{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T04:00:04Z","timestamp":1776139204596,"version":"3.50.1"},"reference-count":116,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Proyecto Retos Junta de Andaluc\u00eda, Spain","award":["P18-RT-2327"],"award-info":[{"award-number":["P18-RT-2327"]}]},{"name":"Proyecto Retos Junta de Andaluc\u00eda, Spain","award":["UAL2020-SEJ-D1931"],"award-info":[{"award-number":["UAL2020-SEJ-D1931"]}]},{"name":"Programa Operativo FEDER Andaluc\u00eda 2014-2020, Spain","award":["P18-RT-2327"],"award-info":[{"award-number":["P18-RT-2327"]}]},{"name":"Programa Operativo FEDER Andaluc\u00eda 2014-2020, Spain","award":["UAL2020-SEJ-D1931"],"award-info":[{"award-number":["UAL2020-SEJ-D1931"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (\u22481.5 points\/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score &gt; 80.57%, H Pearson\u2019s r &gt; 0.97, and CD Pearson\u00b4s r &gt; 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson\u00b4s r = 0.94, and CD Pearson\u00b4s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score &gt; 85% and &gt;76%, and H Pearson\u00b4s r &gt; 0.96 and &gt;0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson\u00b4s r around 0.20 for both approaches).<\/jats:p>","DOI":"10.3390\/rs16213974","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T08:42:13Z","timestamp":1729845733000},"page":"3974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Benchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1525-9512","authenticated-orcid":false,"given":"Abderrahim","family":"Nemmaoui","sequence":"first","affiliation":[{"name":"Department of Engineering, Research Centre CIAIMBITAL, University of Almer\u00eda, Carretera de Sacramento s\/n, La Ca\u00f1ada de San Urbano, 04120 Almer\u00eda, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5144-6411","authenticated-orcid":false,"given":"Fernando J.","family":"Aguilar","sequence":"additional","affiliation":[{"name":"Department of Engineering, Research Centre CIAIMBITAL, University of Almer\u00eda, Carretera de Sacramento s\/n, La Ca\u00f1ada de San Urbano, 04120 Almer\u00eda, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0404-9875","authenticated-orcid":false,"given":"Manuel A.","family":"Aguilar","sequence":"additional","affiliation":[{"name":"Department of Engineering, Research Centre CIAIMBITAL, University of Almer\u00eda, Carretera de Sacramento s\/n, La Ca\u00f1ada de San Urbano, 04120 Almer\u00eda, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A large and persistent carbon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1039\/b809492f","article-title":"Sequestration of atmospheric CO2 in global carbon pools","volume":"1","author":"Lal","year":"2008","journal-title":"Energy Environ. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11676-015-0088-y","article-title":"Drone remote sensing for forestry research and practices","volume":"26","author":"Tang","year":"2015","journal-title":"J. For. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mokro, M., Liang, X., Surov\u00fd, P., Valent, P., \u010cer\u0148ava, J., Chud\u00fd, F., Tun\u00e1k, D., Salo\u0148, I., and Mergani\u010d, J. (2018). Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030093"},{"key":"ref_5","first-page":"e2712097","article-title":"Precisi\u00f3n y eficiencia del inventario de plantaciones de teca en Ecuador mediante esc\u00e1ner l\u00e1ser terrestre","volume":"27","author":"Nemmaoui","year":"2021","journal-title":"Madera Y Bosques"},{"key":"ref_6","unstructured":"Avery, T., and Burkhart, H.E. (1994). Forest Measurements, McGraw-Hill, Inc.. [4th ed.]."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10310-007-0041-9","article-title":"Detection of individual trees and estimation of tree height using LiDAR data","volume":"12","author":"Kwak","year":"2007","journal-title":"J. For. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"353","DOI":"10.5194\/isprs-annals-V-3-2022-353-2022","article-title":"Aleppe Pine Allometric Modeling Through Integrating UAV Image-Based Clouds and Ground Based Data","volume":"3","author":"Aguilar","year":"2022","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_9","unstructured":"Atzberger, C., Zeug, G., Defourny, P., Arag\u00e3o, L., Hammarstr\u00f6m, L., and Immitzer, M. (2020). Monitoring of Forests Through Remote Sensing\u2014Final Report."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lamping, J.E., Zald, H.S.J., Madurapperuma, B.D., and Graham, J. (2021). Comparison of Low-Cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning across Multiple Forest Types in California, USA. Remote Sens., 13.","DOI":"10.3390\/rs13214292"},{"key":"ref_11","first-page":"767","article-title":"The use of fixed\u2013wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer\u2014Broadleaf forest","volume":"73","author":"Jayathunga","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1641\/0006-3568(2002)052[0019:LRSFES]2.0.CO;2","article-title":"Lidar Remote Sensing for Ecosystem StudiesLidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular intere","volume":"52","author":"Lefsky","year":"2002","journal-title":"Bioscience"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"121395","DOI":"10.1016\/j.foreco.2023.121395","article-title":"Mapping site index in coniferous forests using bi-temporal airborne laser scanning data and field data from the Swedish national forest inventory","volume":"547","author":"Wallerman","year":"2023","journal-title":"For. Ecol. Manag."