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Multiscale Inversion of Leaf Area Index in Citrus Tree by Merging UAV LiDAR with Multispectral Remote Sensing Data

文献类型： 外文期刊

作者： Xu, Weicheng 2 ; Yang, Feifan 1 ; Ma, Guangchao 1 ; Wu, Jinhao 1 ; Wu, Jiapei 1 ; Lan, Yubin 1 ;

作者机构： 1.South China Agr Univ, Coll Elect Engn, Guangzhou 510642, Peoples R China

2.Guangdong Acad Agr Sci, Rice Res Inst, Guangzhou 510640, Peoples R China

3.Guangdong Lab Lingnan Modern Agr, Guangzhou 510642, Peoples R China

4.Natl Ctr Int Collaborat Precis Agr Aviat Pesticide, Guangzhou 510642, Peoples R China

5.Texas A&M Univ, Dept Biol & Agr Engn, College Stn, TX 77843 USA

关键词： UAV LiDAR; UAV multispectral; leaf area index

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2023 年 13 卷 11 期

页码：

收录情况： SCI

摘要： The LAI (leaf area index) is an important parameter describing the canopy structure of citrus trees and characterizing plant photosynthesis, as well as providing an important basis for selecting parameters for orchard plant protection operations. By fusing LiDAR data with multispectral data, it can make up for the lack of rich spatial features of multispectral data, thus obtaining higher LAI inversion accuracy. This study proposed a multiscale LAI inversion method for citrus orchard based on the fusion of point cloud data and multispectral data. By comparing various machine learning algorithms, the mapping relationship between the characteristic parameters in multispectral data and point cloud data and citrus LAI was established, and we established the inversion model based on this, by removing redundant features through redundancy analysis. The experiment results showed that the BP neural network performs the best at both the community scale and the individual scale. After removing redundant features, the R2, RMSE, and MAE of the BP neural network at the community scale and individual scale were 0.896, 0.112, 0.086, and 0.794, 0.408, 0.328, respectively. By adding the three-dimensional gap fraction feature to the two-dimensional vegetation index features, the R2 at community scale and individual scale increased by 4.43% and 7.29%, respectively. The conclusion of this study suggests that the fusion of point cloud and multispectral data exhibits superior accuracy in multiscale citrus LAI inversion compared to relying solely on a single data source. This study proposes a fast and efficient multiscale LAI inversion method for citrus, which provides a new idea for the orchard precise management and the precision of plant protection operation.

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