{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:36:24Z","timestamp":1778200584199,"version":"3.51.4"},"reference-count":134,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002790","name":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["CRDPJ-462973-14"],"award-info":[{"award-number":["CRDPJ-462973-14"]}],"id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models \u221212.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.<\/jats:p>","DOI":"10.3390\/rs13142796","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T10:52:58Z","timestamp":1626432778000},"page":"2796","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6142-9009","authenticated-orcid":false,"given":"Bastien","family":"Vandendaele","sequence":"first","affiliation":[{"name":"Department of Applied Geomatics, Centre d\u2019Applications et de Recherches en T\u00e9l\u00e9d\u00e9tection (CARTEL), Universit\u00e9 de Sherbrooke, 2500 Boulevard de l\u2019Universit\u00e9, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard A.","family":"Fournier","sequence":"additional","affiliation":[{"name":"Department of Applied Geomatics, Centre d\u2019Applications et de Recherches en T\u00e9l\u00e9d\u00e9tection (CARTEL), Universit\u00e9 de Sherbrooke, 2500 Boulevard de l\u2019Universit\u00e9, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Udayalakshmi","family":"Vepakomma","sequence":"additional","affiliation":[{"name":"FPInnovations, 570 Boulevard Saint-Jean, Pointe-Claire, QC H9R 3J9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaetan","family":"Pelletier","sequence":"additional","affiliation":[{"name":"Northern Hardwoods Research Institute Inc., 165 Boulevard H\u00e9bert, Edmundston, NB E3V 2S8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9987-9673","authenticated-orcid":false,"given":"Philippe","family":"Lejeune","sequence":"additional","affiliation":[{"name":"TERRA Teaching and Research Centre (Forest Is Life), Gembloux Agro-Bio Tech, University of Liege, Passage des D\u00e9port\u00e9s 2, 5030 Gembloux, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Martin-Ducup","sequence":"additional","affiliation":[{"name":"AMAP, IRD, CNRS, CIRAD, INRA, University Montpellier, botAnique et Mod\u00e9lisation de l\u2019Architecture, des Plantes et des V\u00e9g\u00e9tations, TA A51\/PS2, CEDEX 05, 34398 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1023\/A:1018922728855","article-title":"Forest Planning at the Tactical Level","volume":"95","author":"Church","year":"2000","journal-title":"Ann. Oper. Res."},{"key":"ref_2","unstructured":"Andersson, D. (2019, February 12). Approaches to Integrated Strategic\/Tactical Forest Planning. Available online: https:\/\/pub.epsilon.slu.se\/928\/."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Luoma, V., Saarinen, N., Wulder, M.A., White, J.C., Vastaranta, M., Holopainen, M., and Hyypp\u00e4, J. (2017). Assessing Precision in Conventional Field Measurements of Individual Tree Attributes. Forests, 8.","DOI":"10.3390\/f8020038"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1093\/forestry\/cpu018","article-title":"Suitability of Close-to-Nature Silviculture for Adapting Temperate European Forests to Climate Change","volume":"87","author":"Brang","year":"2014","journal-title":"Forestry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1093\/forestry\/cpr053","article-title":"Twenty-First Century Forestry: Integrating Ecologically Based, Uneven-Aged Silviculture with Increased Demands on Forests","volume":"84","author":"Diaci","year":"2011","journal-title":"Forestry"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bana\u015b, J., Zi\u0229ba, S., and Bujoczek, L. (2018). An Example of Uneven-Aged Forest Management for Sustainable Timber Harvesting. Sustainability, 10.","DOI":"10.3390\/su10093305"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1002\/ece3.3737","article-title":"Comparing the Effects of Even- and Uneven-Aged Silviculture on Ecological Diversity and Processes: A Review","volume":"8","author":"Nolet","year":"2018","journal-title":"Ecol. Evol."},{"key":"ref_8","unstructured":"Leak, W.B., Yamasaki, M., and Holleran, R. (2020, June 20). Silvicultural Guide for Northern Hardwoods in the Northeast, Available online: https:\/\/www.nrs.fs.fed.us\/pubs\/45874."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1080\/02827580310019257","article-title":"Practical Large-Scale Forest Stand Inventory Using a Small-Footprint Airborne Scanning Laser","volume":"19","year":"2004","journal-title":"Scand. J. For. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2014.10.004","article-title":"Generalizing Predictive Models of Forest Inventory Attributes Using an Area-Based Approach with Airborne LiDAR Data","volume":"156","author":"Bouvier","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_11","unstructured":"White, J.C., Tompalski, P., Vastaranta, M., Wulder, M.A., Saarinen, N., Stepper, C., and Coops, N.C. (2019, January 10). A Model Development and Application Guide for Generating an Enhanced Forest Inventory Using Airborne Laser Scanning Data and an Area-Based Approach. Available online: https:\/\/cfs.nrcan.gc.ca\/publications?id=38945."