Applying LiDAR Individual Tree Detection to Management of Structurally Diverse Forest Landscapes

LiDAR individual tree detection (ITD) is a promising tool for measuring forests at a scale that is meaningful ecologically and useful for forest managers. However, most ITD research evaluates methods over small homogeneous areas, while many forest managers work over large, complex landscapes. We investigated how ITD results varied across diverse structural conditions in California's Sierra Nevada mixed-conifer forests and what this taught us about when and how to apply ITD. Our results suggest that it is advantageous to use ITD when it improves analysis interpretability, when measuring horizontal patterns, or when field data are unavailable. In the latter case, it is best to focus on measures dominated by large trees, like basal area and biomass. Thinking of ITD results as tree-approximate objects including one dominant tree and up to a few subordinate tree respects LiDAR's strengths and limitations; we illustrate how this concept keeps analyses consistent across varying structural conditions.