  Access to highly detailed models of heterogeneous forests, spanning from the
near surface to above the tree canopy at varying scales, is increasingly in
demand. This enables advanced computational tools for analysis, planning, and
ecosystem management. LiDAR sensors, available through terrestrial (TLS) and
aerial (ALS) scanning platforms, have become established as primary
technologies for forest monitoring due to their capability to rapidly collect
precise 3D structural information directly. Forestry now recognizes the
benefits of a multi-scale approach, leveraging the strengths of each platform
while minimizing individual source uncertainties. However, effective
integration of these LiDAR sources relies heavily on efficient multi-scale,
multi-view co-registration or point-cloud alignment methods. Here, we propose
an effective, target-less, and fully automatic co-registration method for
aligning forest point clouds collected from multi-view, multi-scale LiDAR
sources. Our co-registration approach employs an incremental alignment
strategy, grouping and aggregating 3D points based on increasing levels of
structural complexity. This strategy aligns 3D points from less complex (e.g.,
ground surface) to more complex structures (e.g., tree trunks/branches,
foliage) sequentially, refining alignment iteratively. Empirical evidence
demonstrates the method's effectiveness in aligning TLS-to-TLS and TLS-to-ALS
scans across various ecosystem conditions. In TLS-to-TLS scenarios, parameter
RMSE errors were less than 0.1 degrees in rotation and less than 2 cm in
translation. For TLS-to-ALS, corresponding errors were less than 0.7 degrees
and 6 cm, respectively. To the best of our knowledge, our method is the first
capable of automatically co-registering forest point clouds from both
TLS-to-TLS and TLS-to-ALS scenarios without relying on targets, while achieving
high performance.
