{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T04:33:17Z","timestamp":1774153997136,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,4]],"date-time":"2018-07-04T00:00:00Z","timestamp":1530662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The present study introduces an advanced method for 3D segmentation of terrestrial laser scanning data into single tree clusters. It intentionally tackled difficult forest situations with dense and structured tree formations, which inventory practitioners are often faced with. The strongly interlocking tree crowns of different sizes and in different layers characterized the test conditions of close to nature forest plots. Volumetric 3D images of the plots were derived from the original point cloud data. A clustering method with automatically derived priors focused on the segmentation of these images by global optimization. Therefore, each image was segmented as a whole and partitioned into individual tree objects using a combination of state-of-the-art techniques. Multiple steps were combined in a workflow that included a morphological detection of the tree stems, the construction of a similarity graph from the image data, the computation of the eigenspectrum which was weighted with the tree stem priors and the final labelling of the transformed data points in a Markov Random Field framework. The detected trees were verified by number and position which allowed for comparison with other studies. Additionally, for a subset of the data, we provided a detailed verification of the three-dimensional extent of the complete trees. The detection rate by number and position was 97.40% for major trees with a stem diameter at breast height (DBH) \u2265 12 cm and 84.62% for regeneration trees with a DBH &lt; 12 cm. The three-dimensional extent of the detected trees resulted in an average producer\u2019s accuracy of 93.66% and a user\u2019s accuracy of 94.06%. Overall, these numbers confirm the capacity of the method for accurate segmentation of strongly layered and understory trees. Future studies could test the method on wider areas with large scale data and different forest types in order to determine its general transferability.<\/jats:p>","DOI":"10.3390\/rs10071056","type":"journal-article","created":{"date-parts":[[2018,7,4]],"date-time":"2018-07-04T12:23:02Z","timestamp":1530706982000},"page":"1056","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Constrained Spectral Clustering of Individual Trees in Dense Forest Using Terrestrial Laser Scanning Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Johannes","family":"Heinzel","sequence":"first","affiliation":[{"name":"Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Z\u00fcrcherstrasse 111, 8903 Birmensdorf, Switzerland"}]},{"given":"Markus O.","family":"Huber","sequence":"additional","affiliation":[{"name":"Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Z\u00fcrcherstrasse 111, 8903 Birmensdorf, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,4]]},"reference":[{"key":"ref_1","first-page":"90","article-title":"Final results of the vision expert system VES: Finding trees in aerial photographs","volume":"Volume 49","author":"Pinz","year":"1989","journal-title":"Wissensbasierte Mustererkennung"},{"key":"ref_2","first-page":"77","article-title":"3D segmentation of full waveform LIDAR data for single tree detection using normalized cut","volume":"37","author":"Reitberger","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","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_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"633","DOI":"10.5589\/m03-024","article-title":"Combined high-density lidar and multispectral imagery for individual tree crown analysis","volume":"29","author":"Leckie","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.3390\/f5051069","article-title":"Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description","volume":"5","author":"Hackenberg","year":"2014","journal-title":"Forests"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"491","DOI":"10.3390\/rs5020491","article-title":"Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data","volume":"5","author":"Raumonen","year":"2013","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2013.04.011","article-title":"3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar","volume":"82","author":"Hosoi","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"189","DOI":"10.5194\/isprsannals-II-3-W4-189-2015","article-title":"Massive-scale tree modelling from TLS data","volume":"II-3\/W4","author":"Raumonen","year":"2015","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cag.2017.04.004","article-title":"Efficient tree modeling from airborne LiDAR point clouds","volume":"67","author":"Hu","year":"2017","journal-title":"Comput. Graph."},{"key":"ref_12","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":"2014","journal-title":"Methods Ecol. Evol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"950","DOI":"10.3390\/rs4040950","article-title":"An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning","volume":"4","author":"Kaartinen","year":"2012","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3188\/szf.2017.0151","article-title":"Evaluation automatischer Einzelbaumerkennung aus luftgest\u00fctzten Laserscanning-Daten","volume":"168","author":"Menk","year":"2017","journal-title":"Schweizerische Zeitschrift fur Forstwesen"},{"key":"ref_15","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_16","unstructured":"Kersten, T.P. (2016, January 6\u20139). Einzelbaumdetektion in bewaldetem Gebiet auf Basis von luftgest\u00fctzten LiDAR-Daten. Proceedings of the Dreil\u00e4ndertagung der DGPF, der OVG und der SGPF, Bern, Schweiz."