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Date of Award
Campus Access Dissertation
Doctor of Philosophy in Forest Science (PhD)
Administrative Home Department
College of Forest Resources and Environmental Science
Committee Member 1
Committee Member 2
Committee Member 3
Light Detection and Ranging (LiDAR) is a spatial data capture technology capable of recording millions of accurate spatial coordinates of the surroundings using light, often laser. LiDAR is often categorized by platform as airborne LiDAR and terrestrial LiDAR. The latter is often used in Architecture, Engineering and Construction (AEC) industries to generate accurate drawings and record. The same practice can be applied to forestry to obtain accurate tree geometrical data and subsequently generate accurate measurements such as diameter at breast height (DBH) and height. Many studies have been performed on this topic and were able to obtain tree parameters and subsequently predict other parameters of interests from the delineated parameters. In this study, methods of obtaining standing tree stem parameters from terrestrial LiDAR point clouds were further investigated. Chapter 1 presented a method of obtaining stem diameters using Locally Weighted Scatterplot Smoothing (LOWESS), as well as cross-sectional areas. LOWESS represented field measurements and cross-sectional profile well when compared to least-square circle fitting. Chapter 2 presented a method of stem volume estimation from terrestrial LiDAR point clouds, which also generated a 2D raster displacement map that could be easily visualized. The volume differences between this method and volume equations were consistent with the volume differences between water displacement method and volume equations. Chapter 3 developed a TLS workflow with mirrors, and investigated the accuracy of the reflected point clouds as a result of the presence of reflective surfaces after they were corrected. Depending on the project, the reflections could be used as auxiliary data.
An, Zhongming, "INDIVIDUAL TREE PARAMETERS DELINEATION FROM TERRESTRIAL LIDAR POINT CLOUDS", Campus Access Dissertation, Michigan Technological University, 2022.