{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T11:56:38Z","timestamp":1773748598350,"version":"3.50.1"},"reference-count":115,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004569","name":"Ministerstwo Nauki i Szkolnictwa Wy\u017cszego","doi-asserted-by":"publisher","award":["DWD\/3\/19\/2019"],"award-info":[{"award-number":["DWD\/3\/19\/2019"]}],"id":[{"id":"10.13039\/501100004569","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allowing for increased performance of object extraction and classification approaches. As many publications have shown, multiple LiDAR-derived metrics, with the assistance of classification algorithms, contribute to the high accuracy of tree species discrimination based on data obtained by laser scanning. The aim of this article is to review studies in the species classification literature which used data collected by Airborne Laser Scanning. We analyzed these studies to figure out the most efficient group of LiDAR-derived features in species discrimination. We also identified the most powerful classification algorithm, which maximizes the advantages of the derived metrics to increase species discrimination performance. We conclude that features extracted from full-waveform data lead to the highest overall accuracy. Radiometric features with height information are also promising, generating high species classification accuracies. Using random forest and support vector machine as classifiers gave the best species discrimination results in the reviewed publications.<\/jats:p>","DOI":"10.3390\/rs13030353","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T00:53:41Z","timestamp":1611190421000},"page":"353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":138,"title":["A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5321-7946","authenticated-orcid":false,"given":"Maja","family":"Micha\u0142owska","sequence":"first","affiliation":[{"name":"Institute of Geodesy and Civil Engineering, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-724 Olsztyn, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8954-7963","authenticated-orcid":false,"given":"Jacek","family":"Rapi\u0144ski","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Civil Engineering, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-724 Olsztyn, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote sensing technologies for enhancing forest inventories: A review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kangas, A., and Maltamo, M. (2006). Forest Inventory: Methodology and Applications, Springer Science & Business Media.","DOI":"10.1007\/1-4020-4381-3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral\/hyperspectral images and LiDAR data","volume":"123","author":"Dalponte","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"101","article-title":"Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation","volume":"18","author":"Heinzel","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"\u00d8rka, H., Dalponte, M., Gobakken, T., N\u00e6sset, E., and Ene, L. (2013). Characterizing forest species composition using multiple remote sensing data sources and inventory approaches. Scand. J. For. Res., 28.","DOI":"10.1080\/02827581.2013.793386"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/0034-4257(88)90007-7","article-title":"Remote sensing of forest canopy and leaf biochemical contents","volume":"24","author":"Peterson","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/0034-4257(94)90146-5","article-title":"Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties","volume":"48","author":"Baret","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1890\/070152","article-title":"Airborne spectranomics: Mapping canopy chemical and taxonomic diversity in tropical forests","volume":"7","author":"Asner","year":"2009","journal-title":"Front. Ecol. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1890\/120111","article-title":"Observing changing ecological diversity in the Anthropocene","volume":"11","author":"Schimel","year":"2013","journal-title":"Front. Ecol. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Baldeck, C.A., Asner, G.P., Martin, R.E., Anderson, C.B., Knapp, D.E., Kellner, J.R., and Wright, S.J. (2015). Operational tree species mapping in a diverse tropical forest with airborne imaging spectroscopy. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118403"},{"key":"ref_11","first-page":"733","article-title":"A review of methods for mapping and prediction of inventory attributes for operational forest management","volume":"60","author":"Brosofske","year":"2014","journal-title":"For. Sci."},{"key":"ref_12","first-page":"460","article-title":"Forestry applications of airborne laser scanning","volume":"27","author":"Maltamo","year":"2014","journal-title":"Concepts Case Stud. Manag. Ecosys"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3733\/ca.v069n01p14","article-title":"Mapping forests with Lidar provides flexible, accurate data with many uses","volume":"69","author":"Kelly","year":"2015","journal-title":"Calif. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Krzystek, P., Serebryanyk, A., Schn\u00f6rr, C., \u010cervenka, J., and Heurich, M. (2020). Large-Scale Mapping of Tree Species and Dead Trees in \u0160umava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12040661"},{"key":"ref_15","unstructured":"Bachman, C.G. (1979). Laser Radar Systems and Techniques, Artech House."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Pritchard, D., Sperner, J., Hoepner, S., and Tenschert, R. (2017). Terrestrial laser scanning for heritage conservation: The Cologne Cathedral documentation project. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 4.","DOI":"10.5194\/isprs-annals-IV-2-W2-213-2017"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-\u00c1lvarez, R., de Luis, J., Pereda-Garc\u00eda, R., Fern\u00e1ndez-Maroto, G., and Malag\u00f3n-Pic\u00f3n, B. (2020). 3D Documentation with TLS of Caliphal Gate (Ceuta, Spain). Appl. Sci., 10.","DOI":"10.3390\/app10155377"},{"key":"ref_19","first-page":"451","article-title":"Generating building outlines from terrestrial laser scanning","volume":"5","author":"Pu","year":"2008","journal-title":"ISPRS08 B"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nowak, R., Or\u0142owicz, R., and Rutkowski, R. (2020). Use of TLS (LiDAR) for building diagnostics with the example of a historic building in Karlino. Buildings, 10.","DOI":"10.3390\/buildings10020024"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Suchocki, C., Dami\u0119cka-Suchocka, M., Katzer, J., Janicka, J., Rapinski, J., and Sta\u0142owska, P. (2020). Remote Detection of Moisture and Bio-Deterioration of Building Walls by Time-Of-Flight and Phase-Shift Terrestrial Laser Scanners. Remote Sens., 12.","DOI":"10.3390\/rs12111708"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1080\/19479832.2016.1188860","article-title":"Use of mobile LiDAR in road information inventory: A review","volume":"7","author":"Guan","year":"2016","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Truong-Hong, L., and Laefer, D.F. (2015, January 23\u201325). Documentation of bridges by terrestrial laser scanner. Proceedings of the IABSE Geneva Conference 2015, Geneva, Switzerland.","DOI":"10.2749\/222137815818358691"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Artese, S., and Zinno, R. (2020). TLS for Dynamic Measurement of the Elastic Line of Bridges. Appl. Sci., 10.","DOI":"10.3390\/app10031182"},{"key":"ref_25","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_26","first-page":"162","article-title":"Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest","volume":"42","author":"Latifi","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.1109\/TGRS.2018.2885057","article-title":"Multispectral airborne LiDAR data in the prediction of boreal tree species composition","volume":"57","author":"Kukkonen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Prieto, I., Izkara, J.L., and Usobiaga, E. (2019). The application of lidar data for the solar potential analysis based on urban 3D model. Remote Sens., 11.","DOI":"10.3390\/rs11202348"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5147","DOI":"10.1080\/01431161.2020.1727053","article-title":"A robust segmentation framework for closely packed buildings from airborne LiDAR point clouds","volume":"41","author":"Wang","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Heritage, G., and Large, A. (2009). Laser Scanning for the Environmental Sciences, John Wiley & Sons.","DOI":"10.1002\/9781444311952"},{"key":"ref_31","first-page":"218","article-title":"Reconstructing tree crowns from laser scanner data for feature extraction","volume":"34","author":"Pyysalo","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.14358\/PERS.72.12.1369","article-title":"Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest","volume":"72","author":"Solberg","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"118695","DOI":"10.1016\/j.foreco.2020.118695","article-title":"Airborne lidar provides reliable estimates of canopy base height and canopy bulk density in southwestern ponderosa pine forests","volume":"481","author":"Chamberlain","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yu, X., Hyypp\u00e4, J., Litkey, P., Kaartinen, H., Vastaranta, M., and Holopainen, M. (2017). Single-sensor solution to tree species classification using multispectral airborne laser scanning. Remote Sens., 9.","DOI":"10.3390\/rs9020108"},{"key":"ref_35","first-page":"31","article-title":"Feature Relevance Assessment of Multispectral Airborne LiDAR Data for Tree Species Classification","volume":"XLII-3","author":"Heurich","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_36","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. Geoinf."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/S0034-4257(97)00041-2","article-title":"Estimating timber volume of forest stands using airborne laser scanner data","volume":"61","author":"Naesset","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1016\/j.rse.2010.01.024","article-title":"Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning","volume":"114","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"117856","DOI":"10.1016\/j.foreco.2019.117856","article-title":"Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicting timber volume in mixed and heterogeneously structured forests","volume":"459","author":"Bont","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"653","DOI":"10.5589\/m12-007","article-title":"Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems","volume":"37","author":"Sarrazin","year":"2011","journal-title":"Can. