Print Email Facebook Twitter Procedural Modelling of Tree Growth Using Multi-temporal Point Clouds Title Procedural Modelling of Tree Growth Using Multi-temporal Point Clouds Author van der Horst, Noortje (TU Delft Architecture and the Built Environment) Contributor Nan, L. (mentor) Stoter, J.E. (mentor) Degree granting institution Delft University of Technology Programme Geomatics Date 2022-06-23 Abstract A digital reconstruction of real-life trees could provide many benefits in fields such as botany, forestry management, biology, and urban planning. Plant growth modelling in particular would enable the analysis of plant structure and behaviour in a customizable, widely applicable and non-destructive manner. Although many data-driven plant reconstruction methods exist, it remains a complex problem due to the intricate branching systems of trees and the need for balancing model soundness with adherence to the often incomplete input data. Modelling plant growth currently proves difficult as well due to the large number of factors involved in the growth process and the level of prior botanical knowledge and/or detailed field data that is often required.This work uses an automatic MST (Minimum Spanning Tree)-based reconstruction method to obtain tree skeleton models from LiDAR input data. Multiple scans of the same tree, gathered at different years, are related to each other to improve and expand upon the reconstruction. A procedural model is used to simulate the growth in the tree tips using a lobe-based approach and a region-growing algorithm. The growth-based models provide a temporally informed reconstruction that is visually, geometrically and topologically sound. Establishing correspondences in the main structure between timestamps can assist the reconstruction of the tree at a time for which the input data was noisier or incomplete, as well as provide an estimate of the tree's structure in between known data points. This type of reconstruction can be used to both model and study the growth behaviour of trees, for multi-temporal visualisations, and to provide more informed tree models for reconstruction purposes. Subject TreesLiDARReconstructionModellingModelingVegetationPoint CloudTree Growth To reference this document use: http://resolver.tudelft.nl/uuid:d284c33a-7297-4509-81e1-e183ed6cca3c Part of collection Student theses Document type master thesis Rights © 2022 Noortje van der Horst Files PDF Thesis_vanderHorst_4697952.pdf 13.26 MB PDF P5_presentation_vanderHor ... 697952.pdf 3.28 MB Close viewer /islandora/object/uuid:d284c33a-7297-4509-81e1-e183ed6cca3c/datastream/OBJ1/view