},{"key":"ref_15","unstructured":"Vosselman, G., and Maas, H.-G. (2010). Airborne and Terrestrial Laser Scanning, Whittles Publishing."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"331","DOI":"10.14358\/PERS.70.3.331","article-title":"Accuracy of airborne lidar-derived elevation: Empirical assessment and error budget","volume":"70","author":"Hodgson","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100022","DOI":"10.1016\/j.fecs.2022.100022","article-title":"Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems","volume":"9","author":"Fernandes","year":"2022","journal-title":"For. Ecosyst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112462","DOI":"10.1016\/j.jenvman.2021.112462","article-title":"Vegetation structure parameters determine high burn severity likelihood in different ecosystem types: A case study in a burned Mediterranean landscape","volume":"288","author":"Calvo","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/j.rse.2011.01.017","article-title":"Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules","volume":"115","author":"Chuvieco","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1080\/15481603.2020.1738060","article-title":"Forest structural diversity characterization in Mediterranean landscapes affected by fires using Airborne Laser Scanning data","volume":"57","author":"Gelabert","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Aguilar, F.J., Nemmaoui, A., Aguilar, M.A., and Pe\u00f1alver, A. (2021). Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression. Appl. Sci., 11.","DOI":"10.3390\/app112110139"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Aguilar, F.J., Nemmaoui, A., Pe\u00f1alver, A., Rivas, J.R., and Aguilar, M.A. (2019). Developing Allometric Equations for Teak Plantations Located in the Coastal Region of Ecuador from Terrestrial Laser Scanning Data. Forests, 10.","DOI":"10.3390\/f10121050"},{"key":"ref_23","first-page":"131","article-title":"Fusion of Terrestrial Laser Scanning and RPAS Image Based point cloud in Mediterranean forest inventories","volume":"94","author":"Aguilar","year":"2019","journal-title":"Dyna Ing. E Ind."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Aguilar, F.J., Rivas, J.R., Nemmaoui, A., Pe\u00f1alver, A., and Aguilar, M.A. (2019). UAV-Based Digital Terrain Model Generation under Leaf-Off Conditions to Support Teak Plantations Inventories in Tropical Dry Forests. A Case of the Coastal Region of Ecuador. Sensors, 19.","DOI":"10.3390\/s19081934"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, C., Huang, Z., Chang, Y.C., Liu, L., and Pei, Q. (2024). A Low-Cost and Lightweight Real-Time Object-Detection Method Based on UAV Remote Sensing in Transportation Systems. Remote Sens., 16.","DOI":"10.3390\/rs16193712"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Maltamo, M., Naesset, E., and Vauhkonen, J. (2014). Forestry Applications of Airborne Laser Scanningg: Concepts and Case Studies, Springer. Managing Forest Ecosystems.","DOI":"10.1007\/978-94-017-8663-8"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Wallace, L., Lucieer, A., Malenovsk\u00fd, Z., Turner, D., Vop\u011bnka, P., Wallace, L., Lucieer, A., Malenovsk\u00fd, Z., Turner, D., and Vop\u011bnka, P. (2016). Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests, 7.","DOI":"10.3390\/f7030062"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.3390\/f5061481","article-title":"Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas","volume":"5","author":"McCall","year":"2014","journal-title":"Forests"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1177\/194008291200500202","article-title":"Dawn of drone ecology: Low-cost autonomous aerial vehicles for conservation","volume":"5","author":"Koh","year":"2012","journal-title":"Trop. Conserv. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"605","DOI":"10.7848\/ksgpc.2015.33.6.605","article-title":"Calculation of Tree Height and Canopy Crown from Drone Images Using Segmentation","volume":"33","author":"Lim","year":"2015","journal-title":"J. Korean Soc. Surv. Geod. Photogramm. Cartogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Maltamo, M., N\u00e6sset, E., and Vauhkonen, J. (2013). Segmentation of Forest to Tree Objects BT\u2014Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies. Forestry Applications of Airborne Laser Scanning. Managing Forest Ecosystems, Springer.","DOI":"10.1007\/978-94-017-8663-8"},{"key":"ref_35","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_36","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_37","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_38","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2012.04.003","article-title":"An individual tree crown delineation method based on multi-scale segmentation of imagery","volume":"70","author":"Jing","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1080\/07038992.2016.1196582","article-title":"Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data","volume":"42","author":"Silva","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4163","DOI":"10.3390\/rs5094163","article-title":"Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches","volume":"5","author":"Jakubowski","year":"2013","journal-title":"Remote Sens."