A Comparative Analysis of Low-Cost Devices for High-Precision Diameter at Breast Height Estimation

Abstract

Forestry is essential for environmental sustainability, biodiversity conservation, carbon sequestration, and renewable resource management. Traditional methods for forest inventory, particularly the manual measurement of diameter at breast height (DBH), are labor-intensive and prone to error. Recent advancements in proximal sensing, including lidar and photogrammetry, have paved the way for more efficient approaches, yet high costs remain a barrier to widespread adoption. This study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation. We compared these devices by assessing their agreement and efficiency when compared to conventional methods in diverse forest conditions across multiple tree species. Additionally, we analyze factors influencing measurement errors and propose a comprehensive decision-making framework to guide technology selection in forest inventory. The results show that the lowest-cost devices and photogrammetric methods achieved the highest agreement with the conventional (caliper-based) measurements, while mobile applications were the fastest and least expensive but also the least accurate. Photogrammetry provided the most accurate DBH estimates (error ≈ 0.7 cm) but required the highest effort; handheld laser scanners achieved an average accuracy of about 1.5 cm at substantially higher cost, while mobile applications were the fastest and least expensive but also the least accurate (3–3.5 cm error). The outcomes of this research aim to facilitate more accessible, reliable, and sustainable forest management practices.

Plant phenotyping relevance

低コストの画像・レーザーセンシング手法を比較検証し、樹木の胸高直径（DBH）という明示的な植物形質を推定することが研究の中心である。

Code and data availability

The supplied blocks describe DBH measurements on 120 trees with multiple devices and statistical analysis, but contain no data availability statement, no public repository deposit, and no authors' code/workflow URL. All URLs present are device vendor pages, app websites, software manuals, or cited references — none isa

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