{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T05:34:41Z","timestamp":1773034481420,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T00:00:00Z","timestamp":1696032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFF1302700"],"award-info":[{"award-number":["2022YFF1302700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["202303"],"award-info":[{"award-number":["202303"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["QNTD202308"],"award-info":[{"award-number":["QNTD202308"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Emergency Open Competition Project of National Forestry and Grassland Administration","award":["2022YFF1302700"],"award-info":[{"award-number":["2022YFF1302700"]}]},{"name":"The Emergency Open Competition Project of National Forestry and Grassland Administration","award":["202303"],"award-info":[{"award-number":["202303"]}]},{"name":"The Emergency Open Competition Project of National Forestry and Grassland Administration","award":["QNTD202308"],"award-info":[{"award-number":["QNTD202308"]}]},{"name":"Outstanding Youth Team Project of Central Universities","award":["2022YFF1302700"],"award-info":[{"award-number":["2022YFF1302700"]}]},{"name":"Outstanding Youth Team Project of Central Universities","award":["202303"],"award-info":[{"award-number":["202303"]}]},{"name":"Outstanding Youth Team Project of Central Universities","award":["QNTD202308"],"award-info":[{"award-number":["QNTD202308"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The vertical structure of forest ecosystems influences and reflects ecosystem functioning. Terrestrial laser scanning (TLS) enables the rapid acquisition of 3D forest information and subsequent reconstruction of the vertical structure, which provides new support for acquiring forest vertical structure information. We focused on artificial forest sample plots in the north-central of Nanning, Guangxi, China as the research area. Forest sample point cloud data were obtained through TLS. By accurately capturing the gradient information of the forest vertical structure, a classification boundary was delineated. A complex forest vertical structure segmentation method was proposed based on the Forest-PointNet model. This method comprehensively utilized the spatial and shape features of the point cloud. The study accurately segmented four types of vertical structure features in the forest sample location cloud data: ground, bushes, trunks, and leaves. With optimal training, the average classification accuracy reaches 90.98%. The results indicated that segmentation errors are mainly concentrated at the branch intersections of the canopy. Our model demonstrates significant advantages, including effective segmentation of vertical structures, strong generalization ability, and feature extraction capability.<\/jats:p>","DOI":"10.3390\/rs15194793","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T04:28:08Z","timestamp":1696220888000},"page":"4793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhibin","family":"Ma","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3129-0819","authenticated-orcid":false,"given":"Yanqi","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Jiali","family":"Zi","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Fu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1000-8455","authenticated-orcid":false,"given":"Feixiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107011","DOI":"10.1016\/j.ecolind.2020.107011","article-title":"Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests","volume":"121","author":"Li","year":"2021","journal-title":"Ecol. 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