},{"key":"ref_41","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_42","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_43","doi-asserted-by":"crossref","first-page":"4999","DOI":"10.1080\/01431161.2010.494633","article-title":"Prior-knowledge-based single-tree extraction","volume":"32","author":"Heinzel","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sa\u010dkov, I., Hl\u00e1sny, T., Bucha, T., and Juri\u0161, M. (2017). Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data. iForest-Biogeosci. For., 10.","DOI":"10.3832\/ifor2093-010"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Xu, X., Iuricich, F., and De Floriani, L. (2020, January 3\u20136). A Persistence-Based Approach for Individual Tree Mapping. Proceedings of the 28th International Conference on Advances in Geographic Information Systems, Seattle, WA, USA.","DOI":"10.1145\/3397536.3422231"},{"key":"ref_46","unstructured":"Kaartinen, H., and Hyypp\u00e4, J. (2008). EuroSDR\/ISPRS Project Commission II, Tree Extraction, Final Report, EuroSDR."},{"key":"ref_47","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_48","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_49","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":"XLII-2-W13","author":"Zaforemska","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.3390\/rs3112494","article-title":"Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements","volume":"3","author":"Edson","year":"2011","journal-title":"Remote Sens."},{"key":"ref_51","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_52","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_53","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1080\/22797254.2017.1336067","article-title":"Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest","volume":"50","author":"Domingo","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neucom.2014.09.091","article-title":"A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables","volume":"167","author":"Troncoso","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"992","DOI":"10.3390\/f5050992","article-title":"Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden","volume":"5","author":"Shendryk","year":"2014","journal-title":"Forests"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Domingo, D., Alonso, R., Lamelas, M.T., Montealegre, A.L., Rodr\u00edguez, F., and de la Riva, J. (2019). Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data. Remote Sens., 11.","DOI":"10.3390\/rs11030261"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Viedma, O., Almeida, D.R.A., and Moreno, J.M. (2020). Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand. Remote Sens., 12.","DOI":"10.3390\/rs12213554"},{"key":"ref_58","first-page":"103","article-title":"Using low density LiDAR data to map Mediterranean forest characteristics by means of an area-based approach and height threshold analysis","volume":"2016","year":"2016","journal-title":"Rev. Teledetecci\u00f3n"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"363","DOI":"10.3390\/f5020363","article-title":"Assessing the Feasibility of Low-Density LiDAR for Stand Inventory Attribute Predictions in Complex and Managed Forests of Northern Maine, USA","volume":"5","author":"Hayashi","year":"2014","journal-title":"Forests"},{"key":"ref_60","first-page":"59","article-title":"Estimation of timber assortments using low-density ALS data","volume":"Volume XXXVIII","year":"2010","journal-title":"ISPRS TC VII Symposium\u2013100 Years ISPRS"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1109\/JSTARS.2015.2418675","article-title":"Estimating Tree Height Distribution Using Low-Density ALS Data with and without Training Data","volume":"8","author":"Virolainen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1093\/forestry\/cpw008","article-title":"Use of low point density ALS data to estimate stand-level structural variables in Mediterranean Aleppo pine forest","volume":"89","author":"Montealegre","year":"2016","journal-title":"Forestry"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S0034-4257(01)00243-7","article-title":"Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve","volume":"79","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.3390\/f5071682","article-title":"Outlook for the Next Generation\u2019s Precision Forestry in Finland","volume":"5","author":"Holopainen","year":"2014","journal-title":"Forests"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lin, L., Hao, Z., Post, C.J., and Mikhailova, E.A. (2023). Protection of Coastal Shelter Forests Using UAVs: Individual Tree and Tree-Height Detection in Casuarina equisetifolia L. Forests. Forests, 14.","DOI":"10.3390\/f14020233"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1111\/2041-210X.13860","article-title":"Optimizing aerial imagery collection and processing parameters for drone-based individual tree mapping in structurally complex conifer forests","volume":"13","author":"Young","year":"2022","journal-title":"Methods Ecol. Evol."