{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T23:54:47Z","timestamp":1772150087256,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T00:00:00Z","timestamp":1631836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018M1A3A3A02066008"],"award-info":[{"award-number":["NRF-2018M1A3A3A02066008"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018R1D1A1B07041203"],"award-info":[{"award-number":["NRF-2018R1D1A1B07041203"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The role of forests is increasing because of rapid land use changes worldwide that have implications on ecosystems and the carbon cycle. Therefore, it is necessary to obtain accurate information about forests and build forest inventories. However, it is difficult to assess the internal structure of the forest through 2D remote sensing techniques and fieldwork. In this aspect, we proposed a method for estimating the vertical structure of forests based on full-waveform light detection and ranging (FW LiDAR) data in this study. Voxel-based tree point density maps were generated by estimating the number of canopy height points in each voxel grid from the raster digital terrain model (DTM) and canopy height points after pre-processing the LiDAR point clouds. We applied an unsupervised classification algorithm to the voxel-based tree point density maps and identified seven classes by profile pattern analysis for the forest vertical types. The classification accuracy was found to be 72.73% from the validation from 11 field investigation sites, which was additionally confirmed through comparative analysis with aerial images. Based on this pre-classification reference map, which is assumed to be ground truths, the deep neural network (DNN) model was finally applied to perform the final classification. As a result of accuracy assessment, it showed accuracy of 92.72% with a good performance. These results demonstrate the potential of vertical structure estimation for extensive forests using FW LiDAR data and that the distinction between one-storied and two-storied forests can be clearly represented. This technique is expected to contribute to efficient and effective management of forests based on accurate information derived from the proposed method.<\/jats:p>","DOI":"10.3390\/rs13183736","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T03:47:35Z","timestamp":1632282455000},"page":"3736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3217-8466","authenticated-orcid":false,"given":"Sung-Hwan","family":"Park","sequence":"first","affiliation":[{"name":"Marine Disaster Research Center, Korea Institute of Ocean Science & Technology, Busan 49111, Korea"},{"name":"Department of Geoinformatics, University of Seoul, Seoul 02504, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2335-8438","authenticated-orcid":false,"given":"Hyung-Sup","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics, University of Seoul, Seoul 02504, Korea"},{"name":"Department of Smart Cities, University of Seoul, Seoul 02504, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5202-8721","authenticated-orcid":false,"given":"Sunmin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics, University of Seoul, Seoul 02504, Korea"},{"name":"Center for Environmental Assessment Monitoring, Korea Environment Institute (KEI), Sejong-si 30147, Korea"}]},{"given":"Eun-Sook","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Forest Ecology, National Institute of Forest Science, Seoul 02455, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"86","DOI":"10.2307\/1311969","article-title":"Assessing water quality with submersed aquatic vegetation: Habitat requirements as barometers of Chesapeake Bay health","volume":"43","author":"Dennison","year":"1993","journal-title":"BioScience"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1461-0248.2009.01294.x","article-title":"Prospects for tropical forest biodiversity in a human-modified world","volume":"12","author":"Gardner","year":"2009","journal-title":"Ecol. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1111\/j.1526-100X.2005.00072.x","article-title":"Restoration success: How is it being measured?","volume":"13","year":"2005","journal-title":"Restor. Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1046\/j.0305-0270.2003.00994.x","article-title":"Animal species diversity driven by habitat heterogeneity\/diversity: The importance of keystone structures","volume":"31","author":"Tews","year":"2004","journal-title":"J. Biogeogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1111\/1442-1984.12020","article-title":"Effects of forest successional status on microenvironmental conditions, diversity, and distribution of filmy fern species in a temperate rainforest","volume":"29","author":"Parra","year":"2014","journal-title":"Plant Species Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s10342-017-1056-1","article-title":"Effect of forest stand management on species composition, structural diversity, and productivity in the temperate zone of Europe","volume":"136","author":"Dieler","year":"2017","journal-title":"Eur. J. For. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1111\/j.1442-9993.2007.01750.x","article-title":"Habitat structure is more important than vegetation composition for local-level management of native terrestrial reptile and small mammal species living in urban remnants: A case study from Brisbane, Australia","volume":"32","author":"Garden","year":"2007","journal-title":"Austral Ecol."},{"key":"ref_8","unstructured":"Anderson, H.W., Hoover, M.D., and Reinhart, K.G. (1976). Forests and Water: Effects of Forest Management on Floods, Sedimentation, and Water Supply."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1126\/science.1155121","article-title":"Forests and climate change: Forcings, feedbacks, and the climate benefits of forests","volume":"320","author":"Bonan","year":"2008","journal-title":"Science"},{"key":"ref_10","first-page":"429","article-title":"Potential and economic efficiency of carbon sequestration in forest biomass through silvicultural management","volume":"40","author":"Hoen","year":"1994","journal-title":"For. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"045023","DOI":"10.1088\/1748-9326\/2\/4\/045023","article-title":"Monitoring and estimating tropical forest carbon stocks: Making REDD a reality","volume":"2","author":"Gibbs","year":"2007","journal-title":"Environ. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s13595-011-0040-z","article-title":"Review of ground-based methods to measure the distribution of biomass in forest canopies","volume":"68","author":"Seidel","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_13","first-page":"2","article-title":"Allometric Models for Tree Volume and Total Aboveground Biomass in a Tropical Humid Forest in Costa Rica 1","volume":"37","author":"Segura","year":"2005","journal-title":"Biotropica J. Biol. Conserv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lee, Y.