Smartphone-based Circular Plot Sampling for Forest Inventory

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 DBH estimates are recovered by fusing SLAM-derived camera poses with segmented depth maps, with absolute real-world scale anchored via a calibrated reference length. The system was evaluated in both managed forest plots and natural forest plot, achieving a mean absolute error of 1.51 cm (MARE 3.98%) and 2.30 cm (MARE 5.69%) respectively, with consistent performance across varying starting directions and positions. Cross-video consistency analysis further demonstrated stable and reproducible tree localization across measurements initiated from different starting positions. The proposed approach achieves accuracy comparable to established field methods while substantially reducing equipment cost and operational complexity, making it accessible to both professional researchers and non-expert forest managers in diverse operational settings.