eprintid: 10068584 rev_number: 25 eprint_status: archive userid: 608 dir: disk0/10/06/85/84 datestamp: 2019-02-21 17:30:59 lastmod: 2021-09-20 00:22:37 status_changed: 2019-02-21 17:30:59 type: article metadata_visibility: show creators_name: Vicari, MB creators_name: Disney, M creators_name: Wilkes, P creators_name: Burt, A creators_name: Calders, K creators_name: Woodgate, W title: Leaf and wood classification framework for terrestrial LiDAR point clouds ispublished: inpress divisions: UCL divisions: B03 divisions: C03 divisions: F26 keywords: 3D point clouds, field data, LiDAR, material separation, simulated data, terrestrial LiDAR, testing framework note: Copyright © 2019 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. abstract: Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. Leaf and wood separation is a key step to allow a new range of estimates from Terrestrial LiDAR data, such as quantifying above-ground biomass, leaf and wood area and their 3D spatial distributions. We present a new method to separate leaf and wood from single tree point clouds automatically. Our approach combines unsupervised classification of geometric features and shortest path analysis. The automated separation algorithm and its intermediate steps are presented and validated. Validation consisted of using a testing framework with synthetic point clouds, simulated using ray-tracing and 3D tree models and 10 field scanned tree point clouds. To evaluate results we calculated accuracy, kappa coefficient and F-score. Validation using simulated data resulted in an overall accuracy of 0.83, ranging from 0.71 to 0.94. Per tree average accuracy from synthetic data ranged from 0.77 to 0.89. Field data results presented and overall average accuracy of 0.89. Analysis of each step showed accuracy ranging from 0.75 to 0.98. F-scores from both simulated and field data were similar, with scores from leaf usually higher than for wood. Our separation method showed results similar to others in literature, albeit from a completely automated workflow. Analysis of each separation step suggests that the addition of path analysis improved the robustness of our algorithm. Accuracy can be improved with per tree parameter optimization. The library containing our separation script can be easily installed and applied to single tree point cloud. Average processing times are below 10 min for each tree. date: 2019-01-30 date_type: published official_url: http://doi.org/10.1111/2041-210X.13144 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1629655 doi: 10.1111/2041-210X.13144 lyricists_name: Boni Vicari, Matheus lyricists_name: Burt, Andrew lyricists_name: Disney, Mathias lyricists_name: Wilkes, Phillip lyricists_id: MBONI73 lyricists_id: ABURT75 lyricists_id: MIDIS56 lyricists_id: PTVWI50 actors_name: Kalinowski, Damian actors_id: DKALI47 actors_role: owner full_text_status: public publication: Methods in Ecology and Evolution issn: 2041-210X citation: Vicari, MB; Disney, M; Wilkes, P; Burt, A; Calders, K; Woodgate, W; (2019) Leaf and wood classification framework for terrestrial LiDAR point clouds. Methods in Ecology and Evolution 10.1111/2041-210X.13144 <https://doi.org/10.1111/2041-210X.13144>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10068584/1/Disney_Leaf%20and%20wood%20classification%20framework%20for%20terrestrial%20LiDAR%20point%20clouds_Proof.pdf