Doctoral defence: Kairi Adamson “Applicability of digital photography in monitoring changes of leaf inclination and foliage clumping with time“

On 24 September at 14.15 Kairi Adamson will defend her doctoral thesis “Applicability of digital photography in monitoring changes of leaf inclination and foliage clumping with time“ for obtaining the degree of Doctor of Philosophy (in Environmental Technology).

Supervisor:
Associate Professor Jan Pisek, University of Tartu

Opponent:
Associate Professor Loïc Tadrist, Aix-Marseille University (France)

Summary
Light availability plays a crucial role in regulating the development of forest canopies, influencing photosynthesis rates, plant growth, and overall ecosystem productivity. Leaf inclination angles and the clumping index are two important parameters that affect how light penetrates a canopy, describing the angles at which leaves are positioned and how tightly they are grouped into shoots, branches and other structures. While both parameters are essential for accurate modelling of radiative transfer and canopy productivity, they have often been treated as constants or neglected entirely due to the complexity of actual data collection.
In this thesis, a simple method based on digital photography was used to measure leaf inclination angles. After demonstrating the method’s reliability, it was applied to study a range of tree species throughout the growing season, revealing that leaf inclination angles vary considerably with species, height in the canopy, period in the growing season, and light availability. A comparison of various clumping measurement methods showed that digital photography proved to be the most suitable means of data collection for clumping calculations.
Throughout this work, the most extensive currently available dataset of leaf inclination angles was gathered and made available for public use by the wider community. Finally, it was shown that the increasing carbon dioxide concentrations brought about by climate change will not affect leaf inclination or the clumping index, ensuring that the collected datasets will remain relevant in the future.