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DESCRIBING FOREST STANDS 
USING TERRESTRIAL LASER-SCANNING 
T. Aschoff™ *, M. Thies”, H. Spiecker® 
* Institute for Forest Growth, University of Freiburg, Tennenbacherstr. 4, D-79106 Freiburg, Germany - 
Tobias. Aschoff@iww.uni-freiburg.de, Michael. Thies@iww.uni-freiburg.de, instww@uni-freiburg 
Commission V, WG V/2 
KEY WORDS: Forestry, Inventory, Modelling, DEM/DTM, Laser scanning, Three-dimensional 
ABSTRACT: 
A method for extracting forest parameters is described in this paper. The 3D point clouds derived from phase difference laser 
scanners in forests were processed in a step-wise separation. The first step contains basic filter methods to reduce raw data which, 
separates isolated points or deletes those artefacts resulting from the ambiguity problem. The next step includes using algorithms to 
isolate the main object categories of forest stands: terrain and trees. A digital terrain model (DTM) is calculated as a triangulated 
irregular network (TIN). Based on this, the point clouds are sliced at different heights above ground and included co-ordinates are 
projected in 2D layers. The following steps use image analysis methods to derive the single tree positions and diameters in the 
different layers. A Hough-transformation is used for the detection of the trees and a circle approximation is used to localise the tree 
exactly. For a precise model of a tree stem. a triangulation is conducted using the power crust algorithm. 
The developed methods enable one to measure some of the standard forest inventory parameters. Unfortunately, important 
parameters such as tree species and tree height have not yet been extracted from the scanning data. Indeed, for the acceptance as a 
standard method, these parameters play a key role. Further work has to investigate an automatic way to extract these parameters 
from the point cloud. 
I. INTRODUCTION 
In Germany forest inventories are repeated periodically every 
10 years. In a grid of 100x200m, sample plots with a radius of 
12.5m are assessed. Principle information measured for the 
standard inventory include tree species, diameter at breast 
height (DBH, breast height=1.3m), branch-free bole length and 
tree height. Current recordings of the inventory parameters are 
measured manually. 
In modern forest management an increasing in quality of 
already analysed parameters is not only needed, for scientific 
questions and ecological aspects, new parameters have to be 
investigated. 
Laser scanning offers new possibilities in forest applications. 
The fast collection of data with a high resolution and accuracy 
can provide standard forest inventory information as well as 
parameters describing quality aspects of trees such as taper, 
sweep, number of branches, etc. 
A raw scanned data set contains a high number of points. Each 
scan point belongs to a reflected object such as terrain, tree 
stem or tree crown. To investigate parameters of these objects, a 
classification of the scan points is essential. Furthermore, there 
are a high number of incorrect points in the raw data set. For 
this reason, the step wise separation starts with a pre-filtering of 
the raw point cloud. In the resulting data set several methods 
are used to generate the sought out parameters (see Figure 1). 
  
Corresponding author. 
2. METHODS 
2.1 Data Collection 
For this investigation we used the panorama scanner IMAGER 
5005 by Z+F. This system offers a high accuracy within the 
millimetre-range for a full panorama scan with a maximum 
resolution of 20,000 pixels vertical and 36,000 pixels 
horizontal. To retain our data volume well, our sample plots 
were scanned as a full panorama with a resolution of 5,000 x 
10,000 pixels. The scan data contains, aside from the 3D 
information of the scanned points, information of the intensity 
of the reflected laser beam. The data volume for each scan is 
250 MB. A scan retains the range and intensity parameters with 
16 Bit and 15 Bit respectively (Zoller + Frohlich, 2004). 
The sensor of this scanner utilizes phase difference 
measurements to capture the distance information. The 
IMAGER 5003 is supported for two different ranges: 25.2m and 
53.5m. Because of the ambiguity problem, the system will 
alwavs recognize a scanned point inside the first interval. 
Reflected points in the second or third interval will calculate to 
the first interval. This hardly affects the quality of a forest raw 
point cloud. In a crown of a tree, as well as in brush wood, the 
distance of views are extremely variable. On leaves and 
branches the reflecting area is small. Between them long views 
that arc farther away than the ambiguity interval is frequently 
reached. The resulting point cloud detects in these sections 
incorrectly calculated points. 
Our sample plots were scanned from different view points. The 
scanner positions have a distance of 10m and 15m between 
cach other. This guarantees an overlapping zone of the different 
scans. To register the point clouds artificial targets are used.