# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 6 Discussion  
The study compared the data quality and information content of TLS and MLS data in a detailed plot analysis. TLS--preferably in a multi-scan configuration to reduce occlusions--delivers very high density point clouds, which form a valuable basis for generating high-quality QSMs. Compared to MLS, the TLS provides a simpler and less expensive way to generate 3D point clouds. On the other hand, MLS offers the possibility of 3D data capture of wide corridor-like areas in even less time due to its mobile platform. We used existing point cloud processing software to derive the parameters from the MLS data. For the registration of multiple-scan forest path MLS data, a suitable software tool capable for nonlinear transformations due to bad or missing GNSS signals is required. Besides the fast and precise 3D recording, MLS also has some challenges compared to TLS. For instance, in this study we used a Mobil Mapping System Riegl VMX-250 mounted on a van, hence the forest paths should have a clear height of 2.8 m to avoid a collision of the MLS-system with branches. Smaller mobile platforms, such as a quad or the use of a PLS/WLS allows to pass through lower vegetation heights. Due to the width of the forest paths, the canopy above the path is often not closed, increasing the light availability for the understory vegetation close to the path. Especially during summer, in a broadleaved or mixed forest a large part of the MLS pulses are blocked by the vegetation next along the path resulting in strong occlusions in the rear trunk and crown space. Driving on skid trails offers an alternative here, since the ground vegetation along the trails will usually be less vigorously developed. However, it is then necessary to use off-road platforms. In this paper, we have emphasized the acquisition and segmentation of each branch to determine the suitability of MLS for the evaluation of interaction processes between adjacent tree individuals. The tree segmentation procedure used here consists of several interactive steps that are still rather time-consuming. The segmented trees from the _SimpleTree_ tool and the voxel-tree-template must be removed manually via a visual control of adjacent branches. Compared to the voxel-tree-template, the segmented trees from _SimpleTree_ contain only very few neighboring branches, which would theoretically be negligible, but which were cleaned up in the course of evaluation of the interactions processes. Automation can be implemented by means of connectivity analyses in the voxel space, given that the adjacent branches do not fall below a minimum distance and the branches are represented with few occlusions. During the processing chain, an enormous number of occlusions appeared in the crown space of the summer data, so that we have omitted the time-consuming tree segmentation. The quality of the point cloud registration of the TLS and MLS datasets is less than 1 cm and forms a good basis for the derivation of forest parameters, especially for the stem parameters. The registration of leaf-on to leaf-off datasets shows a lower quality. One reason for this is the foliage. The weight of the leaves pushes the branches down and thus they have been displaced when compared to the leaf-off dataset. We obtained a RMSE of 3.7 cm for the DBH cylinder fitting in comparison to the manually measured DBHs. The large deviation can be explained by the fact that the trees are 6 to 26 m away from the trajectory and the point density at the trunk is sometimes too low for precise diameter determination. Compared to the high-resolution TLS measurements, the trees have a distance of only 1 to 12 m due to the scanner position in the plot center, which leads to a very high point density. Also Figure 11 shows that with increasing distance of the trees from the trajectory the number of points decreases and thus also the number of modeled branches from the QSM compared to the TLS trees.