Single-Scan Stem Reconstruction Using Low-Resolution Terrestrial Laser Scanner Data

Abstract

Despite the active research, terrestrial laser scanning (TLS) has remained underutilized for forest structure assessment due to reliance of processing algorithms on high-resolution data, which may be costly and time-consuming to collect. Operational inventories, however, necessitate maximizing sample size while minimizing time and cost. The objective of this study was to assess the performance of a novel technique that enables stem reconstruction from low-resolution, single-scan TLS data in an effort to satisfy performance criteria against operational acquisition constraints. Instead of utilizing the curvature of the tree stem, e.g., by circle or cylinder fitting, we take advantage of the sensor-object geometry and reduce the dimensionality of the modeling to a series of one-dimensional (1-D) line fits. This allowed robust recovery of tree stem structure in a range of New England forest types, for tree stems which subtended at least an angular width of 15 mrad—the beam divergence of our system. Assessment was performed by projecting the three-dimensional (3-D) data onto two-dimensional (2-D) images and evaluating the per-point classification accuracies using manually digitized truth maps. Manual forest inventory measurements were also collected for each ${\bf{20}} \times {\bf{20}}\;{\bf{m}}$ plot and compared to measurements derived automatically. Good retrievals of stem location ( $R^2 = 0.99$ , ${\bf{RMSE}} = {0.16\;{\bf{m}}}$ ) and diameter at breast height (DBH) ( $R^2 = {0.80}$ , ${\bf{RMSE}} = {6.0\;{\bf{cm}}}$ ) were achieved. This study demonstrates that low-resolution sensors may be effective in providing data for operational forest inventories constrained by sample size, time, and cost.