-S., Lee, S., and Jung, H.-S. (2020). Mapping forest vertical structure in Gong-ju, Korea using Sentinel-2 satellite images and artificial neural networks. Appl. Sci., 10.","DOI":"10.3390\/app10051666"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, Y.-S., Lee, S., Baek, W.-K., Jung, H.-S., Park, S.-H., and Lee, M.-J. (2020). Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12050797"},{"key":"ref_17","first-page":"44","article-title":"Lidar remote sensing for forestry","volume":"98","author":"Dubayah","year":"2000","journal-title":"J. For."},{"key":"ref_18","unstructured":"Korea Forest Service (2015). Forest Basic Statistics."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jhydrol.2018.04.003","article-title":"Effects of forest structure on hydrological processes in China","volume":"561","author":"Sun","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bergen, K., Goetz, S., Dubayah, R., Henebry, G., Hunsaker, C., Imhoff, M., Nelson, R., Parker, G., and Radeloff, V. (2009). Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. J. Geophys. Res. Biogeosci., 114.","DOI":"10.1029\/2008JG000883"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2012.02.001","article-title":"Lidar sampling for large-area forest characterization: A review","volume":"121","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0034-4257(03)00139-1","article-title":"Characterizing vertical forest structure using small-footprint airborne LiDAR","volume":"87","author":"Zimble","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13021-015-0013-x","article-title":"Airborne lidar-based estimates of tropical forest structure in complex terrain: Opportunities and trade-offs for REDD+","volume":"10","author":"Leitold","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2006.03.003","article-title":"Assessment of forest structure with airborne LiDAR and the effects of platform altitude","volume":"103","author":"Goodwin","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.rse.2012.06.024","article-title":"Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest","volume":"124","author":"Wing","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"152","article-title":"Exploring full-waveform LiDAR parameters for tree species classification","volume":"13","author":"Heinzel","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"68716","DOI":"10.1109\/ACCESS.2018.2880083","article-title":"Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping","volume":"6","author":"Liao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.ecolind.2017.02.045","article-title":"Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest","volume":"78","author":"Nie","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1191\/0309133303pp360ra","article-title":"LiDAR remote sensing of forest structure","volume":"27","author":"Lim","year":"2003","journal-title":"Prog. Phys. Geogr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.rse.2015.07.027","article-title":"Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables","volume":"173","author":"Sumnall","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7110","DOI":"10.3390\/rs6087110","article-title":"Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests","volume":"6","author":"Cao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wagner, F.H., Dalagnol, R., Tagle Casapia, X., Streher, A.S., Phillips, O.L., Gloor, E., and Arag\u00e3o, L.E. (2020). Regional mapping and spatial distribution analysis of canopy palms in an amazon forest using deep learning and VHR images. Remote Sens., 12.","DOI":"10.3390\/rs12142225"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Narine, L.L., Popescu, S.C., and Malambo, L. (2019). Synergy of ICESat-2 and Landsat for mapping forest aboveground biomass with deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11121503"},{"key":"ref_35","unstructured":"Lv, Q., Dou, Y., Niu, X., Xu, J., and Li, B. (2014, January 13\u201318). Classification of land cover based on deep belief networks using polarimetric RADARSAT-2 data. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hamdi, Z.M., Brandmeier, M., and Straub, C. (2019). Forest damage assessment using deep learning on high resolution remote sensing data. Remote Sens., 11.","DOI":"10.3390\/rs11171976"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"188","DOI":"10.13047\/KJEE.2017.31.2.188","article-title":"Vegetation Composition and Structure of Sogwang-ri Forest Genetic Resources Reserve in Uljin-gun, Korea","volume":"31","author":"Kim","year":"2017","journal-title":"Korean J. Environ. Ecol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10","DOI":"10.5532\/KJAFM.2017.19.1.10","article-title":"Topographic and meteorological characteristics of Pinus densiflora dieback areas in Sogwang-ri, Uljin","volume":"19","author":"Kim","year":"2017","journal-title":"Korean J. Agric. For. Meteorol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1080\/01431160903380565","article-title":"LiDAR mapping of canopy gaps in continuous cover forests: A comparison of canopy height model and point cloud based techniques","volume":"31","author":"Gaulton","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","unstructured":"Terrasolid (2004). TerraScan User\u2019s Guide, Terrasolid."},{"key":"ref_41","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_42","unstructured":"(2021, August 25). Creation and Management of Forest Resources Act. 1. Available online: https:\/\/elaw.klri.re.kr\/kor_service\/lawView.do?hseq=51186&lang=ENG."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Richards, J. (1999). Remote Sensing Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-662-03978-6"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/s41583-020-0277-3","article-title":"Backpropagation and the brain","volume":"21","author":"Lillicrap","year":"2020","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Referowska-Chodak, E. (2019). Pressures and threats to nature related to human activities in European urban and suburban forests. Forests, 10.","DOI":"10.3390\/f10090765"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3736\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:01:27Z","timestamp":1760166087000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,17]]},"references-count":45,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183736"],"URL":"https:\/\/doi.org\/10.3390\/rs13183736","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,17]]}}}