{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T12:51:38Z","timestamp":1770555098584,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T00:00:00Z","timestamp":1630195200000},"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":["41871247"],"award-info":[{"award-number":["41871247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Technology Research and Development Program of Sichuan Province","award":["2020YFG0033"],"award-info":[{"award-number":["2020YFG0033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy\u2019s 3D structure information that is provided by point cloud data, and ensuring the method\u2019s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.<\/jats:p>","DOI":"10.3390\/rs13173428","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T21:59:45Z","timestamp":1630447185000},"page":"3428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Tree Extraction from Airborne Laser Scanning Data in Urban Areas"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7678-947X","authenticated-orcid":false,"given":"Hangkai","family":"You","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":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]},{"given":"Yifan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Ze","family":"He","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0232-8862","authenticated-orcid":false,"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710077, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.ufug.2012.06.006","article-title":"A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones","volume":"11","author":"Roy","year":"2012","journal-title":"Urban For. Urban Green."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/00049182.2018.1505285","article-title":"Seeing the trees for the (urban) forest: More-than-human geographies and urban greening","volume":"51","author":"Phillips","year":"2018","journal-title":"Aust. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"584","DOI":"10.3390\/rs5020584","article-title":"A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data","volume":"5","author":"Wu","year":"2013","journal-title":"Remote. Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1023\/B:UECO.0000004828.05143.67","article-title":"Rainfall interception by Santa Monica\u2019s municipal urban forest","volume":"6","author":"Xiao","year":"2002","journal-title":"Urban Ecosyst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.ufug.2017.05.011","article-title":"Mapping leaf area of urban greenery using aerial LiDAR and ground-based measurements in Gothenburg, Sweden","volume":"26","author":"Klingberg","year":"2017","journal-title":"Urban For. Urban Green."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2017). Remote Sensing of Above-Ground Biomass. Remote. Sens., 9.","DOI":"10.3390\/rs9090935"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1080\/01431161.2020.1820618","article-title":"Assessing of urban vegetation biomass in combination with LiDAR and high-resolution remote sensing images","volume":"42","author":"Zhang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/s41586-020-2824-5","article-title":"An unexpectedly large count of trees in the West African Sahara and Sahel","volume":"587","author":"Brandt","year":"2020","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1641\/0006-3568(2002)052[0019:LRSFES]2.0.CO;2","article-title":"Lidar Remote Sensing for Ecosystem Studies","volume":"52","author":"Lefsky","year":"2009","journal-title":"BioScience"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Faridhosseini, A. (2006). Using Airborne Lidar to Differentiate Cottonwood Trees in a Riparian Area and Refine Riparian Water Use Estimates, The University of Arizona.","DOI":"10.1093\/wjaf\/21.3.149"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Feng, X., and Li, P. (2019). A tree species mapping method from UAV images over urban area using similarity in tree-crown object histograms. Remote Sens., 11.","DOI":"10.3390\/rs11171982"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10815","DOI":"10.3390\/rs70810815","article-title":"The Extraction of Vegetation Points from LiDAR Using 3D Fractal Dimension Analyses","volume":"7","author":"Yang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.018","article-title":"Urban tree species mapping using hyperspectral and lidar data fusion","volume":"148","author":"Alonzo","year":"2014","journal-title":"Remote Sens Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40663-018-0146-y","article-title":"Mapping tree canopies in urban environments using airborne laser scanning (ALS): A Vancouver case study","volume":"5","author":"Matasci","year":"2018","journal-title":"For. Ecosyst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Man, Q., Dong, P., Yang, X., Wu, Q., and Han, R. (2020). Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12172725"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ufug.2016.06.026","article-title":"Assessing urban tree condition using airborne light detection and ranging","volume":"19","author":"Plowright","year":"2016","journal-title":"Urban For. Urban Green."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jiang, T., Liu, J., Li, X., and Liang, C. (2020). Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100595"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/S0924-2716(99)00011-8","article-title":"Airborne laser scanning\u2014an introduction and overview","volume":"54","author":"Wehr","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.1109\/JSTARS.2019.2929546","article-title":"Rapid urban roadside tree inventory using a mobile laser scanning system","volume":"12","author":"Chen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","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_21","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_22","first-page":"1","article-title":"Retrieval of Canopy Gap Fraction From Terrestrial Laser Scanning Data Based on the Monte Carlo Method","volume":"PP","author":"Xu","year":"2021","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_23","first-page":"64","article-title":"Automatic extraction of street trees\u2019 nonphotosynthetic components from MLS data","volume":"69","author":"Xu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","first-page":"375","article-title":"Detection and thinning of street trees for calculation of morphological parameters using mobile laser scanner data","volume":"13","author":"Husain","year":"2018","journal-title":"Remote. Sens. Appl. Soc. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1080\/2150704X.2015.1088668","article-title":"Deep learning-based tree classification using mobile LiDAR data","volume":"6","author":"Guan","year":"2015","journal-title":"Remote. Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"411","DOI":"10.5937\/gp23-24675","article-title":"A review of climatic and vegetation surveys in urban environment with laser scanning: A literature-based analysis","volume":"23","author":"Schlosser","year":"2019","journal-title":"Geogr. Pannonica"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2011.12.003","article-title":"Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data","volume":"67","author":"Hollaus","year":"2012","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1080\/01431160701736448","article-title":"Analysis of full waveform LiDAR data for the classification of deciduous and coniferous trees","volume":"29","author":"Reitberger","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lucas, C., Bouten, W., Koma, Z., Kissling, W., and Seijmonsbergen, A. (2019). Identification of linear vegetation elements in a rural landscape using LiDAR point clouds. Remote Sens., 11.","DOI":"10.3390\/rs11030292"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103660","DOI":"10.1016\/j.autcon.2021.103660","article-title":"Seed point set-based building roof extraction from airborne LiDAR point clouds using a top-down strategy","volume":"126","author":"Shao","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_31","first-page":"152","article-title":"Exploring full-waveform LiDAR parameters for tree species classification","volume":"13","author":"Heinzel","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yu, B., Liu, H., Zhang, L., and Wu, J. (2009, January 20\u201322). An object-based two-stage method for a detailed classification of urban landscape components by integrating airborne LiDAR and color infrared image data: A case study of downtown Houston. Proceedings of the 2009 Joint Urban Remote Sensing Event, Shanghai, China.","DOI":"10.1109\/URS.2009.5137543"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.isprsjprs.2018.08.010","article-title":"A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds","volume":"144","author":"Dai","year":"2018","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ma, Z., Pang, Y., Wang, D., Liang, X., Chen, B., Lu, H., Weinacker, H., and Koch, B. (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_35","doi-asserted-by":"crossref","first-page":"112307","DOI":"10.1016\/j.rse.2021.112307","article-title":"Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach","volume":"256","author":"Yun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_36","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."},{"key":"ref_37","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."},{"key":"ref_38","first-page":"98","article-title":"PTrees: A point-based approach to forest tree extraction from lidar data","volume":"33","author":"Vega","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","first-page":"145","article-title":"Improving the efficiency and accuracy of individual tree crown delineation from high-density LiDAR data","volume":"26","author":"Hu","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","first-page":"646","article-title":"Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: Application to a mountainous forest with heterogeneous stands","volume":"13","author":"Durrieu","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/36.921414","article-title":"A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners","volume":"39","author":"Hyyppa","year":"2001","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10310-007-0041-9","article-title":"Detection of individual trees and estimation of tree height using LiDAR data","volume":"12","author":"Kwak","year":"2007","journal-title":"J. For. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5011","DOI":"10.1109\/TGRS.2016.2543225","article-title":"International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TGRS.2003.810682","article-title":"A progressive morphological filter for removing nonground measurements from airborne LiDAR data","volume":"41","author":"Zhang","year":"2003","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_45","unstructured":"Sithole, G., and Vosselman, G. (2015, August 19). The Full Report: ISPRS Comparison of Existing Automatic Filters. Available online: http:\/\/www.itc.nl\/isprswgIII-3\/filtertest\/."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2017.04.027","article-title":"Airborne Laser Scanning for calibration and validation of inshore satellite altimetry: A proof of concept","volume":"197","author":"Zlinszky","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wu, J., Yao, W., Chi, W., and Zhao, X. (2011, January 26\u201329). Comprehensive quality evaluation of airborne lidar data. Proceedings of the SPIE 8286, International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications, Nanjing, China.","DOI":"10.1117\/12.912588"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, D., Wang, J., Scaioni, M., and Si, Q. (2019). Coarse-to-Fine Classification of Road Infrastructure Elements from Mobile Point Clouds Using Symmetric Ensemble Point Network and Euclidean Cluster Extraction. Sensors, 20.","DOI":"10.3390\/s20010225"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","unstructured":"He, Q., and Ma, M. (2012). WATER: Dataset of Airborne LiDAR Mission in the Zhangye-Yingke Flight Zone on Jun. 20 2008, A Big Earth Data Platform for Three Poles."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/S0924-2716(99)00008-8","article-title":"Processing of laser scanner data\u2014algorithms and applications","volume":"54","author":"Axelsson","year":"1999","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"112017","DOI":"10.1016\/j.rse.2020.112017","article-title":"Enhancing airborne LiDAR data for improved forest structure representation in shortwave transmission models","volume":"249","author":"Webster","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.foreco.2019.02.002","article-title":"The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration","volume":"438","author":"Almeida","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Koch, B., Kattenborn, T., Straub, C., and Vauhkonen, J. (2014). Segmentation of forest to tree objects. Forestry Applications of Airborne Laser Scanning, Springer.","DOI":"10.1007\/978-94-017-8663-8_5"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3428\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:54:53Z","timestamp":1760165693000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,29]]},"references-count":55,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173428"],"URL":"https:\/\/doi.org\/10.3390\/rs13173428","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,29]]}}}