{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:50:06Z","timestamp":1776275406539,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371382"],"award-info":[{"award-number":["42371382"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871247"],"award-info":[{"award-number":["41871247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Successfully separating wood and leaves in forest plots is a prerequisite for measuring structural parameters and reconstructing 3D forest models. Terrestrial laser scanning (TLS) can distinguish between the leaves and wood of trees through precise and dense point clouds. However, most existing wood\u2013leaf separation methods face significant accuracy issues, especially in dense forests, due to the complications introduced by canopy shading. In this study, we propose a method to separate the wood and leaves in forest plots using the clustering features of TLS data. The method first filters a point cloud to remove the ground points, and then clusters the point cloud using a region-growing algorithm. Next, the clusters are processed based on their sizes and numbers of points for preliminary separation. Chaos Distance is introduced to characterize the observation that wood points are more orderly while leaf points are more chaotic and disorganized. Lastly, the clusters\u2019 Chaos Distance is used for the final separation. Three representative plots were used to validate this method, achieving an average accuracy of 0.938, a precision of 0.927, a recall of 0.892, and an F1 score of 0.907. The three sample plots were processed in 5.18, 3.75, and 14.52 min, demonstrating high efficiency. Comparing the results with the LeWoS and RF models showed that our method better addresses the accuracy issues of complex canopy structures.<\/jats:p>","DOI":"10.3390\/rs16183355","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T05:53:03Z","timestamp":1725947583000},"page":"3355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Cluster-Based Wood\u2013Leaf Separation Method for Forest Plots Using Terrestrial Laser Scanning Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4948-8900","authenticated-orcid":false,"given":"Hao","family":"Tang","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4807-5012","authenticated-orcid":false,"given":"Shihua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Technology Innovation Center for Southwest Land Space Ecological Restoration and Comprehensive Renovation, Ministry of Natural Resources, Chengdu 610045, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4553-4874","authenticated-orcid":false,"given":"Zhonghua","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9530-4733","authenticated-orcid":false,"given":"Ze","family":"He","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.2528\/PIERB19080702","article-title":"Electromagnetic Resonances of Natural Grasslands and Their Effects on Radar Vegetation Index","volume":"86","author":"Soliman","year":"2020","journal-title":"Prog. Electromagn. Res. B"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/s40725-019-00090-7","article-title":"Methods of Forest Structure Research: A Review","volume":"5","author":"Hui","year":"2019","journal-title":"Curr For. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1111\/2041-210X.13342","article-title":"LeWoS: A Universal Leaf-wood Classification Method to Facilitate the 3D Modelling of Large Tropical Trees Using Terrestrial LiDAR","volume":"11","author":"Wang","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_4","first-page":"102","article-title":"Geometric Leaf Classification","volume":"133","author":"Kalyoncu","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_5","first-page":"916","article-title":"Classification of Plant Leaf Images with Complicated Background","volume":"205","author":"Wang","year":"2008","journal-title":"Appl. Math. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121297","DOI":"10.1016\/j.physa.2019.121297","article-title":"Leaf-Based Plant Species Recognition Based on Improved Local Binary Pattern and Extreme Learning Machine","volume":"527","author":"Turkoglu","year":"2019","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.oneear.2020.05.001","article-title":"Applications in Remote Sensing to Forest Ecology and Management","volume":"2","author":"Lechner","year":"2020","journal-title":"One Earth"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"117484","DOI":"10.1016\/j.foreco.2019.117484","article-title":"On Promoting the Use of Lidar Systems in Forest Ecosystem