Student information

MSc thesis subject: Deriving tree parameters for functional Structural Plant (FSP) models from Terrestrial LiDAR point clouds.

Point clouds of trees scanned with Terrestrial Laser Scanning (TLS) can be analysed to estimate tree structural properties. Nowadays, algorithms allow you to extract structural information, some of them easy to determine, such as tree height, DBH, crown diameter; and more complex to determine, such as branch angle or leaf area index (LAI). Further, using this information, we can estimate indirect plant processes for example above ground biomass, metabolic scaling, wind resistance, crown shyness, among others.

There is a need for designing adequate monitoring and management tools for cocoa field in order to better predict and improve the environmental sustainability of cocoa trees growth and functioning.

A functional structural cocoa model has been developed to address these issues but acquisition of data on tropical perennial is difficult and time consuming.

Novel data provided by TLS can be used as input for the parametrization and validation of FPS models.

The goal of this project is to evaluate the feasibility of extracting TLS-based information for FSP models, i.e. branch and leaf angles, number of secondary branches, to be used as input for the model and variables such as  biomass, LAI, total leaf area that could be used for model validation.

Due to current restrictions, a fieldwork might not be available for cocoa 3D model TLS collection, but for trees closed to the campus.


  • Review literature on state-of-the-art on using TLS for tree bio-physical processes.
  • Apply specialized algorithms to extract tree parameters for FSP models from TLS point clouds.
  • Integrate these parameters into FPS models.



  • Scripting skills (e.g. R, Python, MatLab) are a preference
  • Basic knowledge of Cloudcompare software
  • Completion GRS-32306 Advanced Earth Observation (for GRS students only).

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