Assessing 3D tree model quality and species classification using imbalance indices

TL;DR

This paper evaluates the use of 3D and phylogenetic imbalance indices for analyzing forest trees, improving model quality assessment and species classification accuracy with efficient workflows and new computational tools.

Contribution

It introduces novel 3D and phylogenetic imbalance indices for forest tree analysis, enhancing quality control and species classification performance.

Findings

Imbalance indices can be applied to 3D tree models for quality assessment.

Species classification accuracy reached up to 81.8%.

Adding imbalance indices improves classification, especially for minority species.

Abstract

We investigate the use of additional 3D and phylogenetic non-3D tree balance indices for analyzing and monitoring forests using an exemplary "virtual forest" dataset from the Wytham Woods, Oxford, UK. This study assesses 3D model quality, species classification performance, and the relevance of these indices. Our study shows that indices stemming from the study of ancestry trees of species can be successfully applied to 3D models of organic trees and, accompanied with recently introduced 3D imbalance indices, offer a complementary perspective on 3D tree models and improve the detection of deviations. Their computational efficiency combined with the simple and reproducible workflow presented in this manuscript form a computationally feasible quality control step in the 3D model construction. Species classification models reached an estimated accuracy of up to 81.8% and allowed to make confident species predictions for a large portion of the unlabeled trees in the dataset. While conventional tree metrics can already provide strong predictive performance, the addition of filtered 3D and non-3D statistics improved results consistently, particularly for minority species classes. Alongside this manuscript, we provide updated functionality in the R package treeDbalance to include the necessary functionalities and release the derived index datasets and species predictions.