Abstract
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%.
Highlights
Canopy structure is an important ecosystem trait that governs the spatial and temporal distribution of light transmittance, impacting the composition, distribution, and productivity of the mid- and understory plant community
Structural estimates derived from hemispherical photography are often not comparable within and among studies due to differing lighting conditions, non-standardized exposure settings, and non-standardized binarization or “thresholding” methods applied during image processing [16]
4 illustrates the probability distribution of LiDAR returns as a function of height for each transect andVoxelization each platform calculated from the voxelized point-cloud data
Summary
Canopy structure is an important ecosystem trait that governs the spatial and temporal distribution of light transmittance, impacting the composition, distribution, and productivity of the mid- and understory plant community. Canopy structure is typically measured indirectly using both passive and active remote sensing because direct sampling methods can be laborious, time consuming, and destructive. Passive remote-sensing data are commonly acquired using hemispherical photography (DHP) or light interception instrumentation [5,13,14]; these methods are sensitive to environmental lighting conditions, exposure settings, image processing, and gamma correction [15]. Structural estimates derived from hemispherical photography are often not comparable within and among studies due to differing lighting conditions, non-standardized exposure settings, and non-standardized binarization or “thresholding” methods applied during image processing [16]. Beckschäfer and colleagues [17] reported that gap fraction values
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