Research","volume":"450","author":"Beland","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2015.10.011","article-title":"LiDAR: An Important Tool for next-Generation Phenotyping Technology of High Potential for Plant Phenomics?","volume":"119","author":"Lin","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1038\/s41565-021-00895-3","article-title":"Nanophotonics for Light Detection and Ranging Technology","volume":"16","author":"Kim","year":"2021","journal-title":"Nat. Nanotechnol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/MGRS.2020.3032713","article-title":"Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective","volume":"9","author":"Guo","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_12","first-page":"286","article-title":"Light Detection and Ranging (LIDAR): An Emerging Tool for Multiple Resource Inventory","volume":"103","author":"Reutebuch","year":"2005","journal-title":"J. For."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s10661-008-0254-1","article-title":"Using LiDAR Technology in Forestry Activities","volume":"151","author":"Akay","year":"2009","journal-title":"Environ. Monit. Assess."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.06.021","article-title":"International Benchmarking of Terrestrial Laser Scanning Approaches for Forest Inventories","volume":"144","author":"Liang","year":"2018","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1109\/LGRS.2019.2896613","article-title":"Extracting Wood Point Cloud of Individual Trees Based on Geometric Features","volume":"16","author":"Su","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"018502","DOI":"10.1117\/1.JRS.14.018502","article-title":"Leaf and Wood Separation of Poplar Seedlings Combining Locally Convex Connected Patches and K-Means++ Clustering from Terrestrial Laser Scanning Data","volume":"14","author":"Hu","year":"2020","journal-title":"J. Appl. Rem. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sun, J., Wang, P., Gao, Z., Liu, Z., Li, Y., Gan, X., and Liu, Z. (2021). Wood\u2013Leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information. Remote Sens., 13.","DOI":"10.3390\/rs13204050"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7038","DOI":"10.1109\/TGRS.2020.3032167","article-title":"Leaf and Wood Separation for Individual Trees Using the Intensity and Density Data of Terrestrial Laser Scanners","volume":"59","author":"Tan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.envsoft.2010.12.008","article-title":"An Architectural Model of Trees to Estimate Forest Structural Attributes Using Terrestrial LiDAR","volume":"26","author":"Fournier","year":"2011","journal-title":"Environ. Modell. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.isprsjprs.2021.06.012","article-title":"Wood and Leaf Separation from Terrestrial LiDAR Point Clouds Based on Mode Points Evolution","volume":"178","author":"Hui","year":"2021","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5705111","DOI":"10.1109\/TGRS.2022.3218603","article-title":"Graph-Based Leaf\u2013Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds","volume":"60","author":"Tian","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1111\/2041-210X.13144","article-title":"Leaf and Wood Classification Framework for Terrestrial LiDAR Point Clouds","volume":"10","author":"Vicari","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, J., Wei, H., Zhou, G., and Song, L. (2019). Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales. Sensors, 19.","DOI":"10.3390\/s19081852"},{"key":"ref_24","first-page":"43","article-title":"Foliar and Woody Materials Discriminated Using Terrestrial LiDAR in a Mixed Natural Forest","volume":"64","author":"Zhu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, S., Dai, L., Wang, H., Wang, Y., He, Z., and Lin, S. (2017). Estimating Leaf Area Density of Individual Trees Using the Point Cloud Segmentation of Terrestrial LiDAR Data and a Voxel-Based Model. Remote Sens., 9.","DOI":"10.3390\/rs9111202"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1109\/TGRS.2019.2947198","article-title":"Improved Supervised Learning-Based Approach for Leaf and Wood Classification from LiDAR Point Clouds of Forests","volume":"58","author":"Calders","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, D., Brunner, J., Ma, Z., Lu, H., Hollaus, M., Pang, Y., and Pfeifer, N. (2018). Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging. Forests, 9.","DOI":"10.3390\/f9050252"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3668","DOI":"10.1109\/TCYB.2019.2950779","article-title":"A Survey of Optimization Methods from a Machine Learning Perspective","volume":"50","author":"Sun","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TGRS.2015.2459716","article-title":"Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components within Terrestrial Lidar Point Cloud Data of Forest Canopies","volume":"54","author":"Ma","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","unstructured":"(2024, January 09). PCL Point Cloud Library (PCL). Available online: https:\/\/github.com\/PointCloudLibrary\/pcl\/blob\/master\/doc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D Is Here: Point Cloud Library (PCL). Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5701517","DOI":"10.1109\/TGRS.2021.3121256","article-title":"Discriminating Forest Leaf and Wood Components in TLS Point Clouds at Single-Scan Level Using Derived Geometric Quantities","volume":"60","author":"Tan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1111\/2041-210X.13715","article-title":"A Novel and Efficient Method for Wood\u2013Leaf Separation from Terrestrial Laser Scanning Point Clouds at the Forest Plot Level","volume":"12","author":"Wan","year":"2021","journal-title":"Methods Ecol. Evol."},{"key":"ref_34","unstructured":"(2024, January 09). LiDAR360. Available online: https:\/\/www.lidar360.com\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/2041-210X.12301","article-title":"Nondestructive Estimates of Above-ground Biomass Using Terrestrial Laser Scanning","volume":"6","author":"Calders","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_36","unstructured":"Weiser, H., Sch\u00e4fer, J., Winiwarter, L., Kra\u0161ovec, N., Seitz, C., Schimka, M., Anders, K., Baete, D., Braz, A.S., and Brand, J. (2021). Terrestrial, UAV-Borne, and Airborne Laser Scanning Point Clouds of Central European Forest Plots, Germany, with Extracted Individual Trees and Manual Forest Inventory Measurements, PANGAEA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016). An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yu, B., Chen, J., Liu, C., Zhan, K., Sui, X., Xue, Y., and Li, J. (2019, January 20\u201322). Research on Lidar Point Cloud Segmentation and Collision Detection Algorithm. Proceedings of the 2019 6th International Conference on Information Science and Control Engineering (ICISCE), Shanghai, China.","DOI":"10.1109\/ICISCE48695.2019.00101"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2015.01.011","article-title":"Octree-Based Region Growing for Point Cloud Segmentation","volume":"104","author":"Vo","year":"2015","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, J., Zhang, B., Wang, Y., Yao, J., Zhang, X., Fan, B., Li, X., Hai, Y., and Fan, X. (2022). Three-Dimensional Reconstruction and Phenotype Measurement of Maize Seedlings Based on Multi-View Image Sequences. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.974339"},{"key":"ref_41","first-page":"102830","article-title":"Unsupervised Ground Filtering of Airborne-Based 3D Meshes Using a Robust Cloth Simulation","volume":"111","author":"Yu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yu, D., Li, A., Li, J., Xu, Y., and Long, Y. (2023). Mean Inflection Point Distance: Artificial Intelligence Mapping Accuracy Evaluation Index\u2014An Experimental Case Study of Building Extraction. Remote Sens., 15.","DOI":"10.3390\/rs15071848"},{"key":"ref_43","first-page":"6504705","article-title":"An Elliptical Distance Based Photon Point Cloud Filtering Method in Forest Area","volume":"19","author":"Yang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ji, F., Ming, D., Zeng, B., Yu, J., Qing, Y., Du, T., and Zhang, X. (2021). Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN. Remote Sens., 13.","DOI":"10.3390\/rs13112207"},{"key":"ref_45","unstructured":"(2024, September 02). Nine Layers of the Edible Forest Garden. Available online: https:\/\/tcpermaculture.com\/site\/plant-index\/."},{"key":"ref_46","unstructured":"(2020, December 05). LeWoS. Available online: https:\/\/github.com\/dwang520\/LeWoS."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fernandes, J.P., and Saraiva, J. (2017, January 23). Energy Efficiency across Programming Languages: How Do Energy, Time, and Memory Relate?. Proceedings of the 10th ACM SIGPLAN International Conference on Software Language Engineering, Vancouver, BC, Canada.","DOI":"10.1145\/3136014.3136031"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3355\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:52:50Z","timestamp":1760111570000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183355"],"URL":"https:\/\/doi.org\/10.3390\/rs16183355","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,10]]}}}