{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T23:20:17Z","timestamp":1768260017728,"version":"3.49.0"},"reference-count":66,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T00:00:00Z","timestamp":1647734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Quanfa Zhang","award":["National Natural Science Foundation of China No. 32030069"],"award-info":[{"award-number":["National Natural Science Foundation of China No. 32030069"]}]},{"name":"Dezhi Wang","award":["National Natural Science Foundation of China No. 32101525"],"award-info":[{"award-number":["National Natural Science Foundation of China No. 32101525"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-cost data collection and processing are challenges for UAV LiDAR (light detection and ranging) mounted on unmanned aerial vehicles in crop monitoring. Reducing the point density can lower data collection costs and increase efficiency but may lead to a loss in mapping accuracy. It is necessary to determine the appropriate point cloud density for tea plucking area identification to maximize the cost\u2013benefits. This study evaluated the performance of different LiDAR and photogrammetric point density data when mapping the tea plucking area in the Huashan Tea Garden, Wuhan City, China. The object-based metrics derived from UAV point clouds were used to classify tea plantations with the extreme learning machine (ELM) and random forest (RF) algorithms. The results indicated that the performance of different LiDAR point density data, from 0.25 (1%) to 25.44 pts\/m2 (100%), changed obviously (overall classification accuracies: 90.65\u201394.39% for RF and 89.78\u201393.44% for ELM). For photogrammetric data, the point density was found to have little effect on the classification accuracy, with 10% of the initial point density (2.46 pts\/m2), a similar accuracy level was obtained (difference of approximately 1%). LiDAR point cloud density had a significant influence on the DTM accuracy, with the RMSE for DTMs ranging from 0.060 to 2.253 m, while the photogrammetric point cloud density had a limited effect on the DTM accuracy, with the RMSE ranging from 0.256 to 0.477 m due to the high proportion of ground points in the photogrammetric point clouds. Moreover, important features for identifying the tea plucking area were summarized for the first time using a recursive feature elimination method and a novel hierarchical clustering-correlation method. The resultant architecture diagram can indicate the specific role of each feature\/group in identifying the tea plucking area and could be used in other studies to prepare candidate features. This study demonstrates that low UAV point density data, such as 2.55 pts\/m2 (10%), as used in this study, might be suitable for conducting finer-scale tea plucking area mapping without compromising the accuracy.<\/jats:p>","DOI":"10.3390\/rs14061505","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"1505","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Effects of UAV-LiDAR and Photogrammetric Point Density on Tea Plucking Area Identification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3727-3294","authenticated-orcid":false,"given":"Qingfan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}]},{"given":"Maosheng","family":"Hu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"},{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yansong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Geological Environmental Center of Hubei Province, Wuhan 430034, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2387-5419","authenticated-orcid":false,"given":"Bo","family":"Wan","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"},{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Le","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Quanfa","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9454-8314","authenticated-orcid":false,"given":"Dezhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s12524-019-01014-5","article-title":"Mapping tea plantations from multi-seasonal Landsat-8 OLI imageries using a random forest classifier","volume":"47","author":"Wang","year":"2019","journal-title":"J. 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