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Korpela, I., \u00d8rka, H., Maltamo, M., Tokola, T., Hyypp\u00e4, J., Tokola, M., and Maltamo, T. (2010). Tree Species Classification Using Airborne LiDAR\u2014Effects of Stand and Tree Parameters, Downsizing of Training Set, Intensity Normalization, and Sensor Type. Silva Fenn., 44.","DOI":"10.14214\/sf.156"},{"key":"ref_44","unstructured":"Culvenor, D. (1998). A Spatial Clustering Approach to Automated Tree Crown Delineation, Pacific Forestry Centre."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3020","DOI":"10.3390\/s8053020","article-title":"Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach","volume":"8","author":"Voss","year":"2008","journal-title":"Sensors"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.3390\/rs2061481","article-title":"Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes","volume":"2","author":"Yu","year":"2010","journal-title":"Remote Sens."},{"key":"ref_48","unstructured":"Yao, W. (2012). A Sensitivity Analysis for a Novel Individual Tree Segmentation Algorithm Using 3D Lidar Point Cloud Data, Silvilaser."},{"key":"ref_49","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_50","first-page":"996","article-title":"The importance of understanding error in lidar digital elevation models","volume":"35","author":"Smith","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_51","first-page":"2005","article-title":"A full GIS-based workflow for tree identification and tree crown delineation using laser scanning","volume":"5","author":"Tiede","year":"2005","journal-title":"ISPRS Workshop CMRT"},{"key":"ref_52","first-page":"436","article-title":"Hierarchical watershed segmentation of canopy height model for multi-scale forest inventory","volume":"442","author":"Zhao","year":"2007","journal-title":"Proc. ISPRS Work. Group"},{"key":"ref_53","unstructured":"Zhong, L., Cheng, L., Xu, H., Wu, Y., Chen, Y., and Li, M. (2016). Segmentation of Individual Trees From TLS and MLS Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 1\u201314."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1080\/2150704X.2018.1444286","article-title":"A supervoxel approach to the segmentation of individual trees from LiDAR point clouds","volume":"9","author":"Xu","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Xu, Y., Sun, Z., Hoegner, L., Stilla, U., and Yao, W. (2018, January 19\u201320). Instance Segmentation of Trees in Urban Areas from MLS Point Clouds Using Supervoxel Contexts and Graph-Based Optimization. Proceedings of the 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Beijing, China.","DOI":"10.1109\/PRRS.2018.8486220"},{"key":"ref_56","first-page":"1","article-title":"Assessment of various parameters on 3D semantic object-based point cloud labelling on urban LiDAR dataset","volume":"34","author":"Anandakumar","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_57","first-page":"100242","article-title":"Individual tree detection from airborne laser scanning data based on supervoxels and local convexity","volume":"15","author":"Anandakumar","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1016\/j.rse.2009.02.002","article-title":"Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data","volume":"113","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Shi, Y., Skidmore, A., Holzwarth, S., Heiden, U., Pinnel, N., Zhu, X., and Heurich, M. (2018). Tree species classification using plant functional traits from LiDAR and hyperspectral data. Int. J. Appl. Earth Obs. Geoinf., 73.","DOI":"10.1016\/j.jag.2018.06.018"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rse.2015.08.019","article-title":"LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters","volume":"173","author":"Hovi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ba, A., Laslier, M., Dufour, S., and Hubert-Moy, L. (2019). Riparian Trees Genera Identification Based on Leaf-on\/Leaf-off Airborne Laser Acanner Data and Machine Learning Classifiers in Northern France. Int. J. Remote Sens.","DOI":"10.1080\/01431161.2019.1674457"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1080\/01431160701736471","article-title":"Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images","volume":"29","author":"Holmgren","year":"2008","journal-title":"Int. J. Remote Sens. Int. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.rse.2013.09.006","article-title":"Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data","volume":"140","author":"Dalponte","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2017.08.010","article-title":"Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data","volume":"200","author":"Liu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"68716","DOI":"10.1109\/ACCESS.2018.2880083","article-title":"Deep Learning for Fusion of APEX Hyperspectral and Full-waveform LiDAR Remote Sensing Data for Tree Species Mapping","volume":"6","author":"Liao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_66","first-page":"1","article-title":"Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors","volume":"2019","author":"Yang","year":"2019","journal-title":"J. Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Li, Q., Wong, F., and Fung, T. (2019). Classification of Mangrove Species Using Combined WordView-3 and LiDAR Data in Mai Po Nature Reserve, Hong Kong. Remote Sens., 11.","DOI":"10.3390\/rs11182114"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Shi, Y., Skidmore, A., and Heurich, M. (2019). Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs. Int. J. Appl. Earth Obs. Geoinf., 84.","DOI":"10.1016\/j.jag.2019.101970"},{"key":"ref_69","first-page":"37","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Psychol. Bull."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement For Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wang, K., and Liu, X. (2018). A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment. Forests, 10.","DOI":"10.3390\/f10010001"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.14358\/PERS.78.10.1079","article-title":"Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery","volume":"78","author":"Zhang","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Koenig, K., and H\u00f6fle, B. (2016). Full-Waveform Airborne Laser Scanning in Vegetation Studies\u2014A Review of Point Cloud and Waveform Features for Tree Species Classification. Forests, 7.","DOI":"10.3390\/f7090198"},{"key":"ref_74","first-page":"300","article-title":"Utilizing Airborne Laser Intensity for Tree Species Classification","volume":"36","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/S0034-4257(03)00140-8","article-title":"Identifying species of individual trees using airborne laser scanner","volume":"90","author":"Holmgren","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Axelsson, A., Lindberg, E., and Olsson, H. (2018). Exploring Multispectral ALS Data for Tree Species Classification. Remote Sens., 10.","DOI":"10.3390\/rs10020183"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.agrformet.2012.11.012","article-title":"Classification of tree species based on structural features derived from high density LiDAR data","volume":"171\u2013172","author":"Li","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1109\/TGRS.2017.2656152","article-title":"An Internal Crown Geometric Model for Conifer Species Classification With High-Density LiDAR Data","volume":"55","author":"Harikumar","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_79","first-page":"45","article-title":"A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification","volume":"46","author":"Lin","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.isprsjprs.2009.07.001","article-title":"Tree species identification in mixed coniferous forest using airborne laser scanning","volume":"64","author":"Suratno","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens. ISPRS Photogramm."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.3390\/f5051011","article-title":"Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification","volume":"5","author":"Yu","year":"2014","journal-title":"Forests"},{"key":"ref_82","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_83","first-page":"117","article-title":"Forest Species Classification Based on Three-dimensional Coordinate and Intensity Information of Airborne LiDAR Data with Random Forest Method","volume":"XLII-3\/W10","author":"You","year":"2020","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_84","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_85","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1016\/j.rse.2009.03.017","article-title":"Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data","volume":"113","author":"Kim","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.isprsjprs.2018.02.002","article-title":"Important LiDAR metrics for discriminating forest tree species in Central Europe","volume":"137","author":"Shi","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_87","first-page":"1","article-title":"Support vector machines for tree species identification using LiDAR-derived structure and intensity variables","volume":"28","author":"Zhang","year":"2012","journal-title":"Geocarto Int."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Budei, B., St-Onge, B., Hopkinson, C., and Audet, F.A. (2017). Identifying the genus or species of individual trees using a three-wavelength airborne lidar system. Remote Sens. Environ., 204.","DOI":"10.1016\/j.rse.2017.09.037"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Pirotti, F. (2011). Analysis of full-waveform LiDAR data for forestry applications: A review of investigations and methods. Iforest Biogeosci. For., 100\u2013106.","DOI":"10.3832\/ifor0562-004"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.isprsjprs.2005.12.001","article-title":"Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner","volume":"60","author":"Wagner","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_91","first-page":"39","article-title":"Tree species classification in subtropical forests using small-footprint full-waveform LiDAR data","volume":"49","author":"Cao","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0034-4257(01)00281-4","article-title":"Estimation of tropical forest structural characteristics using large-footprint LiDAR","volume":"79","author":"Drake","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_93","unstructured":"Duong, V. (2010). Processing and Application of ICESat Large Footprint Full Waveform Laser Range Data. [Ph.D. Thesis, Delft University of Technology]."