This thesis will explore fusion of terrestrial LiDAR (TLS) and integrated optical camera data for improved estimates of above ground biomass (AGB). The focus in this work will be on the distinction between hard (i.e. woody component) and soft (i.e. foliage component) targets. This will allow us the describe the distribution of different crown constituents better and therefore improve AGB estimates.
The TLS instrument (Riegl VZ-400) is equipped with an externally mounted RGB camera (Nikon D700 camera). Acquiring additional RGB data next to a 3D point cloud will enrich the spectral information from this single wavelength scanner. Combined RGB/LiDAR data has been captured over various sites (Australia, Belgium, Netherlands). The student will develop a unique “pseudo multi-spectral” LiDAR by integrating RGB camera and LiDAR data. Finally, this will be used to classify its data into a woody and foliage component.
- Provide a literature review of the use of calibrated images in terrestrial laser scanning
- Carry out some calibration/validation experiments to register RGB and TLS data together
- Develop and validate a classification approach to separate between woody and foliage components from LiDAR data.
- Some basic scripting/programming skills (R or Python) are useful.
Theme: Sensing & measuring