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

TL;DR

This paper presents a low-cost, camera-based method for estimating tree diameters at breast height using photogrammetry and segmentation, achieving accuracy close to expensive LiDAR systems.

Contribution

A novel pipeline combining consumer-grade 360 video, SfM, semantic segmentation, and RANSAC for accurate tree DBH estimation at a fraction of the cost.

Findings

Median absolute relative error of 5-9% compared to manual measurements.

Method is only 2-4% less accurate than LiDAR-based estimates.

Uses widely available consumer-grade equipment and minimal setup.

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.