{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T22:45:33Z","timestamp":1781477133320,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"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":["31971578"],"award-info":[{"award-number":["31971578"]}],"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>Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.<\/jats:p>","DOI":"10.3390\/rs13081442","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T21:27:44Z","timestamp":1617917264000},"page":"1442","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5038-1313","authenticated-orcid":false,"given":"Kaisen","family":"Ma","sequence":"first","affiliation":[{"name":"Research Center of Forestry Remote Sensing &amp; Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data &amp; Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5762-3538","authenticated-orcid":false,"given":"Yujiu","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1940-9952","authenticated-orcid":false,"given":"Fugen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing &amp; Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data &amp; Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Chen","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing &amp; Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data &amp; Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5401-6783","authenticated-orcid":false,"given":"Hua","family":"Sun","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing &amp; Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data &amp; Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"ref_1","unstructured":"(2017). FAO Voluntary Guidelines on National Forest Monitoring, Food and Agriculture Organization of the United Nations."},{"key":"ref_2","first-page":"1","article-title":"Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data","volume":"53","author":"Zhao","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rex, F.E., Silva, C.A., Dalla Corte, A.P., Klauberg, C., Mohan, M., Cardil, A., Silva, V.S.d., Almeida, D.R.A.d., Garcia, M., and Broadbent, E.N. (2020). Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12091498"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4734","DOI":"10.1021\/acs.est.7b01543","article-title":"Correction to Characteristics of Particulate Carbon Emissions from Real-World Chinese Coal Combustion","volume":"51","author":"Zhang","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.rse.2016.03.012","article-title":"Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data","volume":"178","author":"Cao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Leite, R.V., Silva, C.A., Mohan, M., Cardil, A., Almeida, D.R.A.D., Carvalho, S.d.P.C.e., Jaafar, W.S.W.M., Guerra-Hern\u00e1ndez, J., Weiskittel, A., and Hudak, A.T. (2020). Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models. Remote Sens., 12.","DOI":"10.3390\/rs12213599"},{"key":"ref_7","first-page":"1138","article-title":"Review on forest parameters inversion using LiDAR","volume":"20","author":"Li","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1080\/02827581.2016.1220617","article-title":"Valuation and production possibilities on a working forest using multi-objective programming, Woodstock, timber NPV, and carbon storage and sequestration","volume":"31","author":"Roise","year":"2016","journal-title":"Scand. J. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4725","DOI":"10.1080\/01431161.2010.494184","article-title":"A review of methods for automatic individual tree crown detection and delineation from passive remote sensing","volume":"32","author":"Ke","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/LGRS.2015.2443553","article-title":"Retrieval and Accuracy Assessment of Tree and Stand Parameters for Chinese Fir Plantation Using Terrestrial Laser Scanning","volume":"12","author":"Sun","year":"2015","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1080\/01431161.2015.1030043","article-title":"Agent-based region growing for individual tree crown delineation from airborne laser scanning (ALS) data","volume":"36","author":"Zhen","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1016\/j.foreco.2009.09.006","article-title":"Multiple attribute decision making for individual tree detection using high-resolution laser scanning","volume":"258","author":"Forzieri","year":"2009","journal-title":"Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1016\/j.rse.2009.02.010","article-title":"Capturing tree crown formation through implicit surface reconstruction using airborne lidar data","volume":"113","author":"Kato","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1007\/s11430-011-4212-3","article-title":"Feature analysis of LIDAR waveforms from forest canopies","volume":"54","author":"Liu","year":"2011","journal-title":"Sci. China Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.isprsjprs.2010.08.002","article-title":"A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements","volume":"65","author":"Jaakkola","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.isprsjprs.2017.09.006","article-title":"Graph SLAM correction for single scanner MLS forest data under boreal forest canopy","volume":"132","author":"Kukko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","unstructured":"Reitberger, J., Krzystek, P., and Stilla, U. (2009, January 9\u201313). Benefit of airborne full waveform lidar for 3D segmentation and classification of single trees. Proceedings of the ASPRS 2009 Annual Conference, Baltimore, MD, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.rse.2012.03.027","article-title":"Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data","volume":"123","author":"Yao","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"75","DOI":"10.14358\/PERS.78.1.75","article-title":"A New Method for Segmenting Individual Trees from the Lidar Point Cloud","volume":"78","author":"Li","year":"2012","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"ref_20","first-page":"45","article-title":"Lidar point cloud based fully automatic 3D single tree modelling in forest and evaluations of the procedure","volume":"37","author":"Wang","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yan, W., Guan, H., Cao, L., Yu, Y., Li, C., and Lu, J. (2020). A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12030515"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ku\u017eelka, K., Slav\u00edk, M., and Surov\u00fd, P. (2020). Very high density point clouds from UAV laser scanning for automatic tree stem detection and direct diameter measurement. Remote Sens., 12.","DOI":"10.3390\/rs12081236"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2016.05.028","article-title":"Lidar detection of individual tree size in tropical forests","volume":"183","author":"Ferraz","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1515\/jag.2007.018","article-title":"Non-parametric segmentation of ALS point clouds using mean shift","volume":"1","author":"Melzer","year":"2007","journal-title":"J. Appl. Geodesy"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Silva, V.S.d., Silva, C.A., Mohan, M., Cardil, A., Rex, F.E., Loureiro, G.H., Almeida, D.R.A.D., Broadbent, E.N., Gorgens, E.B., and