Poster Session I
Project Type
Poster
Project Funding and Affiliations
CAL FIRE Forest Health Research Program
Faculty Mentor’s Full Name
C. Alina Cansler
Faculty Mentor’s Department
W.A. Franke College of Forestry and Conservation
Abstract / Artist's Statement
Bark is one of the primary tree traits associated with fire resistance. Current research has focused on bark thickness as the dominant structural trait contributing to resistance, yet bark rugosity (e.g. roughness) has also been shown to influence the transfer of heat from fire into the living tissues of trees. Bark rugosity is a challenging trait to quantify, often requiring destructive measuring techniques. We applied the equation developed by Shearman and Varner (2021), which defines rugosity as the ratio of the actual cross-section compared to the area of a convex hull; higher values indicate higher rugosity. We compared contemporary, destructive sampling techniques to emerging iPad-based LiDAR. This comparison was conducted on over 200 samples from varying tree species spanning 2.5–24.9 cm in diameter. Samples were first measured via an automated processing workflow in ImageJ, flowing contemporary measurement techniques. These samples were then scanned using an iPad Pro equipped with the LiDAR application Scaniverse, to generate point clouds, which were cleaned in Cloud Compare and analyzed using an automated workflow in R (lidR package). We compared the precision of both workflows, and preliminary results suggest the LiDAR based workflow is a promising non-invasive method for quantifying bark rugosity.
Category
Physical Sciences
Can iPad LiDAR Measure Bark Rugosity?
UC South Ballroom
Bark is one of the primary tree traits associated with fire resistance. Current research has focused on bark thickness as the dominant structural trait contributing to resistance, yet bark rugosity (e.g. roughness) has also been shown to influence the transfer of heat from fire into the living tissues of trees. Bark rugosity is a challenging trait to quantify, often requiring destructive measuring techniques. We applied the equation developed by Shearman and Varner (2021), which defines rugosity as the ratio of the actual cross-section compared to the area of a convex hull; higher values indicate higher rugosity. We compared contemporary, destructive sampling techniques to emerging iPad-based LiDAR. This comparison was conducted on over 200 samples from varying tree species spanning 2.5–24.9 cm in diameter. Samples were first measured via an automated processing workflow in ImageJ, flowing contemporary measurement techniques. These samples were then scanned using an iPad Pro equipped with the LiDAR application Scaniverse, to generate point clouds, which were cleaned in Cloud Compare and analyzed using an automated workflow in R (lidR package). We compared the precision of both workflows, and preliminary results suggest the LiDAR based workflow is a promising non-invasive method for quantifying bark rugosity.