文献类型：期刊文献

英文题名：Tree Recognition on the Plantation Using UAV Images with Ultrahigh Spatial Resolution in a Complex Environment

作者：Guo, Xuzhan[1,2,3] Liu, Qingwang[1,3] Sharma, Ram P.[4] Chen, Qiao[1,3] Ye, Qiaolin[5] Tang, Shouzheng[1,3] Fu, Liyong[1,3]

第一作者：Guo, Xuzhan

通信作者：Fu, LY[1];Fu, LY[2]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Xinyang Normal Univ, Sch Comp & Informat Technol, Xinyang 464000, Peoples R China;[3]Natl Forestry & Grassland Adm, Key Lab Forest Management & Growth Modeling, Beijing 100091, Peoples R China;[4]Tribhuwan Univ, Inst Forestry, Kirtipur 44600, Nepal;[5]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China

年份：2021

卷号：13

期号：20

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20214311054896);Scopus(收录号：2-s2.0-85117423139);WOS:【SCI-EXPANDED(收录号:WOS:000715283100001)】；

基金：FundingThis research was funded by the Central Public Interest Scientific Institution Basal Research Fund under (Grant No. CAFYBB2019QD003).

语种：英文

外文关键词：individual trees crown; multi-radius extraction; chromatic mapping; feature extraction; complex environment; spectral index

摘要：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 m(2) and 5840 m(2), 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.