{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:22:41Z","timestamp":1783938161980,"version":"3.55.0"},"reference-count":68,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science (JSPS)","doi-asserted-by":"publisher","award":["21H02230"],"award-info":[{"award-number":["21H02230"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely acquisition of forest structure is crucial for understanding the dynamics of ecosystem functions. Despite the fact that the combination of different quantitative structure models (QSMs) and point cloud sources (ALS and DAP) has shown great potential to characterize tree structure, few studies have addressed their pros and cons in alpine temperate deciduous forests. In this study, different point clouds from UAV-mounted LiDAR and DAP under leaf-off conditions were first processed into individual tree point clouds, and then explicit 3D tree models of the forest were reconstructed using the TreeQSM and AdQSM methods. Structural metrics obtained from the two QSMs were evaluated based on terrestrial LiDAR (TLS)-based surveys. The results showed that ALS-based predictions of forest structure outperformed DAP-based predictions at both plot and tree levels. TreeQSM performed with comparable accuracy to AdQSM for estimating tree height, regardless of ALS (plot level: 0.93 vs. 0.94; tree level: 0.92 vs. 0.92) and DAP (plot level: 0.86 vs. 0.86; tree level: 0.89 vs. 0.90) point clouds. These results provide a robust and efficient workflow that takes advantage of UAV monitoring for estimating forest structural metrics and suggest the effectiveness of LiDAR in temperate deciduous forests.<\/jats:p>","DOI":"10.3390\/rs16040697","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T06:00:25Z","timestamp":1708063225000},"page":"697","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs"],"prefix":"10.3390","volume":"16","author":[{"given":"Yi","family":"Gan","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Shizuoka University, Shizuoka 422-8529, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5483-0243","authenticated-orcid":false,"given":"Quan","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangman","family":"Song","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"ref_1","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_2","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_3","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s40725-019-00087-2","article-title":"Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions","volume":"5","author":"Goodbody","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2019.01.029","article-title":"Comparison and Integration of Lidar and Photogrammetric Point Clouds for Mapping Pre-Fire Forest Structure","volume":"224","author":"Filippelli","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"McNicol, I.M., Mitchard, E.T.A., Aquino, C., Burt, A., Carstairs, H., Dassi, C., Modinga Dikongo, A., and Disney, M.I. (2021). To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests. J. Geophys. Res. Biogeosci., 126.","DOI":"10.1029\/2021JG006586"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112102","DOI":"10.1016\/j.rse.2020.112102","article-title":"Terrestrial Laser Scanning in Forest Ecology: Expanding the Horizon","volume":"251","author":"Calders","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2989","DOI":"10.5194\/essd-14-2989-2022","article-title":"Individual Tree Point Clouds and Tree Measurements from Multi-Platform Laser Scanning in German Forests","volume":"14","author":"Weiser","year":"2022","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"118695","DOI":"10.1016\/j.foreco.2020.118695","article-title":"Airborne Lidar Provides Reliable Estimates of Canopy Base Height and Canopy Bulk Density in Southwestern Ponderosa Pine Forests","volume":"481","author":"Chamberlain","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_11","first-page":"162","article-title":"Forest Inventories by LiDAR Data: A Comparison of Single Tree Segmentation and Metric-Based Methods for Inventories of a Heterogeneous Temperate Forest","volume":"42","author":"Latifi","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","first-page":"360","article-title":"Estimation of Forest Structural and Compositional Variables Using ALS Data and Multi-Seasonal Satellite Imagery","volume":"78","author":"Shang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/LGRS.2013.2285471","article-title":"Breast Height Diameter Estimation from High-Density Airborne LiDAR Data","volume":"11","author":"Bucksch","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"337","DOI":"10.5721\/EuJRS20164919","article-title":"Effects of Forest Structure and Airborne Laser Scanning Point Cloud Density on 3D Delineation of Individual Tree Crowns","volume":"49","author":"Kandare","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2640","DOI":"10.1016\/j.rse.2011.05.020","article-title":"Strengths and Limitations of Assessing Forest Density and Spatial Configuration with Aerial LiDAR","volume":"115","author":"Richardson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2013.06.005","article-title":"Performance of Dense Digital Surface Models Based on Image Matching in the Estimation of Plot-Level Forest Variables","volume":"83","author":"Nurminen","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.ecolind.2018.08.011","article-title":"Detection of Forest Canopy Gaps from Very High Resolution Aerial Images","volume":"95","author":"Nyamgeroh","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"518","DOI":"10.3390\/f4030518","article-title":"The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning","volume":"4","author":"White","year":"2013","journal-title":"Forests"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3704","DOI":"10.3390\/f6103704","article-title":"Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment","volume":"6","author":"White","year":"2015","journal-title":"Forests"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/02827581.2014.961954","article-title":"Comparing Biophysical Forest Characteristics Estimated from Photogrammetric Matching of Aerial Images and Airborne Laser Scanning Data","volume":"30","author":"Gobakken","year":"2015","journal-title":"Scand. J. For. