## 3 Methodology  
### Automatic Single Tree Segmentation  
The processing workflow is a slightly modified version of the algorithm described in [6] and [17]. The main modification is that multispectral line scanner data was integrated in the procedure as an additional dataset with the objective to improve the separation ofiferous and deciduous trees:  
1. In the first step the study area is classified into the classes: (a)iferous trees and (b) deciduous trees. For this purpose an unsupervised classification procedure as described in [20] is employed: First, a forest mask is generated using intermediate reflections of the ALS data. Due to the characteristic structure of trees in addition to gaps in the forest canopy, intermediate reflections from branches, stems and leaves between the DSM and DTM can be utilized to delineate the forest area. Inside of the forest mask, gray value combinations of the NIR and green channel of the multispectral line scanner data are used to define a two-dimensional feature space. NIR and green channel were selected, since both channels revealed the main difference in the spectral reflectance of coniferous and deciduous trees in the study site. The assumption is that two distinct clusters can be isolated in the feature space; one represents coniferous trees and the other one deciduous trees. For this purpose the feature space is segmented iteratively until exactly two distinct clusters are generated. After that, pixels of the entire image are classified according to \begin{table}
\begin{tabular}{c c c c} \hline
**Plot** & **Age [years]** & **Age class*** & **Number of pines measured in the field** \\ \hline
1 & 28 & II & 78 \\
2 & 30 & II & 87 \\
3 & 55 & III & 14 \\
4 & 55 & III & 21 \\
5 & 55 & III & 27 \\
6 & 80 & IV & 14 \\
7 & 80 & IV & 19 \\
8 & 85 & V & 20 \\
9 & 123 & VII & 8 \\ \hline \end{tabular}  
*age classes are defined as: II = 21–40 years, III = 41–60 years, IV = 61–80 years  
\end{table} 
Table 3: Overview of the field plots with the mean age of the dominant tree layer and the number of pines measured in the field. their affiliation to one of the clusters. Next, the definition domain of the study site is reduced to the first class (all regions with coniferous trees). 2. In the second processing step individual coniferous tree crowns are segmented based on the DSM from ALS data. DSM based segmentation techniques for crown delineation were suggested by several authors e.g., [21-23]. Hypothetically each convex shape in the DSM represents an individual tree crown. The segmentation is achieved with a \"pouring algorithm\". The algorithm works similar to \"watershed segmentation\". However, for the application of watershed segmentation it is necessary to invert the surface model which is not needed for the pouring segmentation because local maxima are used as the initial points. Local maxima are defined as pixels in the surface model which either alone or as a plateau have higher values than their direct neighbors. Thus, the maxima represent potential tree tops. The boundaries between tree crowns appear as valleys in the surface model. Starting from the maxima regions are expanded down to the deepest points of the valleys as long as there are chains of pixels in which the height values become smaller, similar to water drops flowing downwards in all possible directions [24]. 3. Due to the fact that the crown segments are often too large, the tree crowns are corrected in the third processing step with the help of a \"ray-algorithm\". The assumption is that height changes within an individual crown must be continuous. Thus, virtual rays between the tree tops (local maxima) and border points are generated. New border points are created if there is an interruption in the continuous height changes. A result of the automatic segmentation is shown in Figure 3. The figure shows a pine stand with age class III (the trees are approximately 55 years old). For better visual interpretation the irregularly shaped crown regions were transformed into circles.