Terrestrial Laser Scanning Intensity Captures Diurnal Variation in Leaf Water Potential

Abstract

Drought-induced plant mortality has increased globally during the last decades and is forecasted to influence global vegetation dynamics. Timely information on plant water dynamics is essential for understanding and anticipating drought-induced plant mortality. The most common metric that has been used for decades for measuring water stress is leaf water potential (ΨL), which is measured destructively. To obtain information on water dynamics from trees and forested landscapes, remote sensing methods have been developed. However, the spatial and temporal resolution of the existing methods have limited our understanding of water dynamics and diurnal variation of ΨL within single trees. Thus, we investigated the capability of terrestrial laser scanning (TLS) intensity in observing diurnal variation in ΨL during a 50 hour monitoring period and aimed to improve understanding on how large part of the diurnal variation in ΨL can be captured using intensity observations. We found that TLS intensity at 905 nm wavelength was able to explain 78% of the variation in ΨL for three trees of two tree species with a root-mean square error of 0.137 MPa. Based on our experiment with three trees, time-series of TLS intensity measurements can be used in detecting changes in ΨL, and thus it is worthwhile to expand the investigations to cover a wider range of tree species and forests and further increase our understanding of plant water dynamics at wider spatial and temporal scales.

Close-range Hyperspectral Spectroscopy Reveals Leaf Water Content Dynamics

“…The purpose of the second experiment was to test the sensors in a forest field experiment with mature trees and measure diurnal dynamics of leaf water content. We found that the spectral features were in good agreement with the diurnal variation of xylem diameter, which is known to be well-correlated with leaf water dynamics generally (Offenthaler et al, 2001) and also at the same test site (Junttila et al, 2021). We found that the change in spectral features correlated well with the change in xylem diameter, but the absolute values of the spectral features varied between leaves.…”

“…A disadvantage of the presented method is the lack of multiple pixels compared to other imaging sensors, such as terahertz radiation spectroscopy (Browne et al, 2020) or terrestrial lidar (Elsherif et al, 2019b;Junttila et al, 2021). Spatial variation of leaf water content is more difficult to measure using the presented method, but on the other hand we were able to observe very clearly the small diurnal variation in leaf water content, which has been difficult to measure non-destructively.…”

“…The diurnal xylem diameter variation was used as a proxy measurement of changes in leaf water content due to its strong correlation with leaf water potential when using a time lag (Junttila et al, 2021). This has been confirmed on the site before and we used a 90-minute time lag (Junttila et al, 2021). Ten healthy leaves were randomly selected from the canopy for repeated measurements using the NIRONE sensors.…”

“…Various approaches and techniques have been used with different application perspectives and varying level of success, for estimating leaf water content with wide ranging spatial scales, including terrestrial lidar (Elsherif et al, 2019a;Junttila et al, 2019;Junttila et al, 2021), imaging spectroscopy (Kotz et al, 2004), terahertz radiation spectroscopy (Browne et al, 2020) and microwave remote sensing (Konings et al, 2019). However, considering leaf water content monitoring from single trees several times a day (which is required for monitoring diurnal differences in leaf water content) there are still many limiting factors with existing remote sensing methodologies.…”

Integrating terrestrial laser scanning with functional–structural plant models to investigate ecological and evolutionary processes of forest communities

“…Terrestrial laser scanning, optionally complemented with scanning from unmanned aerial vehicles, is a method that enables capturing the full 3-D structure of a forest stand with close to centimetre-level detail in a short amount of time ( Liang et al , 2016 ; Calders et al , 2020 ), including the identification of individual trees and their species ( Xi et al , 2020 ), the ground vegetation ( Muumbe et al , 2021 ) and the topography of the terrain ( Diamond et al , 2020 ). Methods for collecting additional spectral information to estimate the state of various physiological processes within trees along with the structural scanning are also being developed ( Junttila et al , 2021 ).…”