Individual tree segmentation from UAS Lidar data based on hierarchical filtering and clustering

• Published in: International journal of digital earth Vol. 17; no. 1
• Main Authors: Zhang, Cailian, Song, Chengwen, Zaforemska, Aleksandra, Zhang, Jiaxing, Gaulton, Rachel, Dai, Wenxia, Xiao, Wen
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2024; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; Cluster analysis; cluster merging; Clustering; data collection; Datasets; Deep learning; Filtration; Forest management; forests; Hydrofluorocarbons; Individual tree segmentation (ITS); intensity; landscapes; Leaves; Lidar; Machine learning; Outliers (statistics); Parameter estimation; Performance evaluation; Plant species; point cloud; Robustness; Segmentation; Singular value decomposition; Species; Statistical analysis; Trees; unoccupied aerial systems (UAS); Woodlands

Accurate Individual Tree Segmentation (ITS) is fundamental to fine-scale forest structure and management studies. Light detection and ranging (Lidar) from Unoccupied Aerial Systems (UAS) has shown strengths in ITS and tree parameter estimation at stand and landscape scales. However, dense woodlands...