{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T10:33:23Z","timestamp":1718620403730},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2021YFB3901305"]},{"name":"National Natural Science Foundation of China","award":["U2244230","42171369"]},{"name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","award":["Y2022051"]},{"name":"Director Fund of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022DF012"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurate individual-tree segmentation is essential for precision forestry. In previous studies, the canopy height model-based method was convenient to process, but its performance was limited owing to the loss of 3D information, and point-based methods usually had high computational costs. Although some hybrid methods have been proposed to solve the above problems, most canopy height model-based methods are used to detect subdominant trees in one coarse crown and disregard the over-segmentation and accurate segmentation of the crown boundaries. This study introduces a combined approach, tested for the first time, for treetop detection and tree crown segmentation using UAV\u2013LiDAR data. First, a multiscale adaptive local maximum filter was proposed to detect treetops accurately, and a Dalponte region-growing method was introduced to achieve crown delineation. Then, based on the coarse-crown result, the mean-shift voxelization and supervoxel-weighted fuzzy c-means clustering method were used to identify the constrained region of each tree. Finally, accurate individual-tree point clouds were obtained. The experiment was conducted using a synthetic uncrewed aerial vehicle (UAV)\u2013LiDAR dataset with 21 approximately 30 \u00d7 30 m plots and an actual UAV\u2013LiDAR dataset. To evaluate the performance of the proposed method, the accuracy of the remotely sensed biophysical observations and retrieval frameworks was determined using the tree location, tree height, and crown area. The results show that the proposed method was efficient and outperformed other existing methods.<\/jats:p>","DOI":"10.3390\/rs16040608","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T10:36:43Z","timestamp":1707215803000},"page":"608","source":"Crossref","is-referenced-by-count":1,"title":["Individual-Tree Segmentation from UAV\u2013LiDAR Data Using a Region-Growing Segmentation and Supervoxel-Weighted Fuzzy Clustering Approach"],"prefix":"10.3390","volume":"16","author":[{"given":"Yuwen","family":"Fu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yifang","family":"Niu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2929-4255","authenticated-orcid":false,"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Wang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1038\/s41893-022-01020-5","article-title":"Human Fingerprint on Structural Density of Forests Globally","volume":"6","author":"Li","year":"2023","journal-title":"Nat. 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