# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 4 Methods  
### Registration of the TLS and MLS Data  
TLS point clouds were co-registered using the registration tools \"Automatic Registration 2\" and \"Multi Station Adjustment\" of Riegl _RiSCAN Pro_ 2.6.1. Stray and noise points with a so-called surface reflectance less than \(-25\) dB or a pulse shape deviation greater than 15, both terms defined by the scanner manufacturer [55; 56], were removed to achieve a higher quality point cloud. The reflectance value in dB ranges from \(-25\) up to 5, with positive values indicating retro-reflecting targets and negative values are diffusely reflecting targets. For the TLS registration, the point cloud captured in the plot center was used as the project coordinate system and the other four scan positions were registered to the center position. The integrated GNSS sensor in the Riegl VZ-400i enables the global positioning of the scanner positions. Finally, the registered point clouds were exported in the ETRS89/UTM zone 32N coordinate system.  
Due to the arising position and height inaccuracy of the trajectory caused by GNSS inaccuracies in forests, the registration of the MLS data requires further data processing. If the forest path is scanned twice from different directions, the trajectory shows differences in position and height (Figure 5). The bigger the time difference between the two scanning events, the larger the influence. The heights and positional deviations registered within the MLS data in the forest were up to 1.8 m in height and up to 1.2 m in position. The _RiPRECISION_ tool from _RiPROCESS_ (1.8.4) was used to eliminate the height error. Since the error is not constantly distributed over the forest path, but instead depends on the quality of the GNSS signals through the canopy, an alternative approach is required. The presentation of the registration algorithm would go beyond the content and topic of this paper and will be presented in a future publication. Finally, the point cloud of Scanner 1 at time t\({}_{1}\) was taken as the reference dataset and the remaining MLS point clouds were transformed to this point cloud.  
In order to compare both datasets with each other, a global registration was conducted and the point clouds were transformed to the ETRS89/UTM zone 32N coordinate system and were finally aligned with an ICP algorithm of _CloudCompare_ (version 2.10.alpha).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**TLS**} & \multicolumn{2}{c}{**MLS**} \\ \hline System & \multicolumn{2}{c}{Riegl VZ-400i} & \multicolumn{2}{c}{Riegl VMX-250} \\ \hline Date & 16.03.2017 & 21.08.2017 & 14.03.2017 & 14.08.2017 \\ Foliation & leaf-off & leaf-on & leaf-off & leaf-on \\ \hline Scan positions (TLS)/records (MLS) & 5 & 5 & 4 & 4 \\ \hline Scan resolution 1 & 0.05 deg & 0.04 deg & 4.72 m/s & 3.33 m/s \\ Average point density 2 & 661 & 700 & 26 & 26 \\ \hline Footprint size (plot center) & 5.2 mm @ 15 m & 5.2 mm @ 15 m & 4.9 mm @ 16 m & 4.9 mm @ 16 m \\ \hline Data rate (kHz) & 1200 & 600 & 300 & 300 \\ Data points/plot & 36.5 M & 43.5 M & 1.3 M & 2.1 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the different datasets of the sample plots. 1 The scan resolution depends both on the data rate and on the speed of the mobile platform. 2 measured in a 10 cm \(\times\) 10 cm point cloud slice of a stem facing the trajectory, located 2 m from the plot center.