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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...

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Autores principales: Puttonen, Eetu, Lehtomäki, Matti, Litkey, Paula, Näsi, Roope, Feng, Ziyi, Liang, Xinlian, Wittke, Samantha, Pandžić, Miloš, Hakala, Teemu, Karjalainen, Mika, Pfeifer, Norbert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
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.
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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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