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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Research » Research » Publications at this Location » Publication #328115

Research Project: Exotic and Emerging Plant Diseases of Horticultural Crops

Location: Horticultural Crops Research

Title: Rapid measurement of the three-dimensional distribution of leaf orientation and the leaf angle probability density function using terrestrial LiDAR scanning

Author
item Bailey, Brian
item Mahaffee, Walter - Walt

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2017
Publication Date: 3/12/2017
Citation: Bailey, B.N., Mahaffee, W.F. 2017. Rapid measurement of the three-dimensional distribution of leaf orientation and the leaf angle probability density function using terrestrial LiDAR scanning. Remote Sensing of Environment. 194(2017):63-76. doi: 10.1016/j.rse.2017.03.011.

Interpretive Summary: Leaf orientation plays a fundamental role in many transport processes in plant canopies. At the plant or stand level, leaf orientation is often very heterogeneous and difficult to accurately assess. There is a critical need for a technique that can rapidly measure the leaf angle at any point in space and time in order to improve models that estimate crop growth, yields, water use, and carbon sequestration. A new method was developed and tested that uses terrestrial LiDAR scanning data to rapidly measure the three-dimensional distribution of leaf orientation. The method triangulates hit points recorded by the LiDAR scan, calculates the orientation and reconstructs a continuous surface of individual leaves. The method was validated by comparing scans of an isolated tree and a grapevine canopy to manual measurements. Visualizations are presented of the three-dimensional distribution leaf orientation over entire plants, which consisted of millions of individual measurements of leaf orientation. Measured distributions indicated that distributions of both leaf inclination and azimuth were highly heterogeneous and difficult to accurately assess by other methods. This methods will be useful in improving our understanding of how canopy architecture influence pest and disease development, modeling of plant growth, photosynthesis, evapotranspiration, and other transport processes.

Technical Abstract: Leaf orientation plays a fundamental role in many transport processes in plant canopies. At the plant or stand level, leaf orientation is often highly anisotropic and heterogeneous, yet most analyses neglect such complexity. In many cases, this is due to the difficulty in measuring the spatial variation of the leaf angle distribution function. There is a critical need for a technique that can rapidly measure the leaf angle distribution function at any point in space and time. A new method was developed and tested that uses terrestrial LiDAR scanning data to rapidly measure the three-dimensional distribution of leaf orientation for an arbitrary volume of leaves. The method triangulates hit points recorded by the LiDAR scan, which allows for easy calculation of normal vectors. As a byproduct, the triangulation also yields continuous surfaces that reconstruct individual leaves. In order to produce a probability density function for leaf orientation from triangle normal vectors, it is critical that the proper weighting be applied to each triangle. Otherwise, results will heavily bias toward normal vectors that point toward the position of the LiDAR scanner. The method was validated by comparing scans of an isolated tree and a grapevine canopy to manual measurements. Visualizations are presented of the three-dimensional distribution leaf orientation over entire plants, which consisted of millions of individual measurements of leaf orientation. Measured distribution functions indicated that, for the plants measured in this study, distributions of both leaf inclination and azimuth were highly anisotropic and heterogeneous. This indicated a potential need to include such variability in analyses of canopy transport processes.