TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement

TL;DR

TreeDGS introduces a novel aerial imaging method using 3D Gaussian splatting to accurately measure tree diameters at breast height, outperforming LiDAR in cost and precision in complex natural scenes.

Contribution

The paper presents TreeDGS, a new aerial reconstruction technique leveraging 3D Gaussian splatting for precise DBH measurement in natural forests.

Findings

Achieves 4.79 cm RMSE in DBH measurement

Outperforms LiDAR baseline with 7.66 cm RMSE

Enables accurate, low-cost aerial forest surveys

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 .