文献类型： 外文期刊

作者： Lei, Lei ¹ ; Li, Zhenhong ³ ; Yang, Guijun ⁵ ; Yang, Hao ⁵ ;

作者机构： 1.Changan Univ, Coll Geol Engn & Geomat, Key Lab Loess, Xian 710054, Peoples R China

2.Changan Univ, Big Data Ctr Geosci & Satellites BDCGS, Xian 710054, Peoples R China

3.Changan Univ, Coll Geol Engn & Geomat, Big Data Ctr Geosci & Satellites BDCGS, Key Lab Loess,Minist Educ,Key Lab Western Chinas M, Xian 710054, Peoples R China

4.Changan Univ, Key Lab Ecol Geol & Disaster Prevent, Minist Nat Resources, Xian 710054, Peoples R China

5.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

6.Key Lab Quantitat Remote Sensing Agr Minist Agr &, Beijing 100097, Peoples R China

7.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China

关键词： Data mining; Crops; Skeleton; Point cloud compression; Geoengineering; Forestry; Image reconstruction; Different cultivars; different growth stages; different planting densities; distributions of leaf base and inclination angles; light interception capacity; terrestrial laser {scanning (TLS)}

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：7.5； 五年影响因子：7.6 ）

ISSN： 0196-2892

年卷期： 2023 年 61 卷

页码：

收录情况： SCI

摘要： Distributions of leaf base and inclination angles are important crop phenotypic traits, influencing light interception and productivity. Light detection and ranging (LiDAR), and especially terrestrial laser scanning (TLS), provides unprecedented detail of the 3-D structure of the crop canopy. Recent research mainly focuses on the leaf base and inclination angles of maize at the individual level or lower planting density. It is difficult to extract the distributions of leaf base and inclination angles of maize in the field due to the interlocked and overlapped nature of leaves. In this study, we have proposed a high-throughput method to extract the distributions of leaf base and inclination angles of maize in the field. Following the separation of the leaf and stem of maize, hollow cylinders with different thicknesses were used to extract the local leaf points from the separated leaf points based on each stem fit line, and the density-based spatial clustering of applications with noise (DBSCAN) algorithm and singular value decomposition were used to calculate the leaf base and inclination angles. The distributions of leaf base and inclination angles of maize in the field with different cultivars [Jingjiuqingchu 16 (A1), Tianci 19 (A2), Jingnuo 2008 (A3), Nongkenuo 336 (A4), and Zhengdan 958 (A5)], planting densities (3.32, 4.65, 6.64, and 8.63 plants/m(2)), and growth stages (jointing, silking, and filling stages) were extracted and analyzed, and these performed well against the validation data. In addition to TLS data, the extraction of the distributions of leaf base and inclination angles based on LiBackpack and unmanned aerial vehicle (UAV)-LiDAR data was also discussed. This further validated the potential of the method proposed in this study for the extraction of the distributions of leaf base and inclination angles of maize in the field. Furthermore, the relationship between maize with different leaf base angle distributions and the daily cumulative absorbed photosynthetically active radiation (APAR) was analyzed, which demonstrated that compact maize cultivars exhibited higher light interception capabilities than scattered ones under high planting densities. The distributions of leaf base and inclination angles exert a substantial influence on the light interception capacity of maize, thereby exerting a consequential effect on maize yield. The high-throughput extraction of these distributions in maize fields holds significant importance for studying the optimal maize cultivar in conjunction with radiative transfer models.