Tree-by-Tree, Trait-by-Trait: a mobile LiDAR methodology for individual-level forest growth evaluation
Year: 2026
Type: article
Abstract: ABSTRACT: This study evaluates the use of Simultaneous Localization and Mapping (SLAM)-based Mobile LiDAR Scanning (MLS) for precise forest growth trait assessments, focusing on individual tree attributes in large-scale experiments. A novel tree segmentation methodology was developed to enable tree-by-tree analysis, facilitating the estimation of essential growth parameters, such as diameter at breast height (DBH), height, and volume. The research was conducted at the Estação Experimental de Ciências Florestais de Itatinga in Brazil, where approximately 4,000 trees from two Eucalyptus species, E. cloeziana F. Muell. and E. saligna Sm, were scanned. From this dataset, a subset of 21 trees, randomly selected across diameter classes, was analyzed in detail to evaluate the methodology. The methodology demonstrated accurate delineation of individual trees and highlighted the scalability of MLS for forest experimentation. While MLS height measurements were exceptionally accurate, slight discrepancies in DBH estimations, especially for E. saligna, emphasize the need for further refinements in processing and data collection. Despite these challenges, the findings confirm the potential of MLS technology to transform forestry research by offering reliable, scalable, and efficient methods for forest management and silvicultural experimentation. Future advances in data collection and processing can enhance its performance, making LiDAR an even more robust tool in forestry science.
Source: Scientia Agricola
Language: English
Subfield: Forestry
Domain: Life Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $900
Award: G105367837
OpenAlex ID: W7154709334