},{"key":"ref_94","first-page":"14","article-title":"Tree species classification based on full-waveform airborne laser scanning data","volume":"54","author":"Hollaus","year":"2009","journal-title":"SilviLaser"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Zhou, T., Popescu, S., Lawing, A.M., Eriksson, M., Strimbu, B., and Burkner, P. (2018). Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures. Remote Sens., 10.","DOI":"10.3390\/rs10010039"},{"key":"ref_96","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_97","doi-asserted-by":"crossref","unstructured":"Wasser, L., Day, R., Chasmer, L., and Taylor, A. (2013). Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested Riparian Buffers During Leaf-Off and Leaf-On Conditions. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0054776"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Sumnall, M., Hill, R., and Hinsley, S. (2015). Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables. Remote Sens. Environ., 173.","DOI":"10.1016\/j.rse.2015.07.027"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.isprsjprs.2017.08.013","article-title":"Tree species classification using within crown localization of waveform LiDAR attributes","volume":"133","author":"Blomley","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1016\/j.rse.2010.01.016","article-title":"Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics","volume":"114","author":"Vauhkonen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Ballanti, L., Blesius, L., Hines, E., and Kruse, B. (2016). Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8060445"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1016\/j.rse.2010.07.002","article-title":"Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada","volume":"114","author":"Jones","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"377","DOI":"10.3390\/rs4020377","article-title":"Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar","volume":"4","author":"Vaughn","year":"2012","journal-title":"Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Demir, B., and Dalponte, M. (August, January 28). Weighted Support Vector Machines for Tree Species Classification Using Lidar Data. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900398"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn. Vol."},{"key":"ref_106","first-page":"273","article-title":"Support-vector networks","volume":"297","author":"Cortes","year":"2009","journal-title":"Chem. Biol. Drug Des."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_108","unstructured":"Polewski, P.P. (2017). Reconstruction of Standing and Fallen Single Dead Trees in Forested Areas from LiDAR Data and Aerial Imagery. [Ph.D. Thesis, Technische Universit\u00e4t M\u00fcnchen]."},{"key":"ref_109","first-page":"41","article-title":"Free Shape Context descriptors optimized with genetic algorithm for the detection of dead tree trunks in ALS point clouds","volume":"W5","author":"Polewski","year":"2015","journal-title":"ISPRS Geospat. Week"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s11676-017-0441-4","article-title":"Analysis of forest structural complexity using airborne LiDAR data and aerial photography in a mixed conifer\u2013broadleaf forest in northern Japan","volume":"29","author":"Jayathunga","year":"2018","journal-title":"J. For. Res."},{"key":"ref_111","first-page":"69","article-title":"Estimating over-and understorey canopy density of temperate mixed stands by airborne LiDAR data","volume":"89","author":"Latifi","year":"2016","journal-title":"For. Int. J. For. Res."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Zielewska-B\u00fcttner, K., Heurich, M., M\u00fcller, J., and Braunisch, V. (2018). Remotely sensed single tree data enable the determination of habitat thresholds for the three-toed woodpecker (Picoides tridactylus). Remote Sens., 10.","DOI":"10.3390\/rs10121972"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/LGRS.2012.2232278","article-title":"Classification of Spruce and Pine Trees Using Active Hyperspectral LiDAR","volume":"10","author":"Vauhkonen","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2017.04.025","article-title":"Retrieval of higher order statistical moments from full-waveform LiDAR data for tree species classification","volume":"196","author":"Bruggisser","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1080\/01431161.2010.523021","article-title":"Fourier transformation of waveform Lidar for species recognition","volume":"2","author":"Vaughn","year":"2011","journal-title":"Remote Sens. 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