Terrestrial hyperspectral LiDAR (HSL) sensors could provide not only spatial information of the measured targets but also the backscattered spectral intensity signal of the laser pulse. The raw intensity collected by HSL is influenced by several factors, among which the range, incidence angle and sub-footprint play a significant role. Further studies on the influence of the range, incidence angle and sub-footprint are needed to improve the accuracy of backscatter intensity data as it is important for vegetation structural and biochemical information estimation. In this paper, we investigated the effects on the laser backscatter intensity and developed a practical correction method for HSL data. We established a laser ratio calibration method and a reference target-based method for HSL and investigated the calibration procedures for the mixed measurements of the effects of the incident angle, range and sub-footprint. Results showed that the laser ratio at the red-edge and near-infrared laser wavelengths has higher accuracy and simplicity in eliminating range, incident angle and sub-footprint effects and can significantly improve the backscatter intensity discrepancy caused by these effects.

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.

As a novel remote sensor, hyperspectral LiDAR is faced with the incidence angle effect, which restricts its quantitative applications. However, the current radiometric correction algorithms have some limitations, concentrating on: 1) the mathematically polynomial fitting; 2) adjacent wavelength ratio such as ratio vegetation index; and 3) perfect Lambertian assumption and using the Lambert cosine law to correct the effect. In this study, a practical and proper correction algorithm is proposed to overcome these limitations. First, to better characterize the complex reflection characteristics of the object surface, a combination of the Lambert law and Beckmann law is applied to represent the object surface. Then, it considers the impact of both wavelength and incidence angle on describing the surface roughness factor and diffuse fraction. Finally, a modified and physically based radiometric correction algorithm is generated. It provides the detailed correction equations for intensity and reflectance data recorded by hyperspectral LiDAR. To obtain its parameters and verify its reliability, leaves (ten samples per species, 30 in total) were stochastically collected from three broadleaf trees to experiment. The results showed that the algorithm achieved good performances by comparing the intensity and reflectance changes before and after removing the leaf incidence angle effect. Since it is physically based, the algorithm could promisingly be a fundamental solution to eliminate the incidence angle effect for hyperspectral LiDAR.

Full-waveform hyperspectral LiDAR (FWHSL) is able to obtain spectral and spatial information by utilizing a single instrument, and it has become more and more commonly used in vertical distribution studies of structural and biochemical characteristics of vegetation. However, the pulse-echo arrival times of multiple spectral channels of the FWHSL are not consistent and this causes range ambiguity in spectral channels. In this paper, the pulse signal decay effect on range measurements was studied by measuring the varying trends of pulse signal decay between spectral channels with different material properties. The experiments were repeated at different distances. All of the spectral channels were compared for different materials. The results suggest that the channels in the red edge spectral region of vegetation have good stability and accuracy for range measurements of varied distance and materials properties. Finally, based on the geometric invariability in a specific red edge channel, a practical calibration approach for the pulse signal decay effect is also presented. The validation tests showed it could improve the pulse signal decay effect of full-waveform hyperspectral LiDAR.