Smartphone-based Circular Plot Sampling for Forest Inventory

2026-05-18

AI Summary

This study addresses the high cost and labor intensity of traditional forest plot surveys, which hinder scalability. The authors propose a fully automated method that reconstructs circular forest plots and extracts tree parameters using only a single pass of video captured by an ordinary smartphone. This approach uniquely integrates consumer-grade hardware with SLAM, pretrained vision models, and monocular depth estimation—eliminating the need for specialized equipment. By combining trunk instance segmentation, depth maps, and camera pose estimation, along with reference-length calibration, the system operates robustly from arbitrary starting points. Evaluated in both plantation and natural forests, the method achieves diameter-at-breast-height measurement errors of 1.51 cm (MARE 3.98%) and 2.30 cm (MARE 5.69%), respectively—matching the accuracy of conventional techniques while substantially reducing cost and operational 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.

Problem

• forest inventory
• circular plot sampling
• diameter at breast height
• tree spatial location
• low-cost measurement

Innovation

• smartphone-based forest inventory
• monocular depth estimation
• tree instance segmentation
• SLAM
• circular plot sampling