},{"key":"ref_68","first-page":"110","article-title":"DEM Generation from Laser Scanner Data Using Adaptive TIN Models","volume":"33","author":"Axelsson","year":"2000","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cavas-Mart\u00ednez, F., Mar\u00edn Granados, M.D., Mir\u00e1lbes Buil, R., and De-C\u00f3zar-Mac\u00edas, O.D. (2023). UAV-Based Digital Terrain Model Generation to Support Accurate Inventories in Mediterranean Forests. Advances in Design Engineering III. INGEGRAF 2022. Lecture Notes in Mechanical Engineering, Springer International Publishing.","DOI":"10.1007\/978-3-031-20325-1"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1590\/s1982-21702018000300021","article-title":"A Lightweight UAV-Based Laser Scanning System for Forest Application","volume":"24","author":"Torres","year":"2018","journal-title":"Bol. Ci\u00eancias Geod\u00e9sicas"},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"4072","DOI":"10.1109\/JSTARS.2015.2436974","article-title":"de la A Comparison of Open-Source LiDAR Filtering Algorithms in a Mediterranean Forest Environment","volume":"8","author":"Montealegre","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"922","DOI":"10.3390\/f4040922","article-title":"A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery","volume":"4","author":"Lisein","year":"2013","journal-title":"Forests"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"112061","DOI":"10.1016\/j.rse.2020.112061","article-title":"lidR: An R package for analysis of Airborne Laser Scanning (ALS) data","volume":"251","author":"Roussel","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_75","unstructured":"(2022, December 23). R Core Team R: The R Project for Statistical Computing. Available online: https:\/\/www.r-project.org\/."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Zhen, Z., Quackenbush, L.J., Zhang, L., Swatantran, A., Baghdadi, N., and Thenkabail, P.S. (2016). Trends in Automatic Individual Tree Crown Detection and Delineation\u2014Evolution of LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8040333"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10707-023-00487-4","article-title":"A topology-based approach to individual tree segmentation from airborne LiDAR data","volume":"27","author":"Xu","year":"2023","journal-title":"Geoinformatica"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/2041-210X.12575","article-title":"Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data","volume":"7","author":"Dalponte","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.05.032","article-title":"Removing bias from LiDAR-based estimates of canopy height: Accounting for the effects of pulse density and footprint size","volume":"198","author":"Roussel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2015.12.039","article-title":"On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters","volume":"175","author":"Renaud","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0924-2716(99)00015-5","article-title":"Airborne laser scanning: Basic relations and formulas","volume":"54","author":"Baltsavias","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"863","DOI":"10.14358\/PERS.80.9.863","article-title":"Generating pit\u2014Free canopy height models from airborne lidar","volume":"80","author":"Khosravipour","year":"2014","journal-title":"Photogramm. Eng. Remote Sens. PE&RS"},{"key":"ref_83","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_84","doi-asserted-by":"crossref","first-page":"633","DOI":"10.5589\/m03-024","article-title":"Combined high-density lidar and multispectral imagery for individual tree crown analysis","volume":"29","author":"Leckie","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_85","unstructured":"Parkan, M. (2020, November 23). Digital Forestry Toolbox for Matlab\/Octave. Available online: http:\/\/mparkan.github.io\/Digital-Forestry-Toolbox\/."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/0098-3004(96)00021-0","article-title":"Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW)","volume":"22","author":"Bartier","year":"1996","journal-title":"Comput. Geosci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"108736","DOI":"10.1016\/j.ecolmodel.2019.108736","article-title":"Optimizing individual tree detection accuracy and measuring forest uniformity in coconut (Cocos nucifera L.) plantations using airborne laser scanning","volume":"409","author":"Mohan","year":"2019","journal-title":"Ecol. Modell."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"621","DOI":"10.5589\/m02-059","article-title":"Error reduction methods for local maximum filtering of high spatial resolution imagery for locating trees","volume":"28","author":"Wulder","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/S0034-4257(00)00101-2","article-title":"Local Maximum Filtering for the Extraction of Tree Locations and Basal Area from High Spatial Resolution Imagery","volume":"73","author":"Wulder","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"589","DOI":"10.14358\/PERS.70.5.589","article-title":"Seeing the trees in the forest: Using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height","volume":"70","author":"Popescu","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_91","unstructured":"UNFCCC (2005). Report of the Conference of the Parties Serving as the Meeting of the Parties to the Kyoto Protocol on Its First Session, Held at Montreal from 28 November to 10 December 2005. Addendum. Part Two, United Nations ClimateChage."