# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 2 Materials
### Multi-Wavelength LiDAR  
The multi-wavelength LiDAR data were acquired in September 2014 by Optech Titan, a system that includes three active imaging channels at 532 nm visible (green), 1064 nm (NIR), and 1550 nm (MIR) [28]. The flying height is about 400 m. Number of flying strips is three, all merged into a single LAS file. The preprocessing of multi-wavelength LiDAR removes noise points (e.g., air point). Because system parameters and trajectories were limited or unavailable, this study used the intensities from LiDAR's standard product, which is the amplitude of the return reflection without intensity calibration. Both topographic and hydrographic data for spatial mapping can be obtained from the Titan system from these three channels. We compared the characteristics of the individual laser pulses as well as other characteristics of each channel required to evaluate the behavior and performance of the system (Table 1). These three intensities with wavelengths of 532 nm, 1064 nm, and 1550 nm (Figure 1b-d) were stored in separated files according to the ASPRS LAS standard. The tested data contained about 14,731,124, 13,530,547, and 12,439,573 points from these three channels, respectively. The total number of points in the study area was about 40 million, and the average point density was about 70.7 pts/m\({}^{2}\). Point density is an important factor in LiDAR processing. In general, three channels in different look directions may acquire more points than one channel. One advantage of multi-wavelength LiDAR is the ability to increase the number of points in a single strip. The scanning angles of the Titan system were designed in different directions to avoid interference and obtain more points. We selected three sub-regions to compare the point densities for different land covers (Table 2), including simple man-made objects, mixed objects, and complex tree regions (boxes 1, 2, and 3 in Figure 1a). Each sub-region is about 2500 m\({}^{2}\) (50 \(\times\) 50 m). In comparison to point density of the single wavelength system, the additional wavelengths in the three wavelengths system (i.e., 1550 nm in 3.5 degree forward; 1550 nm in nadir; 532 nm in 7.0 degree forward) may increase the number of points in the three sub-regions. For building region, the point densities of single wavelength were originally about 23.2-24.8 points/m\({}^{2}\) and the use of multi-wavelength LiDAR increased it to 71.5 points/m\({}^{2}\). The point density is related to the ability of multiple returns and multi-channel lasers in different scanning directions. This area contains three overlapped flying strips and the tree and building regions were covered by same flying strips. The tree area has a higher penetration rate than buildings and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Channel 1** & **Channel 2** & **Channel 3** \\ \hline Laser Wavelength (nm) & 1550 & 1064 & 532 \\ Beam divergence (mrad) & 0.35 & 0.35 & 0.7 \\ Laser classification & Class IV & Class IV & Class IV \\ Look angle (degree) & 3.5 forward & Nadir & 7.0 forward \\ Pulse repletion frequency (kHz) & 50–300 & 50–300 & 50–300 \\ Pulse energy (uJ) & 20–50 & \(\sim\)15 & \(\sim\)30 \\ Pulse width (ns) & \(\sim\)2.7 & 3–4 & \(\sim\)3.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The specifications of Titan multi-wavelength light detection and ranging (LiDAR) system. Figure 1: Optical image and light detection and ranging (LiDAR) intensity images. (**a**) True color from Worldiview-3 image; (**b**) intensity image from 532 nm channel; (**c**) intensity image from 1064 nm channel; (**d**) intensity image from 1550 nm channel; (**e**) false color image from LiDAR intensities  
thus produces higher number of multiple returns. A comparison of the tree and building shows that the complex tree region has the highest point density because laser beams from different directions may penetrate the tree crown, demonstrating the benefit of using multi-wavelength LiDAR to improve point density in complex tree regions.