{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T23:27:25Z","timestamp":1775777245047,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Central Public interest Scientific Institution Basal Research Fund under","award":["CAFYBB2019QD003"],"award-info":[{"award-number":["CAFYBB2019QD003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The survival rate of seedlings is a decisive factor of afforestation assessment. Generally, ground checking is more accurate than any other methods. However, the survival rate of seedlings can be higher in the growing season, and this can be estimated in a larger area at a relatively lower cost by extracting the tree crown from the unmanned aerial vehicle (UAV) images, which provides an opportunity for monitoring afforestation in an extensive area. At present, studies on extracting individual tree crowns under the complex ground vegetation conditions are limited. Based on the afforestation images obtained by airborne consumer-grade cameras in central China, this study proposes a method of extracting and fusing multiple radii morphological features to obtain the potential crown. A random forest (RF) was used to identify the regions extracted from the images, and then the recognized crown regions were fused selectively according to the distance. A low-cost individual crown recognition framework was constructed for rapid checking of planted trees. The method was tested in two afforestation areas of 5950 m2 and 5840 m2, with a population of 2418 trees (Koelreuteria) in total. Due to the complex terrain of the sample plot, high weed coverage, the crown width of trees, and spacing of saplings vary greatly, which increases both the difficulty and complexity of crown extraction. Nevertheless, recall and F-score of the proposed method reached 93.29%, 91.22%, and 92.24% precisions, respectively, and 2212 trees were correctly recognized and located. The results show that the proposed method is robust to the change of brightness and to splitting up of a multi-directional tree crown, and is an automatic solution for afforestation verification.<\/jats:p>","DOI":"10.3390\/rs13204122","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T23:02:16Z","timestamp":1634252536000},"page":"4122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Tree Recognition on the Plantation Using UAV Images with Ultrahigh Spatial Resolution in a Complex Environment"],"prefix":"10.3390","volume":"13","author":[{"given":"Xuzhan","family":"Guo","sequence":"first","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2339-6223","authenticated-orcid":false,"given":"Qingwang","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8623-6211","authenticated-orcid":false,"given":"Ram P.","family":"Sharma","sequence":"additional","affiliation":[{"name":"Institute of Forestry, Tribhuwan University, Kirtipur 44600, Nepal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6458-8742","authenticated-orcid":false,"given":"Qiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}]},{"given":"Qiaolin","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Shouzheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}]},{"given":"Liyong","family":"Fu","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10584-014-1127-y","article-title":"Thinking globally and siting locally\u2014Renewable energy and biodiversity in a rapidly warming world","volume":"126","author":"Allison","year":"2014","journal-title":"Clim. 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