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Individual tree segmentation is a prerequisite to obtain individual tree details but is highly dependent on the accuracy of seed point detection. However, most of the existing methods, such as the local maximum (LM) and CHM-based methods, are strongly dependent on the window size, and, for individual tree segmentation, they can result in over-segmentation and under-segmentation, especially in natural forests. In this paper, we propose an adaptive crown shaped algorithm for individual tree segmentation without consideration of the window size. It was implemented in four plots with different forest types and topographies (i.e., planted coniferous forest with flat terrain, coniferous forest with sloping terrain, mixed forest with flat terrain and broadleaf forest with flat terrain). First, the normalized point clouds were rotated and blocked at multiple angles to extract the surface points of the forest. Then, the crown boundaries were delineated by analyzing the crown profiles to extract the treetops as seed points. Finally, a region growing method based on seed points was applied for individual tree segmentation. Our results showed that the recall, precision and F1-score of seed point detection reached 91.6%, 95.9% and 0.94, respectively, and that the accuracy rates for individual tree segmentation for the four plots were 87.7%, 80.6%, 73.2% and 70.5%, respectively. Our proposed method can effectively detect seed points via the adaptive crown shaped algorithm and reduce the impacts of elongated branches by applying distance thresholds between trees, enhancing the accuracy of seed point detection and subsequently improving the precision of individual tree segmentation. In addition, the proposed algorithm demonstrated superior performance in comparison to LM and CHM-based methods for the calculation of seed points, as well as outperforming PCS in individual tree segmentation. The proposed method demonstrates effectiveness and feasibility in dense forests and natural forests, providing an important reference for future research on seed point detection and individual tree segmentation.<\/jats:p>","DOI":"10.3390\/rs16050825","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T07:56:02Z","timestamp":1709106962000},"page":"825","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Individual Tree Segmentation Based on Seed Points Detected by an Adaptive Crown Shaped Algorithm Using UAV-LiDAR Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0418-5702","authenticated-orcid":false,"given":"Jiao","family":"Yu","sequence":"first","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Loess, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Xi\u2019an 710054, China"}]},{"given":"Lei","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Loess, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-7449","authenticated-orcid":false,"given":"Zhenhong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Loess, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Ecological Geology and Disaster Prevention, Ministry of Natural Resources, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"ref_1","first-page":"1138","article-title":"Review on forest parameters inversion using LiDAR","volume":"20","author":"Li","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2020.10.016","article-title":"Individual tree detection and crown delineation from Unmanned Aircraft System (UAS) LiDAR in structurally complex mixed species eucalypt forests","volume":"171","author":"Jaskierniak","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2012.01.020","article-title":"3-D mapping of a multi-layered mediterranean forest using ALS data","volume":"121","author":"Ferraz","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5701416","DOI":"10.1109\/TGRS.2021.3121419","article-title":"A hierarchical region-merging algorithm for 3-D segmentation of individual trees using UAV-LiDAR point clouds","volume":"60","author":"Hao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1080\/22797254.2022.2129095","article-title":"Individual tree detection from unmanned aerial vehicle (UAV) derived point cloud data in a mixed broadleaf forest using hierarchical graph approach","volume":"55","author":"Ahmadi","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_7","first-page":"67","article-title":"Accurate measurement of individual tree position based on DBH extraction of terrestrial laser scanning","volume":"33","author":"Liang","year":"2020","journal-title":"For. Res."},{"key":"ref_8","first-page":"48","article-title":"Optimization of individual tree segmentation methods for high canopy density plantation based on UAV LiDAR","volume":"58","author":"Zhu","year":"2022","journal-title":"Sci. Silvae Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1080\/22797254.2018.1474722","article-title":"Single-tree detection in high-density LiDAR data from UAV-based survey","volume":"51","author":"Balsi","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, D., Wang, H., Xu, W., Luan, Z., and Xu, X. (2021). LiDAR applications to estimate forest biomass at individual tree scale: Opportunities, challenges and future perspectives. Forests, 12.","DOI":"10.3390\/f12050550"},{"key":"ref_11","first-page":"82805","article-title":"Review on individual tree detection based on airborne LiDAR","volume":"8","author":"Liu","year":"2018","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_12","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_13","first-page":"180","article-title":"Extraction of the parameters of single tree structure based on SFM algorithm","volume":"35","author":"Sun","year":"2020","journal-title":"J. Northwest For. Univ."},{"key":"ref_14","first-page":"139","article-title":"Usage of Structure-from-Motion for urban forest inventory","volume":"41","author":"Wang","year":"2021","journal-title":"J. Southwest For. Univ. (Nat. Sci.)"