The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B4. Beijing 2008 
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histograms, in particular for the data take on 30 Ih April from 
2000m, the peak is more flat with offsets. As the difference of 
DSMs was calculated over urban area for every pixel, there are 
many reasons causing these differences. As mentioned above, 
systematic offset emerge from systematic errors in the D-GPS 
coordinates, but could emerge also from systematic differences 
between the LIDAR reference DEM and the 3K DSM. One 
difference is that the 3K DSM contains the vegetation surface, 
whereas in the LIDAR reference DEM the vegetation was 
eliminated. Besides, in the 3K DSM generation, many errors are 
caused by shading effects, moving objects, surfaces with low 
texture, etc. 
10 
% 
5 
0 
H=2000m B/Z=1:4 ? 
17/06/2007 
n\=40000 
Difference in [m] 
Figure 6 Histogram of DSM difference image (3K DSM 
minus Reference DEM) for three different data takes 
over urban area 
Table 3 shows the results of the performance tests of the DSM 
algorithms in terms of the point density, the calculation time, 
and the number of outliers. From 1000m a. G. the highest point 
density at 4.4 points per square meter is reached after region 
growing. Hence, the calculation time is also very high at 
together 655 minutes for a square kilometre, which is definitely 
too long for near real time disaster monitoring. Point density 
and processing time reduce with higher flight heights, e.g. from 
2000m the reached point density is only 1.35 resp. 0.65 points 
per square meter and the calculation time 204 resp. 241 minutes. 
The difference between the point densities from the 2000m data 
takes is mainly caused by the “image decorrelation” over urban 
areas. The decorrelation is related to the base-to-height ratio, 
e.g. the data take on 17 th June has a higher base-to-height ratio 
(1:4) as the data take on 30 th April (1:17). 
H [m] 
Hierarchical 
matching 
Region 
growing 
p/m 2 
t/km 2 
p/m 2 
t/km 2 
% 
30/04/2007 
2000 
0.07 
70 
1.35 
134 
4.5 
30/04/2007 
1000 
0.19 
250 
4.44 
405 
4.6 
17/06/2007 
2000 
0.02 
101 
0.65 
141 
4.5 
Table 3 Performance of DSM algorithms in terms of point 
density [p/m 2 ], calculation time in minutes [t/km 2 ], 
and number of outliers in [%] 
4. APPLICATIONS FOR NEAR REALTIME DSM 
4.1 Monitoring of slides and avalanches 
Land slides, slope failures or other movement of masses are a 
big natural threat in montane regions. Facing natural disasters 
like this, the BOS and rescue forces need detailed information 
about the situation in these regions, which could partly derived 
from remote sensing imagery. 
2000 2006 2007 
Figure 7 The slope failure in Austria in the years 2000, 2006, 
and 2007 
A study in the years 2006 and 2007 examined the potential of 
the 3K camera system to monitor a large area slope failure. Test 
area was Vorarlberg in Austria, where a slope failure moving 
fast at times during the last 150 years threads human villages. In 
this area, a weak layer parallel to the surface is mainly causing 
the natural event. The tear-off edge of the slope failure moves 
uphill and threads a small village above the slide. Figure 7 
shows the change of the slide since the year 2000. 
3K images were acquired on the 27 th April 2007 in three flight 
strips from 1500m to 2000m a. G. Around 25 Ground control 
points were measured with GPS and reference DSMs from the 
years 2000, 2003, and 2006 were acquired by the Austrian 
cartographic office. 
Using the 3K image data, a DSM of the region around the slide 
was generated according to the proposed processing scheme. 
The absolute accuracy of the DSM was validated with GCP. 
Thus, the accuracy of the DSM in position is around 14cm and 
in the height around 40cm. Given height variations of several 
meters, the accuracy is sufficient for this kind of slide. 
Figure 8 shows the difference of the DSM between the 3K 
based DSM from the year 2007 and the DSM from the year 
2006. In this case, enormous movement of masses between the 
two acquisition dates could be detected in the difference image. 
The surface height varies up to 30m. 
Around 4.5% of all matching points were detected as outliers 
during the forward intersection. Outliers detected in the 
bidirectional matching before are not included in this number. 
The remaining outliers which were not detected are below one 
point per million (moving objects not included). 
The movement of the masses can be better seen in the cross 
section at a profile line through the whole slope (see figure 9). 
It could be seen, that the movement of the slide downhill is 
separated in different zones of erosion and accumulation, i.e. in