions: With regard to tree biophysical parameters extraction, the acquisition method has no major influence upon visible trees. We emphasize the term “visible”, as an increase in the number of acquisitions led to an increased number of detected trees (16% in old stands and 29% in young stands).