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112857","DOI":"10.1016\/j.rse.2021.112857","article-title":"Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)","volume":"270","author":"Huo","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, X., Shen, X., Cao, L., Wang, G., and Cao, F. (2019). Assessment of individual tree detection and canopy cover estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) data in planted forests. Remote Sens., 11.","DOI":"10.3390\/rs11080908"},{"key":"ref_17","first-page":"757","article-title":"Research on single tree segmentation algorithm of UAV LiDAR plantation","volume":"52","author":"Yu","year":"2022","journal-title":"Laser Infrared"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1109\/JSTARS.2021.3135491","article-title":"Estimating tree structural parameters via automatic tree segmentation from LiDAR point cloud data","volume":"15","author":"Itakura","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_19","first-page":"196","article-title":"A new method of individual tree recognition based on airborne LiDAR data","volume":"26","author":"Tang","year":"2011","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.isprsjprs.2017.07.001","article-title":"Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds","volume":"130","author":"Hamraz","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7199","DOI":"10.1080\/01431161.2014.967886","article-title":"Isolating individual trees in a closed coniferous forest using small footprint LiDAR data","volume":"35","author":"Zhao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","first-page":"62","article-title":"Individual tree structure parameters and effective crown of the stand extraction base on airborne LiDAR data","volume":"54","author":"Geng","year":"2018","journal-title":"Sci. Silvae Sin."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"2690","DOI":"10.1109\/TGRS.2013.2264548","article-title":"Bayesian approach to tree detection based on airborne laser scanning data","volume":"52","author":"Lahivaara","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2004.10.011","article-title":"Automated tree recognition in old growth conifer stands with high resolution digital imagery","volume":"94","author":"Leckie","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, X., Jiang, K., Zhu, Y., Wang, X., and Yun, T. (2021). Individual tree crown segmentation directly from UAV-Borne LiDAR data using the PointNet of deep learning. Forests, 12.","DOI":"10.3390\/f12020131"},{"key":"ref_27","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_28","first-page":"82","article-title":"Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests","volume":"52","author":"Wu","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","first-page":"147","article-title":"Single tree segmentation method for terrestrial LiDAR point cloud based on connectivity marker optimization","volume":"50","author":"Hui","year":"2023","journal-title":"Chin. J. Lasers"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, X.-H., Zhang, Y.-Z., and Xu, M.-M. (2019). A multi-threshold segmentation for tree-level parameter extraction in a deciduous forest using small-footprint airborne LiDAR data. Remote Sens., 11.","DOI":"10.3390\/rs11182109"},{"key":"ref_31","first-page":"66","article-title":"The single tree segmentation of UAV high-density LiDAR point cloud data based on coniferous plantations","volume":"42","author":"Wang","year":"2022","journal-title":"J. Cent. South Univ. For. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ma, K., Xiong, Y., Jiang, F., Chen, S., and Sun, H. (2021). A novel vegetation point cloud density tree-segmentation model for overlapping crowns using UAV LiDAR. Remote Sens., 13.","DOI":"10.3390\/rs13081442"},{"key":"ref_33","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_34","first-page":"W13","article-title":"Clustering in airborne laser scanning raw data for segmentation of single trees","volume":"34","author":"Morsdorf","year":"2003","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.isprsjprs.2015.01.010","article-title":"Detection of fallen trees in ALS point clouds using a normalized cut approach trained by simulation","volume":"105","author":"Polewski","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.14358\/PERS.79.12.1147","article-title":"3D tree reconstruction from simulated small footprint waveform LiDAR","volume":"79","author":"Wu","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","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_38","first-page":"45","article-title":"Research on single tree segmentation based on UAV LiDAR point cloud data","volume":"42","author":"Liu","year":"2022","journal-title":"J. Cent. South Univ. For. Technol."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1109\/JSTARS.2020.2979369","article-title":"An individual tree segmentation method based on watershed algorithm and three-dimensional spatial distribution analysis from airborne LiDAR point clouds","volume":"13","author":"Yang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ma, Z., Pang, Y., Wang, D., Liang, X., and Lu, H. (2020). Individual tree crown segmentation of a larch plantation using airborne laser scanning data based on region growing and canopy morphology features. Remote Sens., 12.","DOI":"10.3390\/rs12071078"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10099","DOI":"10.1109\/TGRS.2019.2931408","article-title":"Assessing the Impacts of Various Factors on Treetop Detection Using LiDAR-Derived Canopy Height Models","volume":"57","author":"Nie","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2015.02.013","article-title":"Effect of slope on treetop detection using a LiDAR Canopy Height Model","volume":"104","author":"Khosravipour","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2179942","DOI":"10.1080\/22797254.2023.2179942","article-title":"UAV DTM acquisition in a forested area\u2014Comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1)","volume":"56","author":"Stroner","year":"2023","journal-title":"Eur. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2016.03.016","article-title":"Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas","volume":"117","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","first-page":"106","article-title":"The application of LiDAR data in forest","volume":"18","author":"Zhao","year":"2008","journal-title":"Remote Sens. Inf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"564","DOI":"10.5589\/m03-027","article-title":"Measuring individual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass","volume":"29","author":"Popescu","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2012.04.003","article-title":"An individual tree crown delineation method based on multi-scale segmentation of imagery","volume":"70","author":"Jing","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","first-page":"1906","article-title":"Single tree segmentation in close-planting orchard using UAV digital image","volume":"47","author":"Xu","year":"2022","journal-title":"Geomat. Inf. Sci. Wuhan Univ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/825\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:05:56Z","timestamp":1760105156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/825"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,27]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050825"],"URL":"https:\/\/doi.org\/10.3390\/rs16050825","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,27]]}}}