{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T19:03:19Z","timestamp":1776279799623,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"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":["31971578"],"award-info":[{"award-number":["31971578"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.<\/jats:p>","DOI":"10.3390\/rs14020298","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T22:03:13Z","timestamp":1641852193000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Performance and Sensitivity of Individual Tree Segmentation Methods for UAV-LiDAR in Multiple Forest Types"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5038-1313","authenticated-orcid":false,"given":"Kaisen","family":"Ma","sequence":"first","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]},{"given":"Zhenxiong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory and Monitoring, Central South Inventory and Planning Institute of National Forestry and Grassland Administration, Changsha 410004, China"}]},{"given":"Liyong","family":"Fu","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}]},{"given":"Wanli","family":"Tian","sequence":"additional","affiliation":[{"name":"Shanghai Huace Navigation Technology Ltd., Shanghai 201702, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1940-9952","authenticated-orcid":false,"given":"Fugen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]},{"given":"Jing","family":"Yi","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]},{"given":"Zhi","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory and Monitoring, Central South Inventory and Planning Institute of National Forestry and Grassland Administration, Changsha 410004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5401-6783","authenticated-orcid":false,"given":"Hua","family":"Sun","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","unstructured":"(2017). FAO Voluntary Guidelines on National Forest Monitoring, Food and Agriculture Organization of the United Nations."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s10712-019-09538-8","article-title":"The importance of consistent global forest aboveground biomass product validation","volume":"40","author":"Duncanson","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_3","first-page":"1","article-title":"Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data","volume":"53","author":"Zhao","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rex, F.E., Silva, C.A., Dalla Corte, A.P., Klauberg, C., Mohan, M., Cardil, A., Silva, V.S.D., Almeida, D.R.A.D., Garcia, M., and Broadbent, E.N. (2020). Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12091498"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s10712-019-09510-6","article-title":"The role and need for space-based Forest biomass-related measurements in environmental management and policy","volume":"40","author":"Herold","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2018.04.015","article-title":"Lidar supported estimators of wood volume and above ground biomass from the Danish national forest inventory (2012\u20132016)","volume":"211","author":"Magnussen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108","DOI":"10.3390\/rs9020108","article-title":"Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning","volume":"9","author":"Litkey","year":"2017","journal-title":"Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1080\/02827581.2016.1220617","article-title":"Valuation and production possibilities on a working forest using multi-objective programming, Woodstock, timber NPV, and carbon storage and sequestration","volume":"31","author":"Roise","year":"2016","journal-title":"Scand. J. Res."},{"key":"ref_10","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 Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2014","journal-title":"Int. J. Digit. Earth."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1177\/0309133312471367","article-title":"Optical remote sensing of forest leaf area index and biomass","volume":"37","author":"Song","year":"2013","journal-title":"Prog. Phys. Geog."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1038\/s41559-017-0194","article-title":"ISS observations offer insights into plant function","volume":"1","author":"Stavros","year":"2017","journal-title":"Nat. Ecol. Evol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Berninger, A., Lohberger, S., St\u00e4ngel, M., and Siegert, F. (2018). SAR-based estimation of above-ground biomass and its changes in tropical forests of Kalimantan using L-and C-Band. Remote Sens., 10.","DOI":"10.3390\/rs10060831"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/LGRS.2018.2819884","article-title":"Forest biomass retrieval from l-band sar using tomographic ground backscatter removal. IEEE Geo","volume":"15","author":"Blomberg","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1080\/01431161.2015.1030043","article-title":"Agent-based region growing for individual tree crown delineation from airborne laser scanning (ALS) data","volume":"36","author":"Zhen","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1016\/j.rse.2009.02.010","article-title":"Capturing tree crown formation through implicit surface reconstruction using airborne lidar data","volume":"113","author":"Kato","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1029\/2018EA000506","article-title":"The GEDI simulator: A large-footprint waveform lidar simulator for calibration and validation of spaceborne missions","volume":"6","author":"Hancock","year":"2018","journal-title":"Earth Space Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s10712-019-09529-9","article-title":"New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar","volume":"40","author":"Kellner","year":"2019","journal-title":"Surv. Geophy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1098\/rsfs.2017.0048","article-title":"Weighing trees with lasers: Advances, challenges and opportunities","volume":"8","author":"Disney","year":"2018","journal-title":"Interface Focus."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Almeida, A., Gon\u00e7alves, F., Silva, G., Mendon\u00e7a, A., Gonzaga, M., Silva, J., Souza, R., Milk, I., Neves, K., and Boeno, M. (2021). Individual Tree Detection and Qualitative Inventory of a Eucalyptus sp. Stand Using UAV Photogrammetry Data. Remote Sens., 13.","DOI":"10.3390\/rs13183655"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hu, X., Chen, W., and Xu, W. (2017). Adaptive Mean Shift-Based Identification ofIndividual Trees Using Airborne LiDAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9020148"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"MA, K., Xiong, Y., Jiang, F., Chen, S., and Sun, H. (2021). A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR. Remote Sens., 13.","DOI":"10.3390\/rs13081442"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"351","DOI":"10.14358\/PERS.70.3.351","article-title":"Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial Imagery","volume":"70","author":"Wang","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1080\/07038992.1995.10874622","article-title":"A Crown-Following Approach to the Automatic Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images","volume":"21","author":"Gougeon","year":"1995","journal-title":"Can. