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A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series
Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast proc...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499199/ https://www.ncbi.nlm.nih.gov/pubmed/31110511 http://dx.doi.org/10.3389/fpls.2019.00486 |
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author | Puttonen, Eetu Lehtomäki, Matti Litkey, Paula Näsi, Roope Feng, Ziyi Liang, Xinlian Wittke, Samantha Pandžić, Miloš Hakala, Teemu Karjalainen, Mika Pfeifer, Norbert |
author_facet | Puttonen, Eetu Lehtomäki, Matti Litkey, Paula Näsi, Roope Feng, Ziyi Liang, Xinlian Wittke, Samantha Pandžić, Miloš Hakala, Teemu Karjalainen, Mika Pfeifer, Norbert |
author_sort | Puttonen, Eetu |
collection | PubMed |
description | Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset. |
format | Online Article Text |
id | pubmed-6499199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64991992019-05-20 A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series Puttonen, Eetu Lehtomäki, Matti Litkey, Paula Näsi, Roope Feng, Ziyi Liang, Xinlian Wittke, Samantha Pandžić, Miloš Hakala, Teemu Karjalainen, Mika Pfeifer, Norbert Front Plant Sci Plant Science Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset. Frontiers Media S.A. 2019-04-17 /pmc/articles/PMC6499199/ /pubmed/31110511 http://dx.doi.org/10.3389/fpls.2019.00486 Text en Copyright © 2019 Puttonen, Lehtomäki, Litkey, Näsi, Feng, Liang, Wittke, Pandžić, Hakala, Karjalainen and Pfeifer. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Puttonen, Eetu Lehtomäki, Matti Litkey, Paula Näsi, Roope Feng, Ziyi Liang, Xinlian Wittke, Samantha Pandžić, Miloš Hakala, Teemu Karjalainen, Mika Pfeifer, Norbert A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series |
title | A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series |
title_full | A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series |
title_fullStr | A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series |
title_full_unstemmed | A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series |
title_short | A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series |
title_sort | clustering framework for monitoring circadian rhythm in structural dynamics in plants from terrestrial laser scanning time series |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499199/ https://www.ncbi.nlm.nih.gov/pubmed/31110511 http://dx.doi.org/10.3389/fpls.2019.00486 |
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