Quantifying the Accuracy of UAS-Lidar Individual Tree Detection Methods Across Height and Diameter at Breast Height Sizes in Complex Temperate Forests

Unpiloted aerial systems (UAS) and light detection and ranging (lidar) sensors provide users with an increasingly accessible mechanism for precision forestry. As these technologies are further adopted, questions arise as to how select processing methods are influencing subsequent high-resolution modelling and analysis. This study addresses how specific individual tree detection (ITD) methods impact the successful detection of trees of varying sizes within complex forests. First, while many studies have compared ITD methods over several sites, algorithms, or sets of parameters based on a singular validation metric, this study quantifies how 10 processing methods perform across varying tree-height size quartiles and varying tree diameter at breast height (dbh) size quartiles. In total, over 1000 reference trees from 20 species within three complex temperate forest sites were analyzed at an average point density of 826.8 pts/m2. The results indicate that across four tree height size classes, the highest overall F-score (0.7344) was achieved with F-scores ranging from 0.857 for the largest and 0.633 for the smallest height size class. To further expand on this analysis, generalized linear models were used to compare the top performing and worst performing ITD method for each tree size variable and study site along a continuous gradient. This analysis suggests clear distinctions in the performance (true positive and false positive rates) based on tree sizes and ITD method. UAS-lidar users must ensure that demonstrated ITD processing methods are validated in ways that communicate their relative effectiveness for trees of all sizes. Without such consideration, the results of this study show that forest surveys and management conducted using these technologies may not accurately characterize trees present within complex forests.