International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol XXXV, Part B3. Istanbul 2004 
  
  
   
   
   
    
   
  
    
   
   
    
  
   
    
   
  
  
  
  
    
   
    
       
   
   
    
   
   
   
   
   
   
   
   
   
   
   
    
   
    
   
  
  
     
   
  
  
  
   
  
  
  
  
  
  
  
  
  
  
  
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Figure 4. Tree point triangulation by power crust algorithms. 
Left is the points cloud of a oak tree with 800,000 
points. In the middle the reduced point cloud with 
50,000 scan points. Right the generated 
triangulation. 
the middle of a stem, a fitted straight line through the diameter 
values is used. This is also used for a tapering check. All 
detected trees have to show decreasing diameter with increasing 
height. 
2.5 TIN Tree Models 
A precise model of a tree stem is achieved by a triangulation of 
its surface. For triangulation the points of an object have to be 
separated from the full point cloud. Ideally, only points on the 
surface of the object are used for the triangulation. In order to 
separate trees in the point cloud several methods are combined. 
The raw point cloud is filtered to eliminate false points due to 
the ambiguity problem and the divergence of the laser beam. 
The methods are described above. In the resulting range image 
most errors are removed. To separate the single tree stem, a 
region growing method is used. Finally, an interactive 
monitoring processes the data by using selection tools and an 
eraser in the intensity or range image. 
At least two views are needed to generate an all-around model 
of a tree. To minimize the errors on the tangential arca of the 
point clouds, in general three or four views of a tree are used. 
The point cloud of one tree with four views easily reaches up to 
800,000 points (see Figure 4 left). This high number of scan 
points is reduced for a good triangulation. Usually 
approximately 50,000 points are used for a tree stem of 8 m 
length (sec Figure 4 middle). 
To triangulate the scan points the power crust algorithm is used 
(Amenta, 2001) (see Figure 4 right). The achieved triangulation 
offers a high accuracy of volume calculations for a tree stem. 
Volume is an important parameter for the price of a stem as 
well as for the investigation of tree growth. With a repeated 
  
scanning in a lime series, growth can be calculated and 
ecological evaluations become possible. 
The resulting triangulation can be provided in the 3D 
visualisation format VRML and in a XML data format. This 
offers an easy visualization and transfer to application in other 
processes. 
2.6 Reconstruction of Tree Crowns 
For ecological as well as economic aspects the tree crown 
provides information about the actual state of a tree. Interactive 
measuring in the point cloud is possible and enables the 
investigation of the tree crown. For a standard application an 
automatic reconstruction of the tree crown is needed. A method 
for automatic reconstruction has already been developed 
(Pfeifer, 2004; Gorte, 2004). 
3. CONCLUSION AND FURTHER PLANS 
The shown methods for describing forest stands enable a part of 
forest inventory parameters to be measured remotly. In order to 
be used in a standard application, the detection of the tree 
species is essential. So far the tree species is not automatically 
recognized and is defined by the operator manually. 
Furthermore, the tree height is an essential forest parameter as 
well. Because the tops of the trees can not be automatically 
detected, an extrapolation of the tree height by using the 
diameters at different heights is necessary. Unfortunately, a 
correct calculation of the tree height has not yet been reached. It 
seems that the reasons include the limited height of free views 
to stems and the accuracy of the calculated tree diameters. For a 
good extrapolation, the highest diameter of the tree stem which 
has an accurate value is most importand. These height levels are 
mostly hidden by branches so it is difficult to obtain an accurate 
value. 
In order to use laser scanning in standard forest inventories, the 
tree species and the tree height have to extracted. For this 
rcason, in the future, the methodology for tree recognition has 
to be verified. The calculation of the tree stem diameter in the 
higher part of the stem also has to improve. To recognize tree 
species an automatic mode of detection has to develop. 
A second aspect for acceptance of these methods in forest 
inventories is the economic aspect. For this reason a high level 
of automatism is essential. The developed methods indicate 
semi automatic data processing. With a better detection of 
outliers in the raw data, as well in the detected parameters, the 
manual interaction can further be reduced. For this reason, 
further work will continue in the development and analysis of 
methods for the better detection and deletion of outliers. 
   
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