Quantitative Assessment of Scots Pine (Pinus Sylvestris L.) Whorl Structure in a Forest Environment Using Terrestrial Laser Scanning

State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to i...

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Vydáno v:IEEE journal of selected topics in applied earth observations and remote sensing Ročník 11; číslo 10; s. 3598 - 3607
Hlavní autoři: Pyorala, Jiri, Liang, Xinlian, Vastaranta, Mikko, Saarinen, Ninni, Kankare, Ville, Wang, Yunsheng, Holopainen, Markus, Hyyppa, Juha
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Branch
Branches
Data
Data models
Detection
Evaluation
Evergreen trees
forestry
Forests
Geospatial analysis
Insertion
Laser modes
Lasers
LiDAR
Measurement by laser beam
Methods
modeling
Occlusion
Parameters
Pine trees
Procurement
Remote sensing
Sawmills
Scanning
Three dimensional models
Three-dimensional displays
Vegetation
Wood
wood procurement
wood quality
ISSN:1939-1404, 2151-1535
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Popis
Shrnutí:State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to introduce a method that produces similar wood quality indicators in standing forests as those currently used in sawmills. Our aim was to develop a quantitative method to detect and model branches from terrestrial laser scanning (TLS) point clouds data of trees in a forest environment. The test data were obtained from 158 Scots pines (Pinus sylvestris L.) in six mature forest stands. The method was evaluated for the accuracy of the following branch parameters: Number of whorls per tree and for every whorl, the maximum branch diameter and the branch insertion angle associated with it. The analysis concentrated on log-sections (stem diameter >15 cm) where the branches most affect wood's value added. The quantitative whorl detection method had an accuracy of 69.9% and a 1.9% false positive rate. The estimates of the maximum branch diameters and the corresponding insertion angles for each whorl were underestimated by 0.34 cm (11.1%) and 0.67° (1.0%), with a root-mean-squared error of 1.42 cm (46.0%) and 17.2° (26.3%), respectively. Distance from the scanner, occlusion, and wind were the main external factors that affect the method's functionality. Thus, the completeness and point density of the data should be addressed when applying TLS point cloud based tree models to assess branch parameters.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2018.2819598