# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 2 State of the Art
### Tree Detection and Tree Segmentation from Point Clouds  
The successful modeling of individual trees and their crown structures require the detection of each tree in the 3D point cloud data. The fact that this task remains an active field of research illustrates the complexity of this process. When using ALS, the analysis of the digital crown model is the commonly used approach [26]. For TLS data, many algorithms are based on the analysis of horizontal TLS point cloud slices in which the circular-shaped trunks and their respective diameters are detected using the Hough-transformation, or by least-squares fitting circle or ellipse functions [27; 28; 29; 30]. The published results of tree detection in single scan data show that the tree detection rate decreases with increasing stem density [2], for example 82% for 153-326 stems/ha [31]; 86.7-100% for 212-410 stems/ha [5]; 73.3% for 358-1042 stems/ha [32], 73.4% for 605-1210 stems/ha [33]. [PERSON] & [PERSON] [34] perform a tree detection after a voxel space transformation using morphological operations with shape and neighborhood information (detection rate of 97%).  
By placing cylinders into the detected tree positions, an automated tree segmentation can be achieved [5; 9]. A disadvantage of this approach is that crowns may not be fully captured (cylinder radius too small) or that adjacent crowns or understory trees are falsely included in the segmentation. A recently published approach of [PERSON] et al. [35] employs an automated reliable segmentation of two deciduous tree species based on an algorithm that has been previously developed for tree modeling [10]. For isolated trees common in MLS data of urban areas, methods of segmentation approaches involving stem detection exist. [PERSON] et al. [16] perform a tree segmentation by clustering of vertical line segments and the analysis of their spatial extent. [PERSON] et al. [23] use the eigenvectors of each point and their neighbors to classify trunk points. A horizontal orientation of the normal vector onto the described point cluster is used to classify the points of the trunk. [PERSON] et al. [19] apply a Hough-transformation to remove flat elements (facades) and merge the remaining smaller segments using a connectivity analysis. [PERSON] et al. [36] present a tree segmentation from MLS data in combination with image data in which overlapping crowns are taken into consideration. After the removal of ground points horizontal slices are analyzed with respect to their spatial extent and point density and finally a tree segmentation is performed. [PERSON] et al. [37] separate adjacent trees based on horizontal voxel layers. Using criteria such as size and perimeter, specific objects (facades, cars, etc.) that do not represent trees are excluded. The differentiation of neighboring crown parts is ultimately performed using a modified region growing algorithm that accounts for distances. [PERSON] et al. [38] detect urban trees from TLS, MLS and ALS data using manual, automatic and semi-automatic methods. They report a detection rate of 79% for the manual MLS method. Up to now, there is a lack of suitable methods for fully automatically registering the MLS data obtained in the forest. Moreover, current TLS tree segmentation algorithms require refinement in order to perform well on low resolved MLS point clouds.