Estimating Effective Leaf Area Index of Winter Wheat Based on UAV Point Cloud Data

Jie Yang; Minfeng Xing; Qiyun Tan; Jiali Shang; Yang Song; Xiliang Ni; Jinfei Wang; Min Xu

doi:10.3390/drones7050299

Drones (May 2023)

Estimating Effective Leaf Area Index of Winter Wheat Based on UAV Point Cloud Data

Jie Yang,
Minfeng Xing,
Qiyun Tan,
Jiali Shang,
Yang Song,
Xiliang Ni,
Jinfei Wang,
Min Xu

Affiliations

Jie Yang: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Minfeng Xing: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Qiyun Tan: Beijing Yuhang Intelligent Technology Co., Ltd., Beijing 100193, China
Jiali Shang: Agriculture and Agri-Food Canada, Ottawa, ON K1A0C6, Canada
Yang Song: Intelligent Agriculture Research Institute, Zoomlion Smart Agriculture, Changsha 410013, China
Xiliang Ni: Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Jinfei Wang: Department of Geography and Environment, University of Western Ontario, London, ON N6A5C2, Canada
Min Xu: The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

DOI: https://doi.org/10.3390/drones7050299
Journal volume & issue: Vol. 7, no. 5
p. 299

Abstract

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Leaf area index (LAI) is a widely used plant biophysical parameter required for modelling plant photosynthesis and crop yield estimation. UAV remote sensing plays an increasingly important role in providing the data source needed for LAI extraction. This study proposed a UAV-derived 3-D point cloud-based method to automatically calculate crop-effective LAI (LAIe). In this method, the 3-D winter wheat point cloud data filtered out of bare ground points was projected onto a hemisphere, and then the gap fraction was calculated through the hemispherical image obtained by projecting the sphere onto a plane. A single-angle inversion method and a multi-angle inversion method were used, respectively, to calculate the LAIe through the gap fraction. The results show a good linear correlation between the calculated LAIe and the field LAIe measured by the digital hemispherical photography method. In particular, the multi-angle inversion method of stereographic projection achieved the highest accuracy, with an R2 of 0.63. The method presented in this paper performs well in LAIe estimation of the main leaf development stages of the winter wheat growth cycle. It offers an effective means for mapping crop LAIe without the need for reference data, which saves time and cost.

Published in Drones

ISSN: 2504-446X (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Motor vehicles. Aeronautics. Astronautics
Website: http://www.mdpi.com/journal/drones

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