Estimating the Diameter at Breast Height of Trees in a Forest With a Single 360 Camera

2025-05-06

AI Summary

To address the high cost and low efficiency of diameter-at-breast-height (DBH) measurement in forest inventory, this paper proposes a low-cost, end-to-end automated estimation method using a single consumer-grade 360° camera. The method innovatively integrates Structure-from-Motion (SfM) 3D reconstruction, projection-based Grounded SAM for semantic segmentation, and RANSAC-robust circle fitting to precisely localize tree stem cross-sections and invert DBH directly from spherical imagery. An interactive visualization tool is developed to support manual verification. Evaluated on 43 trees and 61 field-measured samples, the approach achieves a median absolute relative error of 5–9%, with accuracy only 2–4% lower than LiDAR-based methods, while reducing hardware cost by two to three orders of magnitude. This work establishes a scalable, easily deployable paradigm for precise forest monitoring in resource-constrained settings.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Estimating tree diameter (DBH) using affordable 360 camera
• Replacing costly LiDAR with photogrammetry for forest inventories
• Achieving 5-9% error in DBH via semi-automated 3D reconstruction

Innovation

• Uses consumer-grade 360 camera for cost-effective DBH estimation
• Employs SfM photogrammetry and SAM for 3D trunk segmentation
• Applies RANSAC-based technique for robust cross-section shape analysis