Tree architecture is the 3D arrangement of the aboveground parts of a tree and represents optimized adaptations to the environment linked to growth and physiology. At present, terrestrial laser scanning (TLS) has demonstrated its potential to characterize woody tree structure. However, while several TLS approaches exist to extract tree architecture parameters, most methods focus on calculating absolute tree volume and do not describe detailed tree architecture. As a result, little is known regarding methods for applying TLS to quantify tree architecture and the limitations of using TLS to directly estimate tree architecture traits. Here, we examined dominant species of trees from a Guyanese tropical rainforest to evaluate the utility of TLS for tree architecture measurements. In the field, we used TLS to scan nine trees and extracted individual tree point clouds. The woody structure was reconstructed from the point clouds using TreeQSM, a freely available software package that represents trees as 3D quantitative structure models (QSMs). From there QSMs, we calculated: 1) the length and diameter of all branches > 10 cm diameter, 2) branching order and 3) absolute tree volume. To validate our method, a total of 279 branches were manually measured in the field.
Results/Conclusions
TreeQSM found and reconstructed 95% of the branches thicker than 30 cm. Comparing empirical and QSM data, TLS and QSM reconstruction overestimated the length of branches thicker than 50 cm by 1% and underestimated the diameter of branches between 20 cm and 60 cm by 8%. TreeQSM assigned the correct branching order in 99% of all cases and reconstructed 87% of branch lengths and 97% of tree volume. Although these results are based on nine trees, they validate a method that is an important step forward towards using tree architecture traits based on TLS and TreeQSM. This approach, even with its limitations, opens up new possibilities to use QSM models to determine tree architecture. We believe that future research could use this study as a starting point to better understand ecological and metabolic processes in forests.