Estimating the Diameter at Breast Height of Trees in a Forest from RGB

Abstract

Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon accounting. While LiDAR-based techniques can achieve centimeter-level precision, they are cost-prohibitive and operationally complex. We present a low-cost alternative that only needs a consumer-grade 360 video camera. Our semi-automated pipeline comprises of (i) a dense point cloud reconstruction using Structure from Motion (SfM) photogrammetry software called Agisoft Metashape, (ii) semantic trunk segmentation by projecting Grounded Segment Anything (SAM) masks onto the 3D cloud, and (iii) a robust RANSAC-based technique to estimate cross section shape and DBH. We introduce an interactive visualization tool for inspecting segmented trees and their estimated DBH. On 61 acquisitions of 43 trees under a variety of conditions, our method attains median absolute relative errors of 5-9% with respect to "ground-truth" manual measurements. This is only 2-4% higher than LiDAR-based estimates, while employing a single 360 camera that costs orders of magnitude less, requires minimal setup, and is widely available.

Plant phenotyping relevance

RGB映像から樹木のDBHという明示的な植物形態形質を推定する半自動パイプラインを開発し、手動測定およびLiDARと比較して精度検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper describes a 360° video DBH estimation pipeline, but no public dataset, code repository, or model release is stated in the supplied blocks. The only referenced URL (viser) is a generic third-party visualization library, not a paper-specific asset.

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