Extracting the individual trees of urban forests from high density airborne LiDAR data

Document Type : Research Paper

Authors

1 MS. student of Geomatics Eng., Department of Civil Eng. and Transportation, University of Isfahan, Isfahan, I. R. Iran

2 Assistant Prof., Dept. of Geomatics Eng., Dept. of Civil Eng. and Transportation, University of Isfahan, Isfahan, I. R. Iran

3 Associate Prof., Dept. of Geomatics Eng., Dept. of Civil Eng. and Transportation, University of Isfahan, Isfahan, I. R. Iran

Abstract

Airborne LiDAR (Light Detection and Ranging) has a high potential to provide 3D data for research and operational applications in a wide range of disciplines related to management of forest ecosystems and urban trees. Most proposed methods for extracting the individual trees first detect the points of tree top or bottom and then use them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points effect on the process of detecting individual trees heavily. In this study, a new method is presented to extract the individual tree segments using LiDAR points with 10 cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium. The accuracy assessment of this method showed that with the increasing detection rate of young trees, it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors, respectively.

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Iranian Journal of Forest
Volume 10, Issue 1
June 2018
Pages 27-42

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History

  • Receive Date: 18 August 2016
  • Revise Date: 02 October 2017
  • Accept Date: 04 November 2017

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