Canopy structure plays an essential role in biophysical activities in
forest environments. However, quantitative descriptions of a 3-D canopy
structure are extremely difficult because of the complexity and
heterogeneity of forest systems. Airborne laser scanning (ALS) provides
an opportunity to automatically measure a 3-D canopy structure in large
areas. Compared with other point cloud technologies such as the
image-based Structure from Motion, the power of ALS lies in its ability
to penetrate canopies and depict subordinate trees. However, such
capabilities have been poorly explored so far. In this paper, the
potential of ALS-based approaches in depicting a 3-D canopy structure is
explored in detail through an international benchmarking of five
recently developed ALS-based individual tree detection (ITD) methods.
For the first time, the results of the ITD methods are evaluated for
each of four crown classes, i.e., dominant, codominant, intermediate,
and suppressed trees, which provides insight toward understanding the
current status of depicting a 3-D canopy structure using ITD methods,
particularly with respect to their performances, potential, and
challenges. This benchmarking study revealed that the canopy structure
plays a considerable role in the detection accuracy of ITD methods, and
its influence is even greater than that of the tree species as well as
the species composition in a stand. The study also reveals the
importance of utilizing the point cloud data for the detection of
intermediate and suppressed trees. Different from what has been reported
in previous studies, point density was found to be a highly influential
factor in the performance of the methods that use point cloud data.
Greater efforts should be invested in the point-based or hybrid ITD
approaches to model the 3-D canopy structure and to further explore the
potential of high-density and multiwavelengths ALS data.