{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:17:18Z","timestamp":1769833038357,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T00:00:00Z","timestamp":1559001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012359","name":"Douglas Bomford Trust","doi-asserted-by":"publisher","award":["tba"],"award-info":[{"award-number":["tba"]}],"id":[{"id":"10.13039\/100012359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements.<\/jats:p>","DOI":"10.3390\/rs11111263","type":"journal-article","created":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T11:18:09Z","timestamp":1559042289000},"page":"1263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4922-8228","authenticated-orcid":false,"given":"Wen","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5018-8820","authenticated-orcid":false,"given":"Aleksandra","family":"Zaforemska","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2763-4666","authenticated-orcid":false,"given":"Magdalena","family":"Smigaj","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2552-8253","authenticated-orcid":false,"given":"Yunsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02431 Masala, Finland"},{"name":"Centre of Excellence in Laser Scanning Research, Academy of Finland, 00531 Helsinki, Finland"}]},{"given":"Rachel","family":"Gaulton","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,28]]},"reference":[{"key":"ref_1","unstructured":"Thompson, I., Mackey, B., McNulty, S., and Mosseler, A. (2009). Forest Resilience, Biodiversity, and Climate Change, Secretariat of the Convention on Biological Diversity."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Franklin, S.E. (2001). Remote Sensing for Sustainable Forest Management, CRC Press.","DOI":"10.1201\/9781420032857"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1191\/0309133303pp360ra","article-title":"LiDAR remote sensing of forest structure","volume":"27","author":"Lim","year":"2003","journal-title":"Prog. Phys. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2016.10.018","article-title":"Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping","volume":"187","author":"Qi","year":"2016","journal-title":"Remote Sens. Environ."},{"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":"7619","DOI":"10.1109\/TGRS.2014.2315649","article-title":"Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data","volume":"52","author":"Wallace","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2018.04.019","article-title":"In-situ measurements from mobile platforms: An emerging approach to address the old challenges associated with forest inventories","volume":"143","author":"Liang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2016.01.006","article-title":"Terrestrial laser scanning in forest inventories","volume":"115","author":"Liang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","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":"3","author":"Wehr","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0034-4257(03)00139-1","article-title":"Characterizing vertical forest structure using small-footprint airborne LiDAR","volume":"3","author":"Zimble","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"1367","article-title":"Predicting forest stand characteristics with airborne scanning lidar","volume":"66","author":"Means","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhen, Z., Quackenbush, L., and Zhang, L. (2016). Trends in automatic individual tree crown detection and delineation\u2014Evolution of LiDAR data. Remote Sens., 8.","DOI":"10.3390\/rs8040333"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.3390\/f6051721","article-title":"A benchmark of LiDAR-based single tree detection methods using heterogeneous forest data from the Alpine space","volume":"6","author":"Eysn","year":"2015","journal-title":"Forests"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4163","DOI":"10.3390\/rs5094163","article-title":"Delineating individual trees from LiDAR data: A comparison of vector-and raster-based segmentation approaches","volume":"5","author":"Jakubowski","year":"2013","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bruggisser, M., Hollaus, M., Wang, D., and Pfeifer, N. (2019). Adaptive Framework for the Delineation of Homogeneous Forest Areas Based on LiDAR Points. Remote Sens., 11.","DOI":"10.3390\/rs11020189"},{"key":"ref_20","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_21","first-page":"532","article-title":"A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data","volume":"52","author":"Hamraz","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2015.01.018","article-title":"A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data","volume":"104","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.isprsjprs.2015.08.004","article-title":"An efficient approach to 3D single tree-crown delineation in LiDAR data","volume":"108","author":"Mongus","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"3467","DOI":"10.1109\/JSTARS.2016.2541780","article-title":"Individual Tree Crown Modeling and Change Detection From Airborne Lidar Data","volume":"9","author":"Xiao","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The estimation of the gradient of a density function, with applications in pattern recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/34.400568","article-title":"Mean shift, mode seeking, and clustering","volume":"17","author":"Cheng","year":"1995","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Comaniciu, D., Ramesh, V., and Meer, P. (2000, January 15). Real-time tracking of non-rigid objects using mean shift. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1109\/TGRS.2008.2002577","article-title":"An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery","volume":"46","author":"Huang","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/TGRS.2014.2330857","article-title":"Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images","volume":"53","author":"Michel","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1080\/01431160802395193","article-title":"Mean shift-based clustering analysis of multispectral remote sensing imagery","volume":"30","author":"Bo","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Maschler, J., Atzberger, C., and Immitzer, M. (2018). Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data. Remote Sens., 10.","DOI":"10.3390\/rs10081218"},{"key":"ref_37","first-page":"159","article-title":"Non-parametric segmentation of ALS point clouds using mean shift","volume":"1","author":"Melzer","year":"2007","journal-title":"J. Appl. Geod. Jag"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yao, W., Hinz, S., and Stilla, U. (2009, January 20\u201322). Object extraction based on 3d-segmentation of lidar data by combining mean shift with normalized cuts: Two examples from urban areas. Proceedings of the 2009 Joint Urban Remote Sensing Event, Shanghai, China.","DOI":"10.1109\/URS.2009.5137673"},{"key":"ref_39","unstructured":"Lee, I.-C., Wu, B., and Li, R. (2009, January 9\u201313). Shoreline extraction from the integration of lidar point cloud data and aerial orthophotos using mean-shift segmentation. Proceedings of the 2009 ASPRS Annual Conference, Baltimore, MD, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ferraz, A., Bretar, F., Jacquemoud, S., Gon\u00e7alves, G., and Pereira, L. (2010, January 26\u201329). 3D segmentation of forest structure using a mean-shift based algorithm. Proceedings of the 2010 IEEE International Conference on Image Processing, Hong Kong, China.","DOI":"10.1109\/ICIP.2010.5651310"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"349","DOI":"10.5194\/isprsannals-II-5-W2-349-2013","article-title":"Enhanced detection of 3D individual trees in forested areas using airborne full-waveform LiDAR data by combining normalized cuts with spatial density clustering","volume":"1","author":"Yao","year":"2013","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_42","first-page":"252","article-title":"Estimation of regeneration coverage in a temperate forest by 3D segmentation using airborne laser scanning data","volume":"52","author":"Amiri","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, X., Chen, W., and Xu, W. (2017). Adaptive Mean Shift-Based Identification of Individual Trees Using Airborne LiDAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9020148"},{"key":"ref_44","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_45","unstructured":"Bechtold, S., and H\u00f6fle, B. (2016). HELIOS: A multi-purpose lidar simulation framework for research, planning and training of laser scanning operations with airborne, ground-based mobile and stationary platforms. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 3."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.foreco.2018.12.005","article-title":"Combined use of spectral and structural characteristics for improved red band needle blight detection in pine plantation stands","volume":"434","author":"Smigaj","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_47","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_48","unstructured":"Parkan, M. (2019, March 20). Digital Forestry Toolbox for Matlab\/Octave. Available online: http:\/\/mparkan.github.io\/Digital-Forestry-Toolbox\/."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/2041-210X.12575","article-title":"Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data","volume":"7","author":"Dalponte","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2018.11.008","article-title":"Is field-measured tree height as reliable as believed\u2014A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest","volume":"147","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1263\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:07Z","timestamp":1760187247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,28]]},"references-count":50,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11111263"],"URL":"https:\/\/doi.org\/10.3390\/rs11111263","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,28]]}}}