FUSION-RaLiKI

Forests are important carbon sinks and therefore play a crucial role in the transition to carbon neutrality. The development of robust remote sensing methods for estimating biomass is crucial for monitoring forests on a global scale. Traditional methods use transfer functions between forest height and biomass (H2B allometry), with the former being derived from synthetic aperture radar (SAR) data and the latter often from lidar data. In the future, data from the BIOMASS mission will also be available for deriving forest structure and height. The frequency (P-band) of this data is lower and its spatial resolution is coarser than that of the SAR data previously used for such methods. With regard to monitoring biomass change in forests, absolute changes between two recording points in time have been calculated to date. This has the disadvantage of a potentially larger error, as the errors of the two estimates add up. Therefore, this project initially aims to calculate differential changes in biomass by developing a ΔH2ΔB allometry. The second objective of the project is to transfer both the determination of biomass and its change to BIOMASS data by developing AI models.