Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS

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This article presents a new method to automatically detect stems in dense and homogeneous forest using single-scan terrestrial laser scanning (TLS) data.

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23 p.

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Xia, Shaobo; Wang, Cheng; Pan, Feifei; Xi, Xiaohuan; Zeng, Hongcheng & Liu, He October 30, 2015.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Arts and Sciences to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 20 times , with 5 in the last month . More information about this article can be viewed below.

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This article presents a new method to automatically detect stems in dense and homogeneous forest using single-scan terrestrial laser scanning (TLS) data.

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23 p.

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Abstract: Stem characteristics of plants are of great importance to both ecology study and forest management. Terrestrial laser scanning (TLS) may provide an effective way to characterize the fine-scale structures of vegetation. However, clumping plants, dense foliage and thin structure could intensify the shadowing effect and pose a series of problems in identifying stems, distinguishing neighboring stems, and merging disconnected stem parts in point clouds. This paper presents a new method to automatically detect stems in dense and homogeneous forest using single-scan TLS data. Stem points are first identified with a two-scale classification method. Then a clustering approach is used to group the candidate stem points. Finally, a direction-growing algorithm based on a simple stem curve model is applied to merge stem points. Field experiments were carried out in two different bamboo plots with a stem density of about 7500 stems/ha. Overall accuracy of the stem detection is 88% and the quality of detected stems is mainly affected by the shadowing effect. Results indicate that the proposed method is feasible and effective in detection of bamboo stems using TLS data, and can be applied to other species of single-stem plants in dense forests.

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  • Forests, 2015. Basel, Switzerland: Multidisciplinary Digital Publishing Institute

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Publication Information

  • Publication Title: Forests
  • Volume: 6
  • Pages: 1-23
  • Peer Reviewed: Yes

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • August 31, 2015

Accepted Date

  • October 28, 2015

Creation Date

  • October 30, 2015

Added to The UNT Digital Library

  • Aug. 29, 2017, 9:38 a.m.

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Xia, Shaobo; Wang, Cheng; Pan, Feifei; Xi, Xiaohuan; Zeng, Hongcheng & Liu, He. Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS, article, October 30, 2015; Basel, Switzerland. (digital.library.unt.edu/ark:/67531/metadc990964/: accessed October 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.