Combining single-date mobile and multitemporal airborne laser scanning for retrospective estimation of individual tree growth over a 10-year period in boreal forests

Accurate estimation of individual tree growth is essential for forest inventories and carbon stock assessments, yet traditional manual methods remain labor-intensive and poorly scalable. Laser scanning offers promising alternatives, but slow tree growth rates, sensor limitations, and limited temporal availability of accurate stem-level data challenge growth estimation. This study presents a novel framework combining a single-date mobile laser scanning (MLS) dataset from 2024 with airborne laser scanning (ALS) datasets from 2014 and 2023 to estimate 10-year growth (2014–2024) in diameter at breast height (DBH) and stem volume at the individual tree level. MLS was used for detecting trees and modeling their stem curves, enabling DBH estimation in 2024. These stem curves, alongside ALS-derived heights, were used for volume estimation in 2024. A linear scaling approach, based on ALS-derived height growth factors, was used to model past DBH and stem curves to obtain 2014 attributes, eliminating the need for historical MLS data. Across eight boreal forest plots, growth and one-time attribute estimation accuracy were evaluated against manual DBH measurements and ALS-based reference heights, with analyses across forest complexities, tree species, and size classes. Volume change estimation achieved R 2 values of 0.6–0.8 compared to 0.3–0.4 for DBH change estimation. Root mean square errors (RMSEs) were 0.9–1.7 cm (30%–64%) for DBH change and 0.04–0.10 m 3 (25%–65%) for volume change. Growth estimation was most accurate for pines, medium-sized trees (DBH 20–35 cm), and in sparse stands. Although accuracy varied by environment, the proposed method offers a scalable approach for retrospective growth estimation, with potential to enhance the efficiency and cost-effectiveness of forest monitoring. • Mobile and airborne laser scanning were combined to estimate individual tree growth. • 10-year growth in DBH and stem volume were estimated across eight test sites. • Past tree attributes were modeled without historical mobile laser scanning data. • Growth estimates most accurate for pines, medium-sized trees, and in sparse forests. • Scalable method can improve reference data collection of individual tree growth.