Thesis subject

MSc thsis topic: Forest ecosystem monitoring: Integrating biophysical approaches, high-resolution hyperspectral imaging, and eddy covariance flux towers.

The Loobos site (ICOS code NL-Loo) in the Veluwe region is a needleleaf forest with minimal management, originally established in 1911 to mitigate sandstorms and supply timber. Today, it is part of the ICOS (Integrated Carbon Observation System) network, serving as a key reference site for studying biodiversity proxies and ecosystem function, most importantly providing half hourly carbon flux measurements using the eddy covariance technique. Remote sensing techniques, including radiative transfer models (RTMs), provide valuable tools to simulate spectral responses at multiple scales, improving the retrieval of functional plant traits and linking them with key fluxes such as Gross Primary Production (GPP), derived from eddy covariance measurements.

A major focus of this study is on HyPlant, an airborne hyperspectral sensor specifically designed for Sun-Induced Fluorescence (SIF) retrieval. HyPlant provides high spectral and spatial resolution data, enabling detailed assessments of plant physiological processes, including photosynthetic activity and stress responses. By integrating HyPlant data with other satellite imagers (TROPOMI, Sentinel-2, UAV-based hyperspectral sensors), along with LiDAR imaging, this research will enhance the retrieval of biodiversity-related traits and their relationship with ecosystem fluxes.

Background

This research contributes to ongoing efforts within the GRS group to integrate remote sensing, RTM simulations, and machine learning for ecosystem monitoring. The study enhances the retrieval of biodiversity traits while also advancing methods for estimating GPP from remote sensing data. By combining multispectral and hyperspectral observations with radiative transfer modelling and flux tower measurements, this research strengthens the link between plant traits, ecosystem productivity, and carbon fluxes.

Relevance to research/projects at GRS or other groups

The research topic is part of an ongoing field of research into measuring, modelling, and scaling biodiversity-related traits and ecosystem fluxes within the GRS group, led by Prof. Lammert Kooistra Additionally, it aligns with research conducted within the ICOS network, where Dr. Michiel van der Molen is the principal investigator for flux tower measurements at NL-Loo, Dr. Folkert Boersma works in the Department of Meteorology and Air Quality at Wageningen University & Research, leading the Next Generation TROPOMI NO2 (NO2NEXT) project.

Objectives and Research questions

The objective of this thesis is to develop a multiscale 1-D RTM framework to retrieve biodiversity-related plant traits and assess their relationship with GPP and eddy covariance fluxes at the NL-Loo site. Key research questions include:

Requirement

Required: Geoscripting, Machine Learning, Remote Sensing, Advance Earth Observation
Optional: Spatial Modelling and Statistics, Deep Learning

Literature and information

Clevers, J. G. P. W., & Kooistra, L. (2012). Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 574–583.
Verrelst, J., Camps-Valls, G., Muñoz-Marí, J., Rivera, J. P., Veroustraete, F., Clevers, J. G. P. W., & Moreno, J. (2015). Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 273–290.
Féret, J. B., Berger, K., de Boissieu, F., & Malenovský, Z. (2021). PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents. Remote Sensing of Environment, 252(October 2020). https://doi.org/10.1016/j.rse.2020.112173
MAQ-Observations.nl: information about Loobos and the measurements made there.

Expected reading list before starting the thesis research

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
Baldocchi, D. D. (2020). How eddy covariance flux measurements have contributed to our understanding of Global Change Biology. Global Change Biology, 26(1), 242–260.

Theme(s): Sensing & measuring; Modelling & visualisation