A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series

Abstract

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.

A Long-Term Terrestrial Laser Scanning Measurement Station to Continuously Monitor Structural and Phenological Dynamics of Boreal Forest Canopy

“…The sun rose 05:14 A.M on April 30 with the highest point at 1:20 P.M and set at 9:26 P.M. After sunset the clusters start to move back toward the initial position. This results show that the TLS measurement station provide point cloud time series with enough accuracy and resolution to monitor short-term dynamics, as previously presented by Puttonen et al (2019) .…”

“…The main setback in detecting circadian movements with TLS is the requirement of stable environmental conditions, such as no wind and no precipitation, which present a significant technical challenge for long-term monitoring campaigns outdoors. So far, studies outside laboratory conditions are rare ( Zlinszky et al, 2017 ; Puttonen et al, 2019 ). Here, we demonstrate with the second example that short-term structural phenomena are monitored with the measurement station as a future alternative for studying daily dynamics in trees.…”

“…The feasibility of terrestrial LiDAR data to detect and monitor long-term vegetation dynamics in forest and crop areas have been discussed in previous works, including plant growth ( Yu et al, 2004 , 2006 ; Olivier et al, 2017 ; Guo et al, 2019 ), DBH increase ( Liang et al, 2012 ), AGB change ( Kaasalainen et al, 2014 ; Srinivasan et al, 2014 ) and spring sprouting and flowering ( Olsoy et al, 2014 ; Calders et al, 2015 ). Recently, new studies have also used TLS data for monitoring short-term phenomena, such as circadian rhythms and foliar nyctinasty in different plants and tree species ( Puttonen et al, 2015 , 2016 , 2019 ; Zlinszky et al, 2017 ; Herrero-Huerta et al, 2018 ; Bakay and Moravčík, 2020 ). These studies highlight the high potential of setting up a permanent TLS measurement station as a new non-destructive tool for boreal forest dynamics monitoring.…”

“…The measurements were taken in an indoor environment over 2 days. Puttonen et al (2019) reported a new method to monitor circadian rhythms from point cloud time series. They studied overnight movements in two Norway maples from sunset to sunrise with 20-min scan repetitions that resulted in 130 scans in total.…”

“…Over the last 5 years, new studies have detected and quantified circadian movements in the tree branches and foliage of fullgrown trees using TLS data acquired with short timescale measurements that have had an hourly or even more frequent scanning repetition rate (Puttonen et al, 2015(Puttonen et al, , 2016(Puttonen et al, , 2019Zlinszky et al, 2017;Herrero-Huerta et al, 2018;Bakay and Moravčík, 2020). Puttonen et al (2016) used TLS time series acquired in two geographically different locations (Finland and Austria) to detect short-term vegetation dynamics.…”