},{"key":"ref_92","unstructured":"Sack, J.-R., and Urrutia, J. (2000). Voronoi Diagrams. Handbook of Computational Geometry, North-Holland."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"073699","DOI":"10.1117\/1.JRS.7.073699","article-title":"Application of a single-tree identification algorithm to LiDAR data for the simulation of stem volume current annual increment","volume":"7","author":"Bottai","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_94","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_95","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_96","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_97","doi-asserted-by":"crossref","first-page":"112540","DOI":"10.1016\/j.rse.2021.112540","article-title":"Influence of flight parameters on UAS-based monitoring of tree height, diameter, and density","volume":"263","author":"Swayze","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1139\/cjfr-2020-0433","article-title":"Potential for individual tree monitoring in ponderosa pine dominated forests using unmanned aerial system structure from motion point clouds","volume":"51","author":"Creasy","year":"2021","journal-title":"Can. J. For. Res."},{"key":"ref_99","first-page":"24","article-title":"Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation","volume":"WS-06-06","author":"Sokolova","year":"2006","journal-title":"AAAI Work.-Tech. Rep."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/978-3-540-31865-1_25","article-title":"A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation","volume":"3408","author":"Goutte","year":"2005","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3939","DOI":"10.1080\/01431160110115960","article-title":"Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations","volume":"23","author":"Asner","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.5558\/tfc791075-6","article-title":"Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program","volume":"79","author":"Wulder","year":"2003","journal-title":"For. Chron."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1080\/01431160701736489","article-title":"Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests","volume":"29","author":"Leckie","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Mohan, M., Silva, C.A., 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_105","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_106","doi-asserted-by":"crossref","unstructured":"Tinkham, W.T., and Swayze, N.C. (2021). Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models. Forests, 12.","DOI":"10.3390\/f12020250"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Hauglin, M., and N\u00e6sset, E. (2016). Detection and Segmentation of Small Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning. Remote Sens., 8.","DOI":"10.3390\/rs8050407"},{"key":"ref_108","first-page":"646","article-title":"Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: Application to a mountainous forest with heterogeneous stands","volume":"13","author":"Durrieu","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"650","DOI":"10.5589\/m03-023","article-title":"Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data","volume":"29","author":"Gaveau","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_111","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_112","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1109\/JSTARS.2020.3001978","article-title":"A New Clustering-Based Framework to the Stem Estimation and Growth Fitting of Street Trees from Mobile Laser Scanning Data","volume":"13","author":"Xu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_113","unstructured":"Dalponte, M. (2017, March 01). Package \u2018itcSegment\u2019: Individual Tree Crowns Segmentation. R Package Version 0.6. Available online: https:\/\/CRAN.R-project.org\/package=itcSegment."},{"key":"ref_114","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_115","unstructured":"Montero, G., Ruiz-Peinado, R., and Mu\u00f1oz, M. (2005). Producci\u00f3n de Biomasa y Fijaci\u00f3n de CO2 por los Bosques Espa\u00f1oles, Instituto Nacional de Investigaci\u00f3n y Tecnolog\u00eda Agraria y Alimentaria. Monograf\u00edas I.N.I.A.: Forestal; Ministerio de Educaci\u00f3n y Ciencia."},{"key":"ref_116","unstructured":"(2022, April 03). Spanish Forest Map. Available online: https:\/\/www.miteco.gob.es\/es\/biodiversidad\/servicios\/banco-datos-naturaleza\/informacion-disponible\/mfe50.html."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/3974\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:20:37Z","timestamp":1760113237000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/3974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"references-count":116,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16213974"],"URL":"https:\/\/doi.org\/10.3390\/rs16213974","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]}}}