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.

Storing accurate models of complex geometries in a compact way has become an increasingly challenging issue, especially when dealing with large datasets. One of such datasets is Cobra-Groeninzicht's database of all trees in the Netherlands. In the gaming industry, a new technique is being used to generate tree models: the L-system. An L-system stores a string representation of the structural model of a tree, with the added possibility for recursive modelling using growing rules. This format proves a promising alternative to more traditional methods of storing complex geometries. However, it remains unclear whether it can be an accurate enough representation for modelling and analysing real-life trees.

In this research project, the AdTree algorithm is used to reconstruct a skeleton from a point cloud of a single tree. This skeleton is then transformed to an L-System string format, as well as a CityJSON format (both in JSON structure). The L-system format comes with the advantage that it allows for several methods of increasing its compactness further (growing, generalisation). The overall size of these files also indicates fewer storage space is needed to store the tree geometry. The quality of the L-System skeleton is nearly equal to the input, the skeleton generated by. Assuming it can be read and drawn using a Turtle program, the L-system thus allows for storing the same geometric information more compactly than traditional storage formats, with sufficient accuracy, and the added possibilities of growing or generalising the model.