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"512","DOI":"10.5558\/tfc2011-050","article-title":"Operational Implementation of a LiDAR Inventory in Boreal Ontario","volume":"87","author":"Woods","year":"2011","journal-title":"For. Chron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"830","DOI":"10.3390\/rs4040830","article-title":"LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada","volume":"4","author":"Treitz","year":"2012","journal-title":"Remote Sens."},{"key":"ref_14","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_15","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":"Kelle","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"564","DOI":"10.5589\/m03-027","article-title":"Measuring Individual Tree Crown Diameter with Lidar and Assessing Its Influence on Estimating Forest Volume and Biomass","volume":"29","author":"Popescu","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.biombioe.2007.06.022","article-title":"Estimating Biomass of Individual Pine Trees Using Airborne Lidar","volume":"31","author":"Popescu","year":"2007","journal-title":"Biomass Bioenergy"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.rse.2012.07.006","article-title":"Forest Biomass Estimation from Airborne LiDAR Data Using Machine Learning Approaches","volume":"125","author":"Gleason","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.rse.2017.03.017","article-title":"Area-Based vs Tree-Centric Approaches to Mapping Forest Carbon in Southeast Asian Forests from Airborne Laser Scanning Data","volume":"194","author":"Coomes","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Zhen, Z., Quackenbush, L.J., and Zhang, L. (2016). Trends in Automatic Individual Tree Crown Detection and Delineation-Evolution of LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8040333"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s40725-017-0051-6","article-title":"Individual Tree Crown Methods for 3D Data from Remote Sensing","volume":"3","author":"Lindberg","year":"2017","journal-title":"Curr. For. Rep."},{"key":"ref_25","first-page":"2014","article-title":"Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies","volume":"27","author":"Maltamo","year":"2014","journal-title":"Manag. Ecosyst."},{"key":"ref_26","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_27","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_28","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1111\/gcb.13388","article-title":"Allometric Equations for Integrating Remote Sensing Imagery into Forest Monitoring Programmes","volume":"23","author":"Jucker","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Aubry-Kientz, M., Dutrieux, R., Ferraz, A., Saatchi, S., Hamraz, H., Williams, J., Coomes, D., Piboule, A., and Vincent, G. (2019). A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests. Remote Sens., 11.","DOI":"10.3390\/rs11091086"},{"key":"ref_31","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_32","first-page":"98","article-title":"PTrees: A Point-Based Approach to Forest Tree Extractionfrom Lidar Data","volume":"33","author":"Vega","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3938","DOI":"10.3390\/s8063938","article-title":"A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis and 3D Single Tree Modelling in Forest","volume":"8","author":"Wang","year":"2008","journal-title":"Sensors"},{"key":"ref_34","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_35","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":"2","author":"Raumonen","year":"2015","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.3390\/f6114245","article-title":"SimpleTree \u2014An Efficient Open Source Tool to Build Tree Models from TLS Clouds","volume":"6","author":"Hackenberg","year":"2015","journal-title":"Forests"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cag.2017.05.016","article-title":"Extraction of Tubular Shapes from Dense Point Clouds and Application to Tree Reconstruction from Laser Scanned Data","volume":"66","author":"Ravaglia","year":"2017","journal-title":"Comput. Graph."},{"key":"ref_38","unstructured":"Othmani, A., Piboule, A., Krebs, M., and Stolz, C. (2011, January 16\u201320). Towards Automated and Operational Forest Inventories with T-Lidar. Proceedings of the 11th International Conference on LiDAR Applications for Assessing Forest Ecosystems (SilviLaser 2011), Hobart, Australia. Available online: https:\/\/hal.archives-ouvertes.fr\/hal-00646403\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.06.021","article-title":"International Benchmarking of Terrestrial Laser Scanning Approaches for Forest Inventories","volume":"144","author":"Liang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.3390\/rs3081614","article-title":"Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations","volume":"3","author":"Vastaranta","year":"2011","journal-title":"Remote Sens."