TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement

Abstract

Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM--MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS's depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement .

Plant phenotyping relevance

樹木のDBHという明示的な植物形質を、航空画像と3D Gaussian splattingから推定する新規手法を開発し、実測値およびLiDARと比較検証しているため、方法中心の植物フェノタイピング研究として含める。

Code and data availability

The paper's UAV RGB imagery, field DBH measurements, and TreeDGS analysis code are not publicly deposited; the Data Availability statement directs inquiries to the corresponding author. The only URLs present (OpenMVS, Hesai XT-32, Inertial Labs RESEPI datasheet) are generic third-party tools/specifications, not paper-­

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