Biomass; Forest; Geometric Feature; Leaf-Wood Separation; Segmentation; Tree Information Modeling; Geometric feature; Information Modeling; Leaf-wood separation; Light detection and ranging; Point-clouds; Tree information modeling; Tree structures; Wood separations
Abstract :
[en] A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
NURUNNABI, Abdul Awal Md ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Teferle, Felicia ; Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg
Laefer, Debra F.; Center for Urban Science + Progress, Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, United States
Chen, Meida; Institute for Creative Technologies, University of Southern California, Los Angeles, United States
Ali, Mir Masoom; Department of Mathematical Sciences, Ball State University, Muncie, United States
External co-authors :
yes
Language :
English
Title :
Development of a Precise Tree Structure from LiDAR Point Clouds
Publication date :
11 June 2024
Event name :
ISPRS TC II Mid-term Symposium “The Role of Photogrammetry for a Sustainable World”, 11–14 June 2024, Las Vegas, Nevada, USA
Event place :
Las Vegas, Usa
Event date :
11-06-2024 => 14-06-2024
Audience :
International
Journal title :
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
ISSN :
1682-1750
Publisher :
International Society for Photogrammetry and Remote Sensing
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