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Open AccessTechnical Note

Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR

1
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan
2
Research Fellow of Japan Society for the Promotion of Science, Tokyo 102-0083, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2536; https://doi.org/10.3390/rs11212536
Received: 8 October 2019 / Revised: 23 October 2019 / Accepted: 25 October 2019 / Published: 29 October 2019
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing)
It is critical to take the variability of leaf angle distribution into account in a remote sensing analysis of a canopy system. Due to the physical limitations of field measurements, it is difficult to obtain leaf angles quickly and accurately, especially with a complicated canopy structure. An application of terrestrial LiDAR (Light Detection and Ranging) is a common solution for the purposes of leaf angle estimation, and it allows for the measurement and reconstruction of 3D canopy models with an arbitrary volume of leaves. However, in most cases, the leaf angle is estimated incorrectly due to inaccurate leaf segmentation. Therefore, the objective of this study was an emphasis on the development of efficient segmentation algorithms for accurate leaf angle estimation. Our study demonstrates a leaf segmentation approach based on a k-means algorithm coupled with an octree structure and the subsequent application of plane-fitting to estimate the leaf angle. Furthermore, the accuracy of the segmentation and leaf angle estimation was verified. The results showed average segmentation accuracies of 95% and 90% and absolute angular errors of 3° and 6° in the leaves sampled from mochi and Japanese camellia trees, respectively. It is our conclusion that our method of leaf angle estimation has high potential and is expected to make a significant contribution to future plant and forest research. View Full-Text
Keywords: k-means; leaf angle distribution; leaf angle estimation; LiDAR; octree structure; plane-fitting k-means; leaf angle distribution; leaf angle estimation; LiDAR; octree structure; plane-fitting
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MDPI and ACS Style

Kuo, K.; Itakura, K.; Hosoi, F. Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR. Remote Sens. 2019, 11, 2536.

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