},{"key":"ref_42","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_43","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_44","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_45","doi-asserted-by":"crossref","unstructured":"Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L.P., Chave, J., Danson, F.M., Demol, M., and Disney, M. (2020). Terrestrial Laser Scanning in Forest Ecology: Expanding the Horizon. Remote Sens. Environ., 251.","DOI":"10.1016\/j.rse.2020.112102"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.14358\/PERS.78.11.1275","article-title":"Automated Delineation of Individual Tree Crowns from Lidar Data by Multi-Scale Analysis and Segmentation","volume":"78","author":"Jing","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s11676-017-0378-7","article-title":"Automated Tree Detection and Crown Delineation Using Airborne Laser Scanner Data in Heterogeneous East-Central Europe Forest with Different Species Mix","volume":"28","year":"2017","journal-title":"J. For. Res."},{"key":"ref_49","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":"Shendryk","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_50","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 Ata","volume":"52","author":"Wallace","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Davison, S., Donoghue, D.N.M., and Galiatsatos, N. (2020). The Effect of Leaf-on and Leaf-off Forest Canopy Conditions on LiDAR Derived Estimations of Forest Structural Diversity. Int. J. Appl. Earth Obs. Geoinf., 92.","DOI":"10.1016\/j.jag.2020.102160"},{"key":"ref_52","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_53","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_54","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1080\/01431161.2016.1252477","article-title":"Forestry Applications of UAVs in Europe: A Review","volume":"38","author":"Torresan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. (2020). Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote Sens., 12.","DOI":"10.3390\/rs12061046"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liang, X., Wang, Y., Py\u00f6r\u00e4l\u00e4, J., Lehtom\u00e4ki, M., Yu, X., Kaartinen, H., Kukko, A., Honkavaara, E., Issaoui, A.E.I., and Nevalainen, O. (2019). Forest in Situ Observations Using Unmanned Aerial Vehicle as an Alternative of Terrestrial Measurements. For. Ecosyst., 6.","DOI":"10.1186\/s40663-019-0173-3"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.3390\/rs4061519","article-title":"Development of a UAV-LiDAR System with Application to Forest Inventory","volume":"4","author":"Wallace","year":"2012","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Torresan, C., Carotenuto, F., Chiavetta, U., Miglietta, F., Zaldei, A., and Gioli, B. (2020). Individual Tree Crown Segmentation in Two-Layered Dense Mixed Forests from UAV Lidar Data. Drones, 4.","DOI":"10.3390\/drones4020010"},{"key":"ref_60","first-page":"105","article-title":"First Examples from the RIEGL VUX-SYS for Forestry Applications","volume":"2015","author":"Gottfried","year":"2015","journal-title":"Proc. SilviLaser"},{"key":"ref_61","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_62","doi-asserted-by":"crossref","unstructured":"Brede, B., Calders, K., Lau, A., Raumonen, P., Bartholomeus, H.M., Herold, M., and Kooistra, L. (2019). Non-Destructive Tree Volume Estimation through Quantitative Structure Modelling: Comparing UAV Laser Scanning with Terrestrial LIDAR. Remote Sens. Environ., 233.","DOI":"10.1016\/j.rse.2019.111355"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/rse2.137","article-title":"UAV-Derived Estimates of Forest Structure to Inform Ponderosa Pine Forest Restoration","volume":"6","author":"Belmonte","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s12371-014-0104-1","article-title":"Using Terrestrial Laser Scanning for the Recognition and Promotion of High-Alpine Geomorphosites","volume":"6","author":"Ravanel","year":"2014","journal-title":"Geoheritage"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.isprsjprs.2010.08.002","article-title":"A Low-Cost Multi-Sensoral Mobile Mapping System and Its Feasibility for Tree Measurements","volume":"65","author":"Jaakkola","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1139\/juvs-2017-0030","article-title":"UAV-LiDAR Accuracy in Vegetated Terrain","volume":"6","author":"Kucharczyk","year":"2018","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Torresan, C., Berton, A., Carotenuto, F., Chiavetta, U., Miglietta, F., Zaldei, A., and Gioli, B. (2018). Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV). Remote Sens., 10.","DOI":"10.3390\/rs10071094"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.isprsjprs.2019.01.020","article-title":"Modelling the Effects of Fundamental UAV Flight Parameters on LiDAR Point Clouds to Facilitate Objectives-Based Planning","volume":"149","author":"Sofonia","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"393","DOI":"10.5194\/isprs-archives-XLII-2-W6-393-2017","article-title":"Potential of Multi-Temporal UAV-Borne Lidar in Assessing Effectiveness of Silvicultural Treatments","volume":"42","author":"Vepakomma","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","unstructured":"Li, J., Yang, B., Cong, Y., Cao, L., Fu, X., and Dong, Z. (2019). 3D Forest Mapping Using a Low-Cost UAV Laser Scanning System: Investigation and Comparison. Remote Sens., 11.","DOI":"10.3390\/rs11060717"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2020.08.002","article-title":"Influence of ULS Acquisition Characteristics on Tree Stem Parameter Estimation","volume":"168","author":"Bruggisser","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"7160","DOI":"10.1109\/TGRS.2014.2308208","article-title":"An Assessment of the Repeatability of Automatic Forest Inventory Metrics Derived from UAV-Borne Laser Scanning Data","volume":"52","author":"Wallace","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Jaakkola, A., Hyypp\u00e4, J., Yu, X., Kukko, A., Kaartinen, H., Liang, X., Hyypp\u00e4, H., and Wang, Y. (2017). Autonomous Collection of Forest Field Reference\u2014The Outlook and a First Step with UAV Laser Scanning. Remote Sens., 9.","DOI":"10.3390\/rs9080785"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.isprsjprs.2018.11.001","article-title":"Estimating Forest Structural Attributes Using UAV-LiDAR Data in Ginkgo Plantations","volume":"146","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"657","DOI":"10.5194\/isprs-archives-XLII-2-W13-657-2019","article-title":"Individual Tree Detection from UAV Lidar Data in a Mixed Species Woodland","volume":"42","author":"Zaforemska","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Wang, Y., Py\u00f6r\u00e4l\u00e4, J., Liang, X., Lehtom\u00e4ki, M., Kukko, A., Yu, X., Kaartinen, H., and Hyypp\u00e4, J. (2019). In Situ Biomass Estimation at Tree and Plot Levels: What Did Data Record and What Did Algorithms Derive from Terrestrial and Aerial Point Clouds in Boreal Forest. Remote Sens. Environ., 232.","DOI":"10.1016\/j.rse.2019.111309"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Sa\u010dkov, I., Santopuoli, G., Bucha, T., Lasserre, B., and Marchetti, M. (2016). Forest Inventory Attribute Prediction Using Lightweight Aerial Scanner Data in a Selected Type of Multilayered Deciduous Forest. Forests, 7.","DOI":"10.3390\/f7120307"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wieser, M., Mandlburger, G., Hollaus, M., Otepka, J., Glira, P., and Pfeifer, N. (2017). A Case Study of UAS Borne Laser Scanning for Measurement of Tree Stem Diameter. Remote Sens., 9.","DOI":"10.3390\/rs9111154"},{"key":"ref_80","unstructured":"(2019, June 20). Ecosystem Classification Working Group Our Landscape Heritage: The Story of Ecological Land Classification in New Brunswick. Available online: https:\/\/www2.gnb.ca\/."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1093\/forestscience\/43.3.455","article-title":"Bowersox, The Practice of Silviculture\u2014Applied Forest Ecology, Ninth Edition","volume":"43","author":"Todd","year":"1997","journal-title":"Forest Sci."},{"key":"ref_82","first-page":"25","article-title":"Comparison of Techniques for Terrestrial Laser Scanning Data Georeferencing Applied to 3D Modeling of Cultural Heritage","volume":"36","author":"Alba","year":"2007","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_83","unstructured":"GeoNB (2020, January 05). Province of New Brunswick\u2019s Gateway to Geographic Information. Available online: http:\/\/www.snb.ca\/geonb1\/e\/index-E.asp."},{"key":"ref_84","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_85","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."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.isprsjprs.2020.09.014","article-title":"Is Field-Measured Tree Height as Reliable as Believed\u2014Part II, A Comparison Study of Tree Height Estimates from Conventional Field Measurement and Low-Cost Close-Range Remote Sensing in a Deciduous Forest","volume":"169","author":"Liang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.foreco.2013.08.014","article-title":"Crown Modeling by Terrestrial Laser Scanning as an Approach to Assess the Effect of Aboveground Intra- and Interspecific Competition on Tree Growth","volume":"310","author":"Metz","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2011.10.006","article-title":"Analyzing Forest Canopies with Ground-Based Laser Scanning: A Comparison with Hemispherical Photography","volume":"154\u2013155","author":"Seidel","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.3390\/rs70201877","article-title":"Terrestrial Laser Scanning as an Effective Tool to Retrieve Tree Level Height, Crown