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Terrestrial laser scanning data and code for EucFACE: individual trees and quantitative structure models (QSMs)

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Zenodo2025-07-17 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.16019824
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This repository contains the data and code used for the analysis of the following publication: New allometric models for Eucalyptus tereticornis using terrestrial laser scanning show increased carbon storage in larger trees. Terryn, L., Ellsworth, D., Medlyn, B. E., Boer, M., Verhelst, T. E., Calders, K. Agricultural and Forest Meteorology (accepted). Any use of this dataset should cite the paper above (Creative Commons Attribution 4.0 International Public License). Contact: louise.terryn@ugent.be ================================================                    Dataset================================================ General: TLS data were collected at the EucFACE experiment during the 5th and 6th of May 2022. We used a RIEGL VZ-400i terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH). The instrument has a beam divergence of 0.35 mrad and operates in the infrared (wavelength 1550 nm) with a range up to 350 m. The pulse repetition rate for each scan was 600 kHz and the angular sampling resolution was 0.04°. The azimuth angle range was 0-360° and the zenith angle range was 30-130°. Therefore an additional scan was acquired at each scan location with the scanner tilted at 90° from the vertical to complete sampling of the full hemisphere at each location. A sub-area of approximately 0.3 ha between two of the experimental plots was scanned in an irregular pattern to minimise the occlusion in the point cloud data. Each ring also has scaffolding towers, and the TLS was operated from six different platform heights at each ring to minimise occlusion in the upper canopy. All trees within the sub-area of 0.3 ha were segmented. When a multi-stem tree splits into single stems below 1.3 m, each stem was considered to be an individual tree in the analysis. An additional 11 trees were segmented from outside this area to cover the full size range of trees for our model. This resulted in 188 trees across the whole size range which was reduced to 116 trees excluding dead or considerably damaged trees. Full details of the methods to segment individual trees and generate the QSMs can be found in the publication mentioned above. Datasets:The data can be found under 01_data and consists of several folders: A folder 01_tree-point-clouds with the individual tree point clouds in PLY format separated in a folder for living and dead trees. Tree IDs have a letter and a number. The letter refers to it's status alive (A) or dead/damaged (D - based on inspection of the point cloud). The numbers above 900 are 11 additional trees segmented from outside this area to cover the full size range of trees for our model. The living trees were used for further analysis and were leaf-wood separated.  The results of leaf-wood separation can be found under 02_leaf-wood-point-clouds. The wood point clouds were futher used to model the trees using quantitative structure modelling (QSM).  The results of the QSM process can be found under 03_tree-qsms. For each tree multiple QSMs were made using different settings (10 replicas for each setting). The best models that were selected can be found under the best-models folder.  Data needed to make figure 1 under fig_1. ================================================                    Paper analysis================================================ We have provided all scripts used to extract the structural metrics, fit the models, and make the figures in the folder 02_code. The outputs from the analyses such as the CSV files with the tree structural metrics and the models can be found in 03_output. ================================================                    Funding================================================ EucFACE was built as an initiative of the Australian Government as part of the Nation-building Economic Stimulus Package, and is supported by the Australian Commonwealth through the Terrestrial Ecosystem Research Network (TERN) in collaboration with Western Sydney University.
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2025-07-17
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