Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Assessing the Performance of a Hand-held Laser Scanning for Individual Tree Mapping in an Urban Area

Jinming Yang
ORCID logo ,
Wenwen Yuan
,
Huicui Lu
,
Yuehan Liu
,
Yongkang Wang
,
Letong Sun
,
Shimei Li
,
Haifang Li
*
Version 1 : Received: 20 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (10:46:47 CET)

A peer-reviewed article of this Preprint also exists.

Yang, J.; Yuan, W.; Lu, H.; Liu, Y.; Wang, Y.; Sun, L.; Li, S.; Li, H. Assessing the Performance of Handheld Laser Scanning for Individual Tree Mapping in an Urban Area. Forests 2024, 15, 575. Yang, J.; Yuan, W.; Lu, H.; Liu, Y.; Wang, Y.; Sun, L.; Li, S.; Li, H. Assessing the Performance of Handheld Laser Scanning for Individual Tree Mapping in an Urban Area. Forests 2024, 15, 575.

Abstract

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a hand-held laser scanning system for the extraction of tree position, diameter at breast height (DBH) and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%) compared to 2.54 cm (9.46%) and 0.23 cm (0.84%). Tree height extraction accuracy varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and height were underestimated. Linear mixed effects models (LME) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data. Other fixed factors of plot area, tree density and trajectory length didn’t have significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

Keywords

LiDAR; mobile laser scanning; personal laser scanning; forest inventory; point cloud

Subject

Biology and Life Sciences, Forestry

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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