Bayes Forest: a data-intensive generator of morphological tree clones

Area of Science:

• Ecology and Forestry
• Computational Biology
• Plant Science

Background:

• Tree morphology significantly influences landscapes, species habitats, and biosphere eco-physiology.
• Accurate modeling of tree form is crucial for ecological and forestry research.
• Existing methods may lack the flexibility to generate diverse yet structurally similar tree forms.

Purpose of the Study:

• To develop an adaptable algorithm for generating morphological tree clones.
• To create realistic tree forms that are similar in overall structure but vary in fine detail.
• To provide a versatile pipeline for exploring plant growth models.

Main Methods:

• Utilized laser scanning data for detailed 3D tree reconstruction.
• Developed a statistical measure of similarity (structural distance) for comparing tree morphologies.
• Employed a procedural stochastic plant growth model with adaptable rules.

Main Results:

• Successfully generated morphological tree clones replicated from experimentally measured target trees.
• Demonstrated the algorithm's ability to produce variations in fine-scale structural detail while maintaining tree-level similarity.
• Created a programmable interface for data manipulation within the algorithm.

Conclusions:

• The developed algorithm offers a general and adaptable pipeline for generating realistic tree forms.
• The method facilitates the exploration of morphological potential in plant growth models for diverse research applications.
• This approach enhances the study of tree structure and its ecological implications.