@article{01GPB7Y3M7ZTDH6HE6DJDE47XH,
  abstract     = {{Terrestrial laser scanning (TLS) is a ground-based approach to rapidly acquire 3D point clouds via Light Detection and Ranging (LiDAR) technologies. Quantifying tree-scale structure from TLS point clouds requires segmentation, yet there is a lack of automated methods available to the forest ecology community. In this work, we consider the problem of segmenting a forest TLS point cloud into individual tree point clouds. Different approaches have been investigated to identify and segment individual trees in a forest point cloud. Typically these methods require intensive parameter tuning and time-consuming user interactions, which has inhibited the application of TLS to large area research. Our goal is to define a new automated segmentation method that lifts these limitations.Our Topology-based Tree Segmentation (TTS) algorithm uses a new topological technique rooted in discrete Morse theory to segment input point clouds into single trees. TTS algorithm identifies distinctive tree structures (i.e., tree bottoms and tops) without user interactions. Tree tops and bottoms are then used to reconstruct single trees using the notion of relevant topological features. This mathematically well-established notion helps distinguish between noise and relevant tree features.To demonstrate the generality of our approach, we present an evaluation using multiple datasets, including different forest types and point densities. We also compare our TTS approach with open-source tree segmentation methods. The experiments show that we achieve a higher segmentation accuracy when performing point-by-point validation. Without expensive user interactions, TTS algorithm is promising for greater usage of TLS point clouds in the forest ecology community, such as fire risk and behavior modeling, estimating tree-level biodiversity structural traits, and above-ground biomass monitoring.}},
  articleno    = {{103145}},
  author       = {{Xu, Xin and Iuricich, Federico and Calders, Kim and Armston, John and De Floriani, Leila}},
  issn         = {{1569-8432}},
  journal      = {{INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION}},
  keywords     = {{LiDAR,Point cloud,Tree segmentation,Topological data analysis,cavelab}},
  language     = {{eng}},
  pages        = {{12}},
  title        = {{Topology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds}},
  url          = {{http://doi.org/10.1016/j.jag.2022.103145}},
  volume       = {{116}},
  year         = {{2023}},
}

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