文献类型：期刊文献

英文题名：A Method to Identify Dacrydium pierrei Hickel Using Unmanned Aerial Vehicle Multi-source Remote Sensing Data in a Chinese Tropical Rainforest

作者：Peng, Xi[1,2,4] Liu, Haodong[1,4] Chen, Yongfu[1,4] Chen, Qiao[1,4] Wang, Juan[1,3,4] Li, Huayu[1,3,4] Zhao, Anjiu[2]

第一作者：Peng, Xi

通信作者：Chen, Q[1];Chen, Q[2]|[a0005e604c56678d970cd]陈巧;

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Sichuan Aricultural Univ, Coll Forestry, Chengdu 611130, Sichuan, Peoples R China;[3]Southwest Forestry Univ, Coll Forestry, Kunming 650224, Yunnan, Peoples R China;[4]NFGA, Key Lab Forestry Remote Sensing & Informat Syst, Beijing, Peoples R China

年份：2022

卷号：50

期号：1

起止页码：25-35

外文期刊名：JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING

收录：;Scopus(收录号：2-s2.0-85118898370);WOS:【SCI-EXPANDED(收录号:WOS:000717373000001)】；

基金：This work was supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (No. CAFBB2017ZB004) and the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (No. CAFYBB2020GC006).

语种：英文

外文关键词：Unmanned aerial vehicles (UAV); Tree species identification; Feature selection; Random forest; Multi-source remote sensing data; Chinese tropical rainforest

摘要：Identifying special species in tropical forests is an important topic in forest resource management, and the use of a single type of remote sensing data for identification of species has limited accuracy. To analyze the ability of various unmanned aerial vehicle (UAV) remote sensing data for identifying target species, this study used three types of UAV remote sensing data (light detection and ranging (LiDAR), red, green, blue (RGB), and multispectral) to identify Dacrydium pierrei Hickel (D. pierrei) in Chinese tropical forests. The study compared the effects of using various combinations of UAV remote sensing data on the accuracy of D. pierrei identification and identified the optimal combination. (1) Random forest feature selection improved the accuracy of identification of D. pierrei by UAV multiple source remote sensing data: The producer accuracy (PA) was increased up to by 4.62%. (2) The following eight features were most useful for identifying D. pierrei: four features from multispectral images (DR_Standard, RE_Standard, DR_Mean, and B_Brightne), two features from RGB images (B_Standard and B_Mean), and two features from LiDAR images (INT_kurtosis and INT_aad). (3) Combining remote sensing data by integrating up to three types of data sources improved the accuracy of D. pierrei identification. When using a single type of remote sensing data, multispectral data gave the highest identification accuracy. When combining two types of remote sensing data, RGB and multispectral data achieved the best overall effect, and the highest overall identification accuracy, of more than 90%, was obtained by combining three types of remote sensing data.