Woody Surface Area Measurements with Terrestrial Laser Scanning Relate to the Anatomical and Structural Complexity of Urban Trees

2021 | journal article. A publication with affiliation to the University of Göttingen.

Jump to: Cite & Linked | Documents & Media | Details | Version history

Cite this publication

​Woody Surface Area Measurements with Terrestrial Laser Scanning Relate to the Anatomical and Structural Complexity of Urban Trees​
Arseniou, G.; MacFarlane, D. W. & Seidel, D. ​ (2021) 
Remote Sensing13(16) pp. 3153​.​ DOI: https://doi.org/10.3390/rs13163153 

Documents & Media

License

Details

Authors
Arseniou, Georgios; MacFarlane, David W.; Seidel, Dominik 
Abstract
Urban forests are part of the global forest network, providing important benefits to human societies. Advances in remote-sensing technology can create detailed 3D images of trees, giving novel insights into tree structure and function. We used terrestrial laser scanning and quantitative structural models to provide comprehensive characterizations of the woody surface area allometry of urban trees and relate them to urban tree anatomy, physiology, and structural complexity. Fifty-six trees of three species (Gleditsia triacanthos L., Quercus macrocarpa Michx., Metasequoia glyptostroboides Hu & W.C. Cheng) were sampled on the Michigan State University campus. Variations in surface area allocation to non-photosynthesizing components (main stem, branches) are related to the fractal dimension of tree architecture, in terms of structural complexity (box-dimension metric) and the distribution of “path” lengths from the tree base to every branch tip. The total woody surface area increased with the box-dimension metric, but it was most strongly correlated with the 25th percentile of path lengths. These urban trees mainly allocated the woody surface area to branches, which changed with branch order, branch-base diameter, and branch-base height. The branch-to-stem area ratio differed among species and increased with the box-dimension metric. Finally, the woody surface area increased with the crown surface area of the study trees across all species combined and within each species. The results of this study provide novel data and new insights into the surface area properties of urban tree species and the links with structural complexity and constraints on tree morphology.
Urban forests are part of the global forest network, providing important benefits to human societies. Advances in remote-sensing technology can create detailed 3D images of trees, giving novel insights into tree structure and function. We used terrestrial laser scanning and quantitative structural models to provide comprehensive characterizations of the woody surface area allometry of urban trees and relate them to urban tree anatomy, physiology, and structural complexity. Fifty-six trees of three species (Gleditsia triacanthos L., Quercus macrocarpa Michx., Metasequoia glyptostroboides Hu & W.C. Cheng) were sampled on the Michigan State University campus. Variations in surface area allocation to non-photosynthesizing components (main stem, branches) are related to the fractal dimension of tree architecture, in terms of structural complexity (box-dimension metric) and the distribution of “path” lengths from the tree base to every branch tip. The total woody surface area increased with the box-dimension metric, but it was most strongly correlated with the 25th percentile of path lengths. These urban trees mainly allocated the woody surface area to branches, which changed with branch order, branch-base diameter, and branch-base height. The branch-to-stem area ratio differed among species and increased with the box-dimension metric. Finally, the woody surface area increased with the crown surface area of the study trees across all species combined and within each species. The results of this study provide novel data and new insights into the surface area properties of urban tree species and the links with structural complexity and constraints on tree morphology.
Issue Date
2021
Journal
Remote Sensing 
Organization
Fakultät für Forstwissenschaften und Waldökologie ; Burckhardt-Institut ; Abteilung Räumliche Strukturen und Digitalisierung von Wäldern ; Abteilung Waldbau und Waldökologie der gemäßigten Zonen 
eISSN
2072-4292
Language
English
Sponsor
United States Department of Agriculture Forest Service, Forest Inventory and Analysis Program, Northern Research Station

Reference

Citations


Social Media