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A topology-based approach to individual tree segmentation from airborne LiDAR data

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Abstract

Light Detection and Ranging (LiDAR) sensors emit laser signals to calculate distances based on the time delay of the returned laser pulses. They can generate dense point clouds to map forest structures at a high level of spatial resolution. In this work, we consider the problem of segmenting out individual trees in Airborne Laser Scanning (ALS) point clouds. Several techniques have been proposed for this purpose which generally require time-consuming parameter tuning and intense user interaction. Our goal is to design an automated, intuitive, and robust approach requiring minimal user interaction. To this aim, we define a new segmentation approach based on topological tools, namely on the watershed transform and on persistence-based simplification. The approach follows a divide-and-conquer paradigm, splitting a LiDAR point cloud into regions with uniform densities. Our algorithm is validated on coniferous forests collected in the NEW technologies for a better mountain FORest timber mobilization (NEWFOR) dataset, and deciduous forests collected in the Smithsonian Environmental Research Center (SERC) dataset. When compared to four state-of-the-art tree segmentation algorithms, our method performs best in both ecosystem types. It provides more accurate stem estimations and single tree segmentation results at various of stem and point densities. Also, our method requires only a single (Boolean) parameter, which makes it extremely easy to use and very promising for various forest analysis applications, such as biomass estimation and field inventory surveys.

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Acknowledgements

This work has been partially supported by the US National Science Foundation under grant number IIS-1910766. We thank Dr. Ralph Dubayah, Dr. Hao Tang, Dr. Laura Duncanson, Dr. Steven Hancock and Dr. John Armston for their help and valuable comments. We also thank the reviewers and the associate editor for their time and their careful consideration of our manuscript.

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  1. University of Maryland, College Park, MD, USA

    Xin Xu & Leila De Floriani

  2. Clemson University, Clemson, SC, USA

    Federico Iuricich

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Correspondence to Xin Xu.

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The experimental datasets used in this manuscript are publicly available by provided by the NEW technologies for a better mountain FORest timber mobilization (NEWFOR) program [12], NASA Goddard’s LiDAR, Hyperspectral and Thermal Imager (G-LiHT) [7], and National Science Foundation’s National Ecological Observatory Network (NEON). The experimental results reported in this manuscript are collected in the same hardware environment and with the standard process.

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Appendix

Appendix

Tables 7 and 8 show the values used for each parameter required by the segmentation methods provided by the lidR package. For the approach by Li et al [33], a description of the thresholds Zu, dt1, and dt2 can be found in the original paper [33]. We refer an interested reader to the lidR package documentation (https://cran.r-project.org/web/packages/lidR/lidR.pdf) for a description of the parameters used by the CHM generation [29], Dalponte et al. [8], Silva et al. [51] and Watershed [37].

Table 7 Values of key parameters for each method implemented in the lidR package [25] used in the NEWFOR dataset
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Table 8 Values of key parameters for each method implemented in the lidR package [25] used in the SERC dataset
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Xu, X., Iuricich, F. & De Floriani, L. A topology-based approach to individual tree segmentation from airborne LiDAR data. Geoinformatica 27, 759–788 (2023). https://doi.org/10.1007/s10707-023-00487-4

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