Data underlying the publication: Improving the 3D Representation of Plant Architecture and Parameterization Efficiency of Functional-Structural Tree Models using Terrestrial LiDAR Data
DOI:10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142.v1
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For a link that will always point to the latest version, please use
DOI: 10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142
DOI: 10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142
Datacite citation style
Bekkers, Vera; Lau Sarmiento, A.I. (Alvaro) (2024): Data underlying the publication: Improving the 3D Representation of Plant Architecture and Parameterization Efficiency of Functional-Structural Tree Models using Terrestrial LiDAR Data. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Data repository for all materials for the paper "Improving the 3D Representation of Plant Architecture and Parameterization Efficiency of Functional-Structural Tree Models using Terrestrial LiDAR Data". For this paper we demonstrated the use of TLS data for FSP model inputs of a general tropical tree model and Scots Pine model. This dataset constrains the raw tropical tree point clouds collected in Guyana, and scots pine trees collected in Loobs, the Netherlands. These were processed by removing the leaves using the LeWoS algorithm and fitting a Quantitative Structural Model (QSM) to derive tree architecture information. The information from the QSM was processed and used as FSPM model inputs. The dataset contains the QSM output results from the processed point clouds. Additionally the field data used to assess the accuracy are provided, and the python scripts to process, analyse, and visualise the data.
History
- 2024-12-11 first online, published, posted
Publisher
4TU.ResearchDataFormat
.txt, .xlsx, .csv, .pyReferences
Organizations
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & ResearchDATA
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7d137d04e001a29610da0a1358787897tree_data_80_23_noreg.txt - 106 bytesMD5:
9b5e520ea31d144c51e211e2f3f78edctree_data_TREE04_noreg.txt - 105 bytesMD5:
1ae56fd08d22c2b378ddccaf4f493748tree_data_TREE102_noreg.txt - 104 bytesMD5:
ae390ffda338aa87230eb02dff683c4dtree_data_TREE18_noreg.txt - 113 bytesMD5:
5ca24aae58450bf00104178b4afb484dtree_data_TREE31_noreg.txt - 108 bytesMD5:
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c7252f34588e0188790e35824ec899a0tree_data_TREE35_noreg.txt - 108 bytesMD5:
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8a48d5fe4b1b5c77e572e36f94a84040tree_data_TREE63_noreg.txt - 108 bytesMD5:
f9eb434890a50839af260199def17c70tree_data_TREE85_noreg.txt -
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