Dalla Corte, A.P. (2020). Combined impact of sample size and modeling approaches for predicting stem volume in eucalyptus spp. forest plantations using field and LiDAR data. Remote Sens., 12.","DOI":"10.3390\/rs12091438"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.rse.2004.05.013","article-title":"LiDAR-based geometricreconstruction of boreal type forest stands at single tree level for forest and wildland fire management","volume":"92","author":"Morsdorf","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"968","DOI":"10.3390\/rs2040968","article-title":"Comparative analysis of clustering-based approaches for 3-D single tree detection using airborne fullwave LiDAR data","volume":"2","author":"Gupta","year":"2010","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4999","DOI":"10.1080\/01431161.2010.494633","article-title":"Prior-knowledge-based single-tree extraction","volume":"32","author":"Heinzel","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"923","DOI":"10.14358\/PERS.72.8.923","article-title":"Isolating individual trees in a Savanna woodland using small footprint LiDAR data","volume":"72","author":"Chen","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/S0034-4257(03)00008-7","article-title":"Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America","volume":"85","author":"Brandtberg","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5171","DOI":"10.1080\/01431161.2012.657363","article-title":"Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates","volume":"33","author":"Ene","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","first-page":"19","article-title":"Individual tree identification using different LIDAR and optical imagery data processing methods","volume":"1","author":"Smits","year":"2012","journal-title":"Biosyst. Inf. Technol."},{"key":"ref_33","first-page":"1055","article-title":"An individual tree segmentation method based on watershed algorithm and 3D spatial distribution analysis from airborne LiDAR point clouds","volume":"13","author":"Yang","year":"2020","journal-title":"IEEE J-STARS"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hao, Y., Widagdo, F.R.A., Liu, X., Quan, Y., Dong, L., and Li, F. (2021). Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning. Remote Sens., 13.","DOI":"10.3390\/rs13010024"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhen, Z., Quackenbush, L.J., and Zhang, L. (2016). Trends in automatic individual tree crown detection and delineation-evolution of LiDAR data. Remote Sens., 8.","DOI":"10.3390\/rs8040333"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wuming, Z., Jianbo, Q., Peng, W., Wang, H., Xiu, D., and Wang, X. (2016). An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, J., Yao, W., and Polewski, P. (2018). Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective. Remote Sens., 10.","DOI":"10.3390\/rs10091403"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.02.014","article-title":"An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy","volume":"92","author":"Hu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, W., Zheng, Q., Xiang, H., Chen, X., and Sakai, T. (2021). Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS\/PALSAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13020174"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"833","DOI":"10.3390\/rs2030833","article-title":"Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues","volume":"2","author":"Meng","year":"2010","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, W., Hu, X., Chen, W., Hong, Y., and Yang, M. (2018). Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques. Remote Sens., 10.","DOI":"10.3390\/rs10071078"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"555","DOI":"10.3390\/rs6010555","article-title":"Impact of tree-oriented growth order in marker-controlled region growing for individual tree crown delineation using airborne laser scanner (ALS) data","volume":"6","author":"Zhen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2004.10.013","article-title":"Estimating forest canopy fuel parameters using LIDAR data","volume":"94","author":"Andersen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2008.09.009","article-title":"Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers","volume":"113","author":"Zhao","year":"2009","journal-title":"Remote Sens Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1016\/j.rse.2008.03.004","article-title":"Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser","volume":"112","author":"Gobakken","year":"2008","journal-title":"Remote Sens Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/S0034-4257(02)00050-0","article-title":"Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration","volume":"82","author":"Pouliot","year":"2002","journal-title":"Remote. Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"357","DOI":"10.14358\/PERS.72.4.357","article-title":"Detection of Individual Tree Crowns in Airborne Lidar Data","volume":"72","author":"Koch","year":"2006","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Vauhkonen, J., Ene, L., Gupta, S., Heinzel, J., Holmgren, J., Pitk\u00e4nen, J., Solberg, S., Wang, Y., Weinacker, H., and Hauglin, K. (2011). Comparative testing of single-tree detection algorithms under different types of forest. Forestry.","DOI":"10.1093\/forestry\/cpr051"},{"key":"ref_49","first-page":"928","article-title":"A novel method for identifying the boundary of urban built-up areas with POI data","volume":"71","author":"Xu","year":"2016","journal-title":"Geogr. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/0143116042000298289","article-title":"Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LiDAR and multi-spectral remotely sensed data","volume":"26","author":"Koukoulas","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.03.014","article-title":"A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data","volume":"94","author":"Lu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.1109\/TGRS.2016.2538203","article-title":"A Hierarchical Approach to Three-Dimensional Segmentation of LiDAR Data at Single-Tree Level in a Multilayered Forest","volume":"54","author":"Paris","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/01431160902882561","article-title":"Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests","volume":"31","author":"Lee","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.3390\/rs4051190","article-title":"Advances in Forest Inventory Using Airborne Laser Scanning","volume":"4","author":"Yu","year":"2012","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1442\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:26:38Z","timestamp":1760361998000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1442"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,8]]},"references-count":54,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081442"],"URL":"https:\/\/doi.org\/10.3390\/rs13081442","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,8]]}}}