Multi-echo hyperspectral reflectance extraction method based on full waveform hyperspectral LiDAR

Full waveform hyperspectral LiDAR can simultaneously obtain three-dimensional spatial information and spectral information of targets. As a result of it, target attributes in the vertical direction can be accurately assessed. However, spectral reflectance extraction from full waveform hyperspectral LiDAR data, especially for multi-echo full waveforms, is still challenging due to the indeterminate backscattering cross-sectional area and unseparated reflectance. This challenge hampers the extraction accuracy of multi-echo reflectance and then causes underestimation consequently. Here, we proposed a new method to extract reflectance of multi-echo full waveforms, namely multi-echo reflectance correction using neighbourhood single-echo reflectance (MCNS), and verified its validation by using the coefficient of determination (R2) and the root mean square error (RMSE) based on simulated and measured data. Results indicated that R2 and RMSE between the corrected simulated multi-echo reflectance and the spectrometer reflectance were more than 0.95 and less than 0.042 respectively, while that between the corrected measured multi-echo reflectance and the spectrometer reflectance were above 0.56 and below 0.15 respectively. These results demonstrated the feasibility of the proposed method in extracting multi-echo reflectance. This study lays a foundation for subsequent forest attribute assessments more accurately, making it possible to characterise multiple target features vertically using high-resolution spectral reflectance.