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chmielewski, L.J., Bator, M., Zasada, M., Stere\u0144czak, K., and Strzeli\u0144ski, P. (2010). Fuzzy Hough Transform-Based Methods for Extraction and Measurements of Single Trees in Large-Volume 3D Terrestrial LIDAR Data. Computer Vision and Graphics, Springer.","DOI":"10.1007\/978-3-642-15910-7_30"},{"key":"ref_18","first-page":"50","article-title":"Tree detection and diameter estimations by analysis of forest terrestrial laserscanner point clouds","volume":"XXXVI-3\/W52","author":"Bienert","year":"2007","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","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_20","first-page":"92","article-title":"Voxel space analysis of terrestrial laser scans in forests for wind field modelling","volume":"38","author":"Bienert","year":"2010","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.isprsjprs.2009.04.002","article-title":"3D segmentation of single trees exploiting full waveform LIDAR data","volume":"64","author":"Reitberger","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.rse.2013.07.044","article-title":"An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems","volume":"154","author":"Duncanson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","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_25","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_26","first-page":"307","article-title":"Detecting and measuring individual trees with laser scanning in mixed mountain forest of central Europe using an algorithm developed for Swedish boreal forest conditions","volume":"36","author":"Heurich","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","first-page":"W2","article-title":"Development of filtering, segmentation and modelling modules for lidar and multispectral data as a fundament of an automatic forest inventory system","volume":"36","author":"Weinacker","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3923","DOI":"10.3390\/f6113923","article-title":"Detecting stems in dense and homogeneous forest using single-scan TLS","volume":"6","author":"Xia","year":"2015","journal-title":"Forests"},{"key":"ref_29","first-page":"49","article-title":"Range image segmentation for tree detection in forest scans","volume":"II-5\/W2","author":"Bienert","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/JSTARS.2016.2565519","article-title":"Segmentation of Individual Trees From TLS and MLS Data","volume":"10","author":"Zhong","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1109\/LGRS.2017.2764938","article-title":"Tree Classification in Complex Forest Point Clouds Based on Deep Learning","volume":"14","author":"Zou","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","first-page":"520","article-title":"Detection and modelling of 3D trees from mobile laser scanning data","volume":"38","author":"Rutzinger","year":"2010","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1080\/01431161.2011.599349","article-title":"Three-level frame and RD-schematic algorithm for automatic detection of individual trees from MLS point clouds","volume":"33","author":"Lin","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","year":"2007","journal-title":"Stat. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1109\/JSTARS.2016.2569408","article-title":"Individual Tree Species Classification From Airborne Multisensor Imagery Using Robust PCA","volume":"9","author":"Lee","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/TIP.2013.2271865","article-title":"Multi-class constrained normalized cut with hard, soft, unary and pairwise priors and its applications to object segmentation","volume":"22","author":"Hu","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","unstructured":"Klein, D., Kamvar, S.D., and Manning, C.D. (2002, January 8\u201312). From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. Proceedings of the Nineteenth International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_40","unstructured":"Peluffo-Ord\u00f3\u00f1ez, D.H., Castro-Ospina, A.E., Chavez-Chamorro, D., Acosta-Medina, C.D., and Castellanos-Dom\u00ednguez, G. (2013, January 24\u201326). Normalized cuts clustering with prior knowledge and a pre-clustering stage. Proceedings of the European Symposium on Artificial Neural Networks, Bruges, Belgium."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1016\/j.rse.2010.03.019","article-title":"Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar","volume":"115","author":"Yao","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"526","DOI":"10.5589\/m08-070","article-title":"Sampling design of ground-based lidar measurements of forest canopy structure and its effect on shadowing","volume":"34","author":"Zande","year":"2008","journal-title":"Can. J. Remote Sens."},{"key":"ref_43","unstructured":"FARO Technologies Inc. (2014). FARO SCENE 5.1 Users Manual, FARO."},{"key":"ref_44","unstructured":"Ayachit, U. (2016). The ParaView Guide, Kitware Inc."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Heinzel, J., and Huber, M. (2017). Detecting Tree Stems from Volumetric TLS Data in Forest Environments with Rich Understory. Remote Sens., 9.","DOI":"10.3390\/rs9010009"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Soille, P. (2004). Morphological Image Analysis: Principles and Applications, Springer.","DOI":"10.1007\/978-3-662-05088-0"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/TPAMI.2007.1061","article-title":"Approximate Labeling via Graph Cuts Based on Linear Programming","volume":"29","author":"Komodakis","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","unstructured":"Kamvar, K., Sepandar, S., Klein, K., Dan, D., Manning, M., and Christopher, C. (2003, January 9\u201315). Spectral learning. Proceedings of the International Joint Conference of Artificial Intelligence, Acapulco, Mexico."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.cviu.2008.06.007","article-title":"Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies","volume":"112","author":"Komodakis","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"135","DOI":"10.2478\/aslh-2013-0011","article-title":"Mapping Forest Regeneration from Terrestrial Laser Scans","volume":"9","author":"Brolly","year":"2013","journal-title":"Acta Silv. Lign. Hung."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.3390\/s130201614","article-title":"Automatic Stem Mapping by Merging Several Terrestrial Laser Scans at the Feature and Decision Levels","volume":"13","author":"Liang","year":"2013","journal-title":"Sensors"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1056\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:11:14Z","timestamp":1760195474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,4]]},"references-count":52,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["rs10071056"],"URL":"https:\/\/doi.org\/10.3390\/rs10071056","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,4]]}}}