Access to highly detailed models of heterogeneous forests from the near
surface to above the tree canopy at varying scales is of increasing demand as
it enables more advanced computational tools for analysis, planning, and
ecosystem management. LiDAR sensors available through different scanning
platforms including terrestrial, mobile and aerial have become established as
one of the primary technologies for forest mapping due to their inherited
capability to collect direct, precise and rapid 3D information of a scene.
However, their scalability to large forest areas is highly dependent upon use
of effective and efficient methods of co-registration of multiple scan sources.
Surprisingly, work in forestry in GPS denied areas has mostly resorted to
methods of co-registration that use reference based targets (e.g., reflective,
marked trees), a process far from scalable in practice. In this work, we
propose an effective, targetless and fully automatic method based on an
incremental co-registration strategy matching and grouping points according to
levels of structural complexity. Empirical evidence shows the method's
effectiveness in aligning both TLS-to-TLS and TLS-to-ALS scans under a variety
of ecosystem conditions including pre/post fire treatment effects, of interest
to forest inventory surveyors