Estimating leaf chlorophyll and nitrogen contents using active hyperspectral LiDAR and partial least square regression method

As hyperspectral LiDAR (HSL) combines the advantages of hyperspectral remote sensing and LiDAR, it has the potential of estimating vegetation biochemical contents at any three-dimensional (3-D) location. We investigate the capability of HSL to monitor leaf chlorophyll and nitrogen contents at a distance of 7.5 m. Using full-waveform LiDAR data obtained by HSL, the performance of the partial least square regression (PLSR) model using three strategies (only wavelength bands, only vegetation indices, and both wavelength bands and vegetation indices) is explored based on the correlation analysis of 16 wavelength bands and 16 vegetation indices. The result shows that the PLSR model can make full use of various wavelength bands and vegetation indices, having a strong ability to monitor leaf chlorophyll and nitrogen contents using full-waveform LiDAR data. The analysis can be applied for predicting leaf biochemical components of other vegetation and provides the basis for monitoring 3-D distribution of biochemical contents using HSL in the future.