Effective methods for assessing vegetation structure and change from multi-echo and full-waveform laser scanner data of terrestrial LiDAR systems

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For the derivation of structural parameters such as vegetation density, biomass, and growth parameters in tree stands, the volumetric reconstruction of terrestrial LiDAR data with ray-propagation modelling approaches proves to be particularly advantageous. However, the performance of existing methods is limited, e.g. by systematic under-representations due to occlusion effects, non-uniform spatial resolution of the measurement data and their restriction to static measurement systems. The central goal of the project is the development of effective methods for the generation of complete unbiased volumetric reconstructions of the vegetation structure from mobile or stationary terrestrial LiDAR data. The focus is on novel ray propagation modelling approaches that take into account both the geometry and the radiometry of the laser pulses when transforming static and mobile laser scanner data into a voxel space. For full-waveform data, the complete signal is transformed into voxel space by deriving voxel space entries from the amplitudes of the reconstructed differential backscatter cross section. In the case of multi-echo data, the contact frequency within each traversed voxel is determined and the intensity of the detected echoes is taken into account. The resolution of the voxel space is adapted to the recording geometry and the spatial resolution of the measurement data. For vegetation applications, the majority of laser pulses are affected by partial or total occlusions, leading to a systematic underrepresentation of the vegetation structure. Partial occlusion modelling allows for the derivation of a correction term compensating for the effects of partial interceptions of the laser beam at leaves and branches. The basic idea of the approach to be developed is an analysis of the individual pulse history in combination with the interpretation of the recorded intensity values. For full-waveform data, an integral correction based on the signal waveform, and for multi-echo data, a segment-wise correction based on the contact frequency can be applied. Filling gaps in the volumetric reconstruction due to total occlusion effects requires the development of 3D mathematical morphology operators. The resulting volumetric representations provide a basis for better exploiting the potential of the 3D measurement data and thus serve a wide range of scientific measurement tasks in the field of vegetation structure research. This includes new possibilities in the field of tree species recognition with AI-based methods as well as more accurate 3D grid data for numerical simulations in meteorology and climate change assessment. Applying the methodology to multi-temporal data sets, changes in vegetation structure can be determined at high accuracy.