Graduate Thesis Or Dissertation


Measuring and Modeling the Structure of Coniferous Trees with Point Clouds Data Public Deposited

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  • Coniferous trees are a major North American crop that has been intensively managed for its commercial value, while also serving as critical habitat for abundant wildlife and as carbon sinks. Having diverse functions, North American temperate coniferous forests have become a research hotspot for numerous scientific studies aiming to integrate ecological and economic objectives, such as examining the contribution of the conifer crown architecture to long-term forest management schemes. Point clouds have become an important source of forest inventory data and forest ecological studies, as provide accurate and comprehensive estimates of many structural variables. The present thesis aims to improve the understanding of conifer crown structure by estimating crown variables and developing stem and crown models using point clouds derived from images or laser scanning. The utilizations of point clouds were tested on loblolly pine plantations and mature Douglas-fir trees in a natural stand. Various types of 3D models were constructed for tree stems and branch attributes using point clouds. The 3D models provide direct volume estimates, as well as estimates of tree structural variables including tree height, stem diameter, branch basal diameter, length, insertion angle, and azimuth. The variable extractions were executed with semi-automatic methods, which combine human interpretation with an automatic estimation algorithm. The accuracy and reliability of point-clouds-based estimates were assessed with ground measurements and estimates from existing equations through simulations. Stem taper equations were developed using point-clouds-based stem diameter estimates. Nonlinear models of branch variables, as well as systematic crown models, were developed using lidar-based estimates by considering neighboring competition effects. The results demonstrate the reliability and efficiency of using point clouds data as alternatives or complements to traditional fieldwork. Stem and branch variables estimated nondestructively from lidar and photogrammetry point clouds agreed with ground measurements and fit in the range of observations from existing equations. Workflows developed and presented in this thesis can be employed by forestry practitioners and researchers to acquire fast and accurate tree structural variables, while models of stem and branch attributes can guide forest inventory and silvicultural practices as well as advance ecological research.
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