Tree height and canopy volume are critical forestry parameters that are used to derive estimates of growth, carbon sequestration, standing timber volume, and biomass. Through the use of light detection and ranging, these attributes can be estimated rapidly over large areas. At the stand level, estimates of these attributes have been derived successfully from canopy height models. However, a number of challenges identified in using canopy height models remain, such as correcting for height underestimation and canopy surface irregularities, such as data pits and holes that may result from acquisition and/or post-processing, and consistent delineation of tree crowns – all of which can limit the accurate retrieval of individual tree and crown attributes. In this letter, a novel canopy model is proposed in which individual tree crowns are represented as objects for which delineations can be derived through geometric operations. The technique is based on fitting simple geometric shapes to the raw light detection and ranging point cloud and thereby compensates for this underestimation, reduces data size, and allows effective and efficient modelling at the individual tree level.

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 20 × 20 m plot and compared to measurements derived automatically. Good retrievals of stem location (R ² = 0.99, RMSE = 0.16 m) and diameter at breast height (DBH) (R ² = 0.80, RMSE = 6.0 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.