Discovering Tree Architecture: A Comparison of the Performance of 3D Digitizing and Close-Range Photogrammetry

Abstract

Accurate measurement of tree architecture is vital for understanding forest dynamics and supporting effective forest management. This study evaluates close-range photogrammetry (CRP) using TreeQSM (v2.4.1) software, reconstructing 3D tree structures in both deciduous and coniferous species and comparing its performance to the Fastrak 3D digitizing method. CRP proved less labor-intensive and effective for estimating parameters like tree height, stem diameter, and volume of thicker branches in small trees. However, it struggled with capturing intricate structures, overestimating volumetric values and underestimating branch lengths and counts. Mean relative root mean square errors for height, diameter at 0.3 m height, volume, and branch count were 34.19%, 69.9%, 107.87%, and 142.03%, respectively. These discrepancies stem from challenges in reconstructing moving objects and filtering non-woody elements. While CRP shows potential as a complementary method, further advancements are necessary to improve 3D tree model reconstruction, emphasizing the need for ongoing research in this domain.

Plant phenotyping relevance

樹木の3D構造・形態形質を取得するCRPと3Dデジタイジング手法を比較・検証しており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The supplied blocks describe Fastrak digitization and photogrammetric data collection and analysis (Agisoft Metashape, CloudCompare, TreeQSM, R/Python), but contain no data availability statement, no public repository deposit, no author URL for datasets, images, point clouds, or analysis code. All referenced software (

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