Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling

The concept for a multi-spectral, full-waveform canopy LiDAR instrument was tested by simulating return waveforms using a model providing ecological sound tree structure (TREEGROW) and a model of leaf optical properties (PROSPECT). The proposed instrument will take measurements at four different wavelengths, which were chosen according to physiological processes altering leaf reflectance and transmittance. The modelling was used to assess both the structural and physiological information content such an instrument could provide, especially whether the normally structure-dominated return waveform would pick up small changes in reflectance at the leaf level. Multi-spectral waveforms were simulated for models of single Scots pine trees of different ages and at different stages of the growing season, including chlorophyll concentration induced changes in leaf optical properties. It was shown that the LiDAR waveforms would not only capture the tree height information, but would also pick up the seasonal and vertical variation of NDVI computed from two of the four MSCL wavelengths inside the tree canopy. The instrument concept was further tested in a simulation of a virtual forest stand constructed of 74 trees of different ages according to measurements taken on a field site being 20 by 20 meter in size. A total of 1521 NDVI profiles were computed and mean NDVI corrected backscatter was compared to the actual canopy profile of the virtual stand. The profiles picked up the seasonal variation of chlorophyll within the canopy, while the return of ground remained unchanged from June to September. Thus, it was shown that a MSCL instrument would be able to separately pick up the physiology of canopy and understorey and/or soil. It was found that occlusion would mask the lower parts of the canopy volume within the stand and the seasonal variation of this occlusion effect was quantified, being larger in September, when the absorption of canopy elements is higher. In addition, it could be demonstrated that a new multi-wavelength LiDAR predictor variable was able to significantly improve the retrieval accuracy of photosynthetically active biomass opposed to using a single-wavelength LiDAR alone.