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/forestry\/cpr051","article-title":"Comparative testing of single-tree detection algorithms under different types of forest","volume":"85","author":"Vauhkonen","year":"2011","journal-title":"Forestry"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.isprsjprs.2017.09.006","article-title":"Graph SLAM correction for single scanner MLS forest data under boreal forest canopy","volume":"132","author":"Kukko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Reitberger, J., Krzystek, P., and Stilla, U. (2009, January 9\u201313). Benefit of airborne full waveform lidar for 3D segmentation and classification of single trees. Proceedings of the ASPRS 2009 Annual Conference, Baltimore, MD, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ku\u017eelka, K., Slav\u00edk, M., and Surov\u00fd, P. (2020). Very high density point clouds from UAV laser scanning for automatic tree stem detection and direct diameter measurement. Remote Sens., 12.","DOI":"10.3390\/rs12081236"},{"key":"ref_31","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_32","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_33","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1080\/07038992.2017.1252907","article-title":"Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds","volume":"43","author":"Ayrey","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2020.10.016","article-title":"Individual tree detection and crown delineation from Unmanned Aircraft System (UAS) LiDAR in structurally complex mixed species eucalypt forests","volume":"171","author":"Jaskierniak","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2018.12.034","article-title":"Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges","volume":"223","author":"Yin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1080\/22797254.2018.1474722","article-title":"Single-tree detection in high-density LiDAR data from UAV-based survey","volume":"51","author":"Balsi","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.rse.2015.11.008","article-title":"Bottom-up delineation of individual trees from full-waveform airborne laser scans in a structurally complex eucalypt forest","volume":"173","author":"Iurii","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Silva, V.S., Silva, C.A., Mohan, M., Cardil, A., Rex, F.E., Loureiro, G.H., Almeida, D.R.A.D., Broadbent, E.N., Gorgens, E.B., and Dalla Corte, A.P. (2020). Combined impact of sample size and modeling approaches for predicting stem volume in eucalyptus spp. forest plantations using field and LiDAR data. Remote Sens., 12.","DOI":"10.3390\/rs12091438"},{"key":"ref_39","unstructured":"RIEGL (2019). RIEGL VUX-1UAV Data Sheet, RIEGL Laser Measurement Systems GmbH."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2016.03.016","article-title":"Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas","volume":"117","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, H., Zhang, H., Sun, M., and Liu, X. (2016). A Point Cloud Filtering Approach to Generating DTMs for Steep Mountainous Areas and Adjacent Residential Areas. Remote Sens., 8.","DOI":"10.3390\/rs8010071"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, C., Ji, M., Wang, J., Wen, W., Li, T., and Sun, Y. (2019). An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation. Sensors., 19.","DOI":"10.3390\/s19010172"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.02.014","article-title":"An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy","volume":"92","author":"Hu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, W., Zheng, Q., Xiang, H., Chen, X., and Sakai, T. (2021). Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS\/PALSAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13020174"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Brede, B., Lau, A., Bartholomeus, H.M., and Kooistra, L. (2017). Comparing RIEGL RiCOPTER UAV LiDAR derived canopy height and DBH with terrestrial LiDAR. Sensors, 17.","DOI":"10.3390\/s17102371"},{"key":"ref_47","first-page":"1055","article-title":"An individual tree segmentation method based on watershed algorithm and 3D spatial distribution analysis from airborne LiDAR point clouds","volume":"13","author":"Yang","year":"2020","journal-title":"IEEE J.-STARS"},{"key":"ref_48","first-page":"925","article-title":"Detecting and measuring individual trees using an airborne laser scanner","volume":"68","author":"Persson","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.isprsjprs.2018.12.006","article-title":"Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data","volume":"148","author":"Cao","year":"2019","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5171","DOI":"10.1080\/01431161.2012.657363","article-title":"Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates","volume":"33","author":"Ene","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/0143116042000298289","article-title":"Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LiDAR and multi-spectral remotely sensed data","volume":"26","author":"Koukoulas","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mohan, M., Silva, C., Klauberg, C., Jat, P., Catts, G., Cardil, A., Hudak, A.T., and Dia, M. (2017). Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests, 8.","DOI":"10.3390\/f8090340"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Yang, Q., Su, Y., Jin, S., Kelly, M., Hu, T., Ma, Q., Li, Y., Song, S., Zhang, J., and Xu, G. (2019). The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11232880"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Japkowicz, N., and Szpakowicz, S. (2006). Beyond accuracy, Fscore and ROC: A family of discriminant measures for performance evaluation. AI 2006: Advances in Artificial Intelligence, Springer.","DOI":"10.1007\/11941439_114"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1139\/X10-223","article-title":"Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands","volume":"41","author":"Peuhkurinen","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_57","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_58","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_59","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1111\/2041-210X.12071","article-title":"Measuring tree height: A quantitative comparison of two common field methods in a moist tropical forest","volume":"4","author":"Larjavaara","year":"2013","journal-title":"Methods Ecol. Evol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1890\/11-2059.1","article-title":"Quantifying the sampling error in tree census measurements by volunteers and its effect on carbon stock estimates","volume":"23","author":"Butt","year":"2013","journal-title":"Ecol. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Krause, S., Sanders, T.G.M., Mun, J.P., and Greve, K. (2019). UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring. Remote Sens., 11.","DOI":"10.3390\/rs11070758"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:11Z","timestamp":1760362031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,10]]},"references-count":61,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020298"],"URL":"https:\/\/doi.org\/10.3390\/rs14020298","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,10]]}}}