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cao, L., Liu, H., Fu, X., Zhang, Z., Shen, X., and Ruan, H. (2019). Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests. Forests, 10.","DOI":"10.3390\/f10020145"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mielcarek, M., Kami\u0144ska, A., and Stere\u0144czak, K. (2020). Digital Aerial Photogrammetry (DAP) and Airborne Laser Scanning (ALS) as Sources of Information about Tree Height: Comparisons of the Accuracy of Remote Sensing Methods for Tree Height Estimation. Remote Sens., 12.","DOI":"10.3390\/rs12111808"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1111\/nph.15517","article-title":"Terrestrial LiDAR: A Three-Dimensional Revolution in How We Look at Trees","volume":"222","author":"Disney","year":"2019","journal-title":"New Phytol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.isprsjprs.2019.09.015","article-title":"Comparison of Terrestrial LiDAR and Digital Hemispherical Photography for Estimating Leaf Angle Distribution in European Broadleaf Beech Forests","volume":"158","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.isprsjprs.2016.11.012","article-title":"Feasibility of Terrestrial Laser Scanning for Collecting Stem Volume Information from Single Trees","volume":"123","author":"Saarinen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112912","DOI":"10.1016\/j.rse.2022.112912","article-title":"Quantifying Tropical Forest Structure through Terrestrial and UAV Laser Scanning Fusion in Australian Rainforests","volume":"271","author":"Terryn","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2016.10.041","article-title":"Measurement of Fine-Spatial-Resolution 3D Vegetation Structure with Airborne Waveform Lidar: Calibration and Validation with Voxelised Terrestrial Lidar","volume":"188","author":"Hancock","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.rse.2013.05.012","article-title":"Integrating Terrestrial and Airborne Lidar to Calibrate a 3D Canopy Model of Effective Leaf Area Index","volume":"136","author":"Hopkinson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.agrformet.2013.09.005","article-title":"On Seeing the Wood from the Leaves and the Role of Voxel Size in Determining Leaf Area Distribution of Forests with Terrestrial LiDAR","volume":"184","author":"Baldocchi","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2013.04.011","article-title":"3-D Voxel-Based Solid Modeling of a Broad-Leaved Tree for Accurate Volume Estimation Using Portable Scanning Lidar","volume":"82","author":"Hosoi","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.rse.2007.06.011","article-title":"A Voxel-Based Lidar Method for Estimating Crown Base Height for Deciduous and Pine Trees","volume":"112","author":"Popescu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1016\/j.rse.2009.01.017","article-title":"The Structural and Radiative Consistency of Three-Dimensional Tree Reconstructions from Terrestrial Lidar","volume":"113","author":"Widlowski","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.rse.2017.01.032","article-title":"Estimation of 3D Vegetation Density with Terrestrial Laser Scanning Data Using Voxels. A Sensitivity Analysis of Influencing Parameters","volume":"191","author":"Grau","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103675","DOI":"10.1016\/j.autcon.2021.103675","article-title":"Voxel-Based Representation of 3D Point Clouds: Methods, Applications, and Its Potential Use in the Construction Industry","volume":"126","author":"Xu","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"113115","DOI":"10.1016\/j.rse.2022.113115","article-title":"Estimation of Vertical Plant Area Density from Single Return Terrestrial Laser Scanning Point Clouds Acquired in Forest Environments","volume":"279","author":"Nguyen","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2018.02.028","article-title":"Detecting and Quantifying Standing Dead Tree Structural Loss with Reconstructed Tree Models Using Voxelized Terrestrial Lidar Data","volume":"209","author":"Putman","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.rse.2018.06.024","article-title":"Estimators and Confidence Intervals for Plant Area Density at Voxel Scale with T-LiDAR","volume":"215","author":"Pimont","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s40663-020-00243-2","article-title":"Influence of Voxel Size on Forest Canopy Height Estimates Using Full-Waveform Airborne LiDAR Data","volume":"7","author":"Wang","year":"2020","journal-title":"For. Ecosyst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111355","DOI":"10.1016\/j.rse.2019.111355","article-title":"Non-Destructive Tree Volume Estimation through Quantitative Structure Modelling: Comparing UAV Laser Scanning with Terrestrial LIDAR","volume":"233","author":"Brede","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"491","DOI":"10.3390\/rs5020491","article-title":"Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data","volume":"5","author":"Raumonen","year":"2013","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1111\/2041-210X.12904","article-title":"Estimation of Above-Ground Biomass of Large Tropical Trees with Terrestrial LiDAR","volume":"9","author":"Lau","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1007\/s00468-018-1704-1","article-title":"Quantifying Branch Architecture of Tropical Trees Using Terrestrial LiDAR and 3D Modelling","volume":"32","author":"Lau","year":"2018","journal-title":"Trees\u2014Struct. Funct."