BayesForest

BayesForest generates realistic morphological tree clones from laser scanning data to model and compare tree morphology using a multipurpose procedural stochastic growth model and a structural distance metric.

Key Features:

• Detailed Reconstruction: BayesForest utilizes laser scanning data for precise reconstruction of tree morphology.
• Statistical Measure of Similarity: The algorithm incorporates a structural distance metric to compare pairs of trees and quantify morphological similarity.
• Stochastic Growth Model: BayesForest employs a multipurpose procedural stochastic growth model that can be adjusted to replicate specific morphological characteristics of measured trees.
• Programmable Interface: The implementation includes a programmable interface for manipulation of the data inputs required by the algorithm.

Scientific Applications:

• Ecology and Forestry: Analysis of how variations in tree morphology influence landscape formation, habitat creation, and eco-physiological characteristics.
• Plant Science Research: Exploration of the morphological potential of growth models and replication of experimentally measured tree forms for theoretical and experimental studies.

Methodology:

Reconstruction of tree forms from laser scanning data; quantification of structural similarities using the structural distance metric; generation of tree clones via a multipurpose procedural stochastic growth model adjustable to observed morphologies.