Smartphone-based Circular Plot Sampling for Forest Inventory
Su Sun, Jui-Cheng Chiu, Nabin Khanal, Songlin Fei, Yingjie Victor Chen

TL;DR
This paper introduces a smartphone-based method for forest plot sampling that uses video, deep learning, and SLAM to measure trees accurately without expensive equipment.
Contribution
A novel lightweight pipeline combining monocular depth estimation, segmentation, and SLAM for efficient, accurate forest inventory from a single smartphone video.
Findings
Achieved mean absolute errors of 1.51 cm and 2.30 cm in different forest plots.
Demonstrated stable and reproducible tree localization across multiple videos.
Performed comparably to traditional methods with lower cost and complexity.
Abstract
Circular sample plots are a cornerstone of forest inventory, yet accurate measurement of tree diameter at breast height (DBH) and spatial location within such plots remains challenging. Conventional approaches rely either on costly terrestrial LiDAR systems or labor-intensive manual methods involving calipers and compass bearings, limiting their scalability and accessibility in large scale environments. We present a lightweight, smartphone-based pipeline that enables complete plot sampling based tree measurement from a single walkthrough video, requiring no specialized hardware beyond a consumer smartphone mounted on a portable stand. The proposed method integrates pretrained monocular depth estimation and tree instance segmentation with a simultaneous localization and mapping (SLAM) framework to jointly refine camera trajectories and depth across the video sequence. Tree positions and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