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.isprsjprs.2020.08.009","article-title":"Tree Species Classification Using Structural Features Derived from Terrestrial Laser Scanning","volume":"168","author":"Terryn","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13021-018-0098-0","article-title":"Estimating Urban above Ground Biomass with Multi-Scale LiDAR","volume":"13","author":"Wilkes","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Fan, G., Nan, L., Dong, Y., Su, X., and Chen, F. (2020). AdQSM: A New Method for Estimating above-Ground Biomass from TLS Point Clouds. Remote Sens., 12.","DOI":"10.3390\/rs12183089"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"117751","DOI":"10.1016\/j.foreco.2019.117751","article-title":"Terrestrial Laser Scanning for Non-Destructive Estimates of Liana Stem Biomass","volume":"456","author":"Raumonen","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1093\/aob\/mcab111","article-title":"Terrestrial Laser Scanning: A New Standard of Forest Measuring and Modelling?","volume":"128","author":"Kaitaniemi","year":"2021","journal-title":"Ann. Bot."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bienert, A., Georgi, L., Kunz, M., Maas, H.G., and von Oheimb, G. (2018). Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories. Forests, 9.","DOI":"10.3390\/f9070395"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/2041-210X.12301","article-title":"Nondestructive Estimates of Above-Ground Biomass Using Terrestrial Laser Scanning","volume":"6","author":"Calders","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agrformet.2018.11.014","article-title":"Finite Element Analysis of Trees in the Wind Based on Terrestrial Laser Scanning Data","volume":"265","author":"Jackson","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_54","first-page":"47","article-title":"Analysing the Potential of UAV Point Cloud as Input in Quantitative Structure Modelling for Assessment of Woody Biomass of Single Trees","volume":"81","author":"Ye","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Song, G.M., Wang, Q., and Jin, J. (2020). Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest. Forests, 11.","DOI":"10.3390\/f11030318"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"10511","DOI":"10.1080\/10106049.2022.2037730","article-title":"Combining Both Spectral and Textural Indices for Alleviating Saturation Problem in Forest LAI Estimation Using Sentinel-2 Data","volume":"37","author":"Wang","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.isprsjprs.2023.01.013","article-title":"GlobalMatch: Registration of Forest Terrestrial Point Clouds by Global Matching of Relative Stem Positions","volume":"197","author":"Wang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","unstructured":"Hackenberg, J., Calders, K., Miro, D., Raumonen, P., Piboule, A., and Mathias, D. (2021). SimpleForest\u2014A Comprehensive Tool for 3d Reconstruction of Trees from Forest Plot Point Clouds. bioRxiv."},{"key":"ref_59","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"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2012.08.006","article-title":"Forest Variable Estimation Using a High-Resolution Digital Surface Model","volume":"74","author":"Pekkarinen","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.rse.2019.03.027","article-title":"Comparing the Accuracies of Forest Attributes Predicted from Airborne Laser Scanning and Digital Aerial Photogrammetry in Operational Forest Inventories","volume":"226","author":"Noordermeer","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.rse.2014.08.036","article-title":"Comparison of Four Types of 3D Data for Timber Volume Estimation","volume":"155","author":"Rahlf","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.02.002","article-title":"Comparison of Airborne Laser Scanning and Digital Stereo Imagery for Characterizing Forest Canopy Gaps in Coastal Temperate Rainforests","volume":"208","author":"White","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_64","first-page":"102943","article-title":"3D Modeling of Laser-Scanned Trees Based on Skeleton Refined Extraction","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Dong, Y., Fan, G., Zhou, Z., Liu, J., Wang, Y., and Chen, F. (2021). Low Cost Automatic Reconstruction of Tree Structure by Adqsm with Terrestrial Close-Range Photogrammetry. Forests, 12.","DOI":"10.3390\/f12081020"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2019.08.008","article-title":"Automated Fusion of Forest Airborne and Terrestrial Point Clouds through Canopy Density Analysis","volume":"156","author":"Dai","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Guo, L., Wu, Y., Deng, L., Hou, P., Zhai, J., and Chen, Y. (2023). A Feature-Level Point Cloud Fusion Method for Timber Volume of Forest Stands Estimation. Remote Sens., 15.","DOI":"10.3390\/rs15122995"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1109\/TGRS.2017.2675963","article-title":"A Novel Automatic Method for the Fusion of ALS and TLS LiDAR Data for Robust Assessment of Tree Crown Structure","volume":"55","author":"Paris","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/4\/697\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:00:44Z","timestamp":1760104844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/4\/697"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":68,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16040697"],"URL":"https:\/\/doi.org\/10.3390\/rs16040697","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,16]]}}}