Width, and Stem Diameter","volume":"7","author":"Srinivasan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_90","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_91","doi-asserted-by":"crossref","unstructured":"Levick, S.R., Whiteside, T., Loewensteiner, D.A., Rudge, M., and Bartolo, R. (2021). Leveraging Tls as a Calibration and Validation Tool for Mls and Uls Mapping of Savanna Structure and Biomass at Landscape-Scales. Remote Sens., 13.","DOI":"10.3390\/rs13020257"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"891","DOI":"10.5194\/isprsarchives-XL-8-891-2014","article-title":"Evaluation of Partially Overlapping 3D Point Cloud\u2019s Registration by Using ICP Variant and Cloudcompare","volume":"40","author":"Rajendra","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/01431161.2011.565815","article-title":"A Simple Technique for Co-Registration of Terrestrial LiDAR Observations for Forestry Applications","volume":"3","author":"Hilker","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.isprsjprs.2014.06.015","article-title":"Keypoint-Based 4-Points Congruent Sets\u2014Automated Marker-Less Registration of Laser Scans","volume":"96","author":"Theiler","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_95","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":"2012","journal-title":"Forestry"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Roussel, J.R., Auty, D., Coops, N.C., Tompalski, P., Goodbody, T.R.H., Meador, A.S., Bourdon, J.F., de Boissieu, F., and Achim, A. (2020). LidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data. Remote Sens. Environ., 251.","DOI":"10.1016\/j.rse.2020.112061"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"3899","DOI":"10.3390\/f6113899","article-title":"Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds","volume":"6","author":"Audet","year":"2015","journal-title":"Forests"},{"key":"ref_98","unstructured":"Python Software Foundation (2019, February 01). Python Language Reference, Version 2.7. Available online: http:\/\/www.python.org."},{"key":"ref_99","unstructured":"Computree Core Team (2019, May 20). Computree Platform. Available online: http:\/\/rdinnovation.onf.fr\/computree."},{"key":"ref_100","unstructured":"Stere\u0144czak, K., B\u0119dkowski, K., and Weinacker, H. (2008, January 3\u201311). Accuracy of Crown Segmentation and Estimation of Selected Trees and Forest Stand Parameters in Order to Resolution of Used DSM and NDSM Models Generated from Dense Small Footprint LIDAR Data. Proceedings of the ISPRS Congress, Commission VI, WG VI\/5, Beijing, China."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Barnes, C., Balzter, H., Barrett, K., Eddy, J., Milner, S., and Su\u00e1rez, J.C. (2017). Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands. Remote Sens., 9.","DOI":"10.3390\/rs9030231"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/BF01386390","article-title":"A Note on Two Problems in Connexion with Graphs","volume":"1","author":"Dijkstra","year":"1959","journal-title":"Numer. Math"},{"key":"ref_103","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_104","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.3390\/f6041274","article-title":"Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density","volume":"6","author":"Hackenberg","year":"2015","journal-title":"Forests"},{"key":"ref_105","unstructured":"R Core Team (2019). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Schneider, R., Calama, R., and Martin-Ducup, O. (2020). Understanding Tree-to-Tree Variations in Stone Pine (Pinus Pinea L.) Cone Production Using Terrestrial Laser Scanner. Remote Sens., 12.","DOI":"10.3390\/rs12010173"},{"key":"ref_107","first-page":"343","article-title":"Segmented: An R Package to Fit Regression Models with Broken-Line Relationships","volume":"3","author":"Muggeo","year":"2008","journal-title":"R. News"},{"key":"ref_108","first-page":"649","article-title":"Laser-Scanned Tree Stem Filtering for Forest Inventories Measurements","volume":"1","author":"Ravaglia","year":"2013","journal-title":"Digit. Herit. Int. Congr."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Kore\u0148, M., Hun\u010daga, M., Chud\u00e1, J., Mokro\u0161, M., and Surov\u00fd, P. (2020). The Influence of Cross-Section Thickness on Diameter at Breast Height Estimation from Point Cloud. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9090495"},{"key":"ref_110","first-page":"187","article-title":"Adaptive Methods for Individual Tree Detection on Airborne Laser Based Canopy Height Model","volume":"36","author":"Maltamo","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Info. Sci."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Tanhuanp\u00e4\u00e4, T., Saarinen, N., Kankare, V., Nurminen, K., Vastaranta, M., Honkavaara, E., Karjalainen, M., Yu, X., Holopainen, M., and Hyypp\u00e4, J. (2016). Evaluating the Performance of High-Altitude Aerial Image-Based Digital Surface Models in Detecting Individual Tree Crowns in Mature Boreal Forests. Forests, 7.","DOI":"10.3390\/f7070143"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Che, E., Jung, J., and Olsen, M.J. (2019). Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review. Sensors, 19.","DOI":"10.3390\/s19040810"},{"key":"ref_113","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_114","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.isprsjprs.2020.11.016","article-title":"Combining Graph-Cut Clustering with Object-Based Stem Detection for Tree Segmentation in Highly Dense Airborne Lidar Point Clouds","volume":"172","author":"Dersch","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"99783","DOI":"10.1109\/ACCESS.2020.2995389","article-title":"Combining Trunk Detection with Canopy Segmentation to Delineate Single Deciduous Trees Using Airborne LiDAR Data","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_116","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_117","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_118","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.rse.2013.08.049","article-title":"Monitoring Selective Logging in Western Amazonia with Repeat Lidar Flights","volume":"151","author":"Andersen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2015.05.007","article-title":"Modeling Aboveground Tree Woody Biomass Using National-Scale Allometric Methods and Airborne Lidar","volume":"106","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.rse.2017.09.037","article-title":"Identifying the Genus or Species of Individual Trees Using a Three-Wavelength Airborne Lidar System","volume":"204","author":"Budei","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_121","first-page":"26","article-title":"Urban Tree Health Assessment Using Airborne Hyperspectral and LiDAR Imagery","volume":"73","author":"Degerickx","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.foreco.2016.04.047","article-title":"Response of Sugar Maple (Acer saccharum, Marsh.) Tree Crown Structure to Competition in Pure versus Mixed Stands","volume":"374","author":"Olivier","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Chen, X., Jiang, K., Zhu, Y., Wang, X., and Yun, T. (2021). Individual Tree Crown Segmentation Directly from Uav-Borne Lidar Data Using the Pointnet of Deep Learning. Forests, 12.","DOI":"10.3390\/f12020131"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1190\/tle36070566.1","article-title":"UAV-Based LiDAR Acquisition for the Derivation of High-Resolution Forest and Ground Information","volume":"36","author":"Morsdorf","year":"2017","journal-title":"Lead. Edge"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"3174","DOI":"10.1109\/JSTARS.2014.2331276","article-title":"Delineation of Tree Crowns and Tree Species Classification from Full-Waveform Airborne Laser Scanning Data Using 3-d Ellipsoidal Clustering","volume":"7","author":"Lindberg","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_126","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_127","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_128","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2021.635440","article-title":"Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning","volume":"12","author":"Seidel","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.ecolind.2017.10.066","article-title":"Predicting Stem Diameters and Aboveground Biomass of Individual Trees Using Remote Sensing Data","volume":"85","author":"Dalponte","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_130","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_131","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1080\/01431160903380649","article-title":"Estimation of Tree Lists from Airborne Laser Scanning by Combining Single-Tree and Area-Based Methods","volume":"31","author":"Lindberg","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1139\/cjfr-2014-0285","article-title":"Matching Remotely Sensed and Field Measured Tree Size Distributions","volume":"45","author":"Vauhkonen","year":"2014","journal-title":"Can. J. For. Res."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"6776","DOI":"10.1109\/TGRS.2015.2448056","article-title":"Canopy Density Model: A New ALS-Derived Product to Generate Multilayer Crown Cover Maps","volume":"53","author":"Ferraz","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Du, S., Lindenbergh, R., Ledoux, H., Stoter, J., and Nan, L. (2019). AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees. Remote Sens., 11.","DOI":"10.20944\/preprints201907.0058.v2"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2796\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:30:52Z","timestamp":1760164252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":134,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142796"],"URL":"https:\/\/doi.org\/10.3390\/rs13142796","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,16]]}}}