干旱区生态环境知识资源中心(Arid): Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods

Arid

DOI	10.3390/rs14020415
	Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods
	Ilniyaz, Osman; Kurban, Alishir; Du, Qingyun
通讯作者	Du, QY
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:2
英文摘要	The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R-2 and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively; those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework.
英文关键词	leaf area index (LAI) light extinction coefficient unmanned aerial vehicles (UAV) multispectral data RGB data machine learning pergola-trained vineyards
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000746005900001
WOS关键词	AERIAL VEHICLE UAV ; PHOTOSYNTHETICALLY ACTIVE RADIATION ; SIZE ANALYSIS THEORY ; VEGETATION INDEXES ; SPECTRAL REFLECTANCE ; CHLOROPHYLL CONTENT ; COVER PHOTOGRAPHY ; CANOPY ; LAI ; IMAGERY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376799
推荐引用方式 GB/T 7714	Ilniyaz, Osman,Kurban, Alishir,Du, Qingyun. Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods[J],2022,14(2).
APA	Ilniyaz, Osman,Kurban, Alishir,&Du, Qingyun.(2022).Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods.REMOTE SENSING,14(2).
MLA	Ilniyaz, Osman,et al."Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods".REMOTE SENSING 14.2(2022).