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LiDAR Voxel-Size Optimization for Canopy Gap Estimation

Formally Refereed
Authors: C. Wade Ross, E. Louise Loudermilk, Nicholas Skowronski, Scott Pokswinski, J. Kevin Hiers, Joseph O’Brien
Year: 2022
Type: Scientific Journal
Station: Southern Research Station
Source: Remote Sensing


Terrestrial laser scanning of forest structure is used increasingly in place of traditional technologies; however, deriving physical parameters from point clouds remains challenging because LiDAR returns do not have defined areas or volumes. While voxelization methods overcome this challenge, estimation of canopy gaps and other structural attributes are often performed by reducing the point cloud to two-dimensions, thus decreasing the fidelity of the data. Furthermore, relatively few studies have evaluated voxel-size effects on estimation accuracy. Here, we show that voxelized laser-scanning data can be used for canopy-gap estimation without performing dimensionality reduction to the point cloud. Both airborne and terrestrial LiDAR were used to estimate canopy gaps along six vertical transects and four height intervals. Voxel-based estimates were evaluated against hemispherical photography and a sensitivity analysis was performed to identify an optimal voxel size. While the results indicate that our approach can be used with both airborne and terrestrial LiDAR, voxel size has a considerable influence on canopy-gap estimation. Results from our sensitivity analysis indicate that TLS estimation performs best when using 10 cm voxels, yielding canopy gaps ranging from 32-78%. The optimal voxel size for ALS estimation was obtained with 25 cm voxels, yielding estimates ranging from 25-68%.


airborne laser scanning, terrestrial laser scanning, gap fraction, vegetation structure, vegetation occlusion, hemispherical photography, point cloud data, scaling, longleaf pine


Ross, C. Wade; Loudermilk, E. Louise; Skowronski, Nicholas; Pokswinski, Scott; Hiers, J. Kevin; O Brien, Joseph. 2022. LiDAR Voxel-Size Optimization for Canopy Gap Estimation. Remote Sensing. 14(5): 1054-.