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From canopy complementarity to asymmetric competition: The negative relationship between structural diversity and productivity during succession 期刊论文
JOURNAL OF ECOLOGY, 2022, 卷号: 110, 期号: 2, 页码: 457-465
作者:  Yi, Xiaoxia;  Wang, Ningning;  Ren, Haibao;  Yu, Jianping;  Hu, Tianyu;  Su, Yanjun;  Mi, Xiangcheng;  Guo, Qinghua;  Ma, Keping
Adobe PDF(927Kb)  |  收藏  |  浏览/下载：16/0  |  提交时间：2024/03/07
asymmetric competition  canopy structural diversity  coarse woody productivity  community composition  disturbance regime  leaf area index  species diversity  
Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 卷号: 58, 期号: 4, 页码: 2644-2658
作者:  Jin, Shichao;  Su, Yanjun;  Gao, Shang;  Wu, Fangfang;  Ma, Qin;  Xu, Kexin;  Hu, Tianyu;  Liu, Jin;  Pang, Shuxin;  Guan, Hongcan;  Zhang, Jing;  Guo, Qinghua
Adobe PDF(14928Kb)  |  收藏  |  浏览/下载：138/0  |  提交时间：2022/03/01
Classification  deep learning  LiDAR  phenotype  segmentation  structural components  
Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 卷号: 57, 期号: 3, 页码: 1336-1346
作者:  Jin, Shichao;  Su, Yanjun;  Wu, Fangfang;  Pang, Shuxin;  Gao, Shang;  Hu, Tianyu;  Liu, Jin;  Guo, Qinghua
Adobe PDF(7737Kb)  |  收藏  |  浏览/下载：113/0  |  提交时间：2022/01/06
Light detection and ranging (LiDAR)  phenotypic traits  regional growth  segmentation  skeleton  
Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms 期刊论文
FRONTIERS IN PLANT SCIENCE, 2018, 卷号: 9
作者:  Jin, Shichao;  Su, Yanjun;  Gao, Shang;  Wu, Fangfang;  Hu, Tianyu;  Liu, Jin;  Li, Wankai;  Wang, Dingchang;  Chen, Shaojiang;  Jiang, Yuanxi;  Pang, Shuxin;  Guo, Qinghua
Adobe PDF(2278Kb)  |  收藏  |  浏览/下载：88/0  |  提交时间：2022/02/25
deep learning  detection  classification  segmentation  phenotype  Lidar (light detection and ranging)