A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning

2026-07-24

AI Summary

This study addresses the challenge of accurately reconstructing long-term diameter-at-breast-height (DBH) and volume growth trajectories for individual trees in boreal forests, hindered by scarce historical stem data, difficulties in cross-temporal point cloud correspondence, and measurement errors. Leveraging 136 point clouds acquired from 11 laser scanning platforms between 2014 and 2025, we propose a robust framework that eliminates the need for repeated understory scanning. Our approach employs deep learning for cross-temporal tree segmentation and matching, integrating mobile and terrestrial laser scanning (MLS/TLS) stem curves with airborne laser scanning (ALS)-derived height measurements to develop a height-growth–based scaling model for reconstructing individual-tree DBH and volume time series. By synergistically combining multi-platform LiDAR data with a height-growth model—demonstrated here for the first time—our method substantially improves tree-level monitoring accuracy, achieving lower RMSE than direct differencing over 5–10 year intervals and maintaining maximum RMSE within 8–12% for DBH and 12–23% for volume over a 12-year period.

Abstract

Accurate tree-level forest monitoring using laser scanning data requires reliable tree delineation, consistent tree correspondence across multitemporal point clouds, and accurate estimation of tree attributes and their change. Reconstructing tree growth in boreal forests is challenging due to the scarcity of historical stem-level data, propagation of errors from older sensors into change estimation, and growth rates with a magnitude of measurement uncertainty. This study investigates a framework for estimating individual tree diameter at breast height (DBH) and stem volume growth using 136 point clouds acquired between 2014--2025 with 11 scanners on airborne (ALS), mobile (MLS), and terrestrial laser scanning (TLS) platforms across boreal forest test sites. Trees were delineated from an MLS point cloud using deep learning-based segmentation which was transferred to the remaining point clouds, resulting in reliable multitemporal tree correspondence. Stem curves were derived from MLS/TLS data, with ALS data used for height estimation, enabling DBH and volume estimation and time series. A height growth-based scaling model was used to reconstruct stem attributes across time and estimate growth. Results showed that modeled growth achieved higher agreement with manual growth estimates than differencing independently estimated attributes from point clouds. The modeled-manual 5- and 10-year growth RMSEs were 55--111\% and 26--67\% for DBH, and 31--87\% and 21--67\% for volume, respectively, depending on plot difficulty. The scaling model was temporally robust, with errors remaining stable or stabilizing after 5--6 years, reaching maximum RMSEs of 8--12\% for DBH and 12--23\% for volume after 12 years. Combining MLS/TLS-derived stem measurements with multitemporal ALS-derived heights provided a robust framework for individual tree growth estimation without requiring multiple under-canopy scans.

Problem

• individual tree growth
• boreal forests
• laser scanning
• DBH estimation
• stem volume

Innovation

• multi-platform laser scanning
• individual tree growth reconstruction
• deep learning-based segmentation
• height growth scaling model
• boreal forest monitoring