Automatic Tree Height Measurement Based on Three-Dimensional Reconstruction Using Smartphone

Abstract

Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2-17 m, meeting the accuracy requirements of forest survey.

Plant phenotyping relevance

スマートフォン画像・深度情報と3D再構成を用いて樹高という植物構造形質を自動測定する手法を開発し、誤差評価とAndroidアプリ化まで行っているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's Data Availability Statement explicitly provides two paper-specific public assets: the source code of the TreeHeight prototype app on GitHub and the authors' annotated tree image dataset (300 annotated images augmented to 1000 pairs of color images, relative depth maps, and tree masks) on Google Drive. Both,