The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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3.4 Results 
Using the acquired UAV-images, a DSM of the lower part of 
the Randa rockslide with 15cm resolution was generated (see 
Figure 6). The complete site was documented with the Helimap 
system. For the Helimap system, the pixel size was 6cm to 8cm. 
In comparison to the flight defined for the mini UAV-system, 
the Helimap flight was controlled by a pilot, who followed the 
flight lines with an accuracy of 30m. Therefore, for the whole 
area the scale varies depending on the complexity of the terrain. 
Furthermore, the Helimap system acquired the cliff with an 
oblique field of view, which evokes the gaps through occlusion 
in the data set. For the laser scan, the final point density was 
approximately 3pt/m 2 (see Figure 6). 
The visual comparison of the extracted UAV-DSM and the 
LiDAR-DSM shows clearly that the fine structure of the cliff 
could be modeled out of the UAV-DSM, while the LiDAR- 
DSM shows big holes and less resolution (see Figure 6). 
The analysis of the unexpected turning of the UAV-system 
during the flight showed that the effect was caused by the fact 
that the neighboring points of the flight plan were defined to 
close to each other. Furthermore, the effect evoked while the 
neighboring flight lines had a distance in horizontal and vertical 
direction, which caused a miss-interpretation in the flight 
control software. Hence, during the flight, one predefined data 
acquisition point was skipped and the turning occurred during 
the flight at Randa. Therefore, the flight control software was 
improved to handle autonomous flights flown with this type of 
configuration. 
4. MAIZE FIELD 
4.1 Project Aims 
The goal of this study was to analyze the influence of terrain 
characteristics on pollen dispersal in maize. For that application, 
the task was to generate 3D elevation models as well as to use 
the oriented images for a stereoscopic inspection of maize 
fields, and to combine them with high resolution cross 
pollination data. The elevation model and the stereoscopic 
measures were used to determine the heights of pollen donor 
and receptor plants with respect to their absolute orthometric 
height. For these investigations, two test areas (2005: A and 
2006: B) were defined. 
Parameter 
Value 
Image scale 
1:4000 
Side / end lap 
75% / 75 % 
Flying height above ground 
~ 80 m 
Camera 
Canon EOS 20D 
Focal length (calibrated) 
20.665, RMSE 1.5e-003 mm 
Pixel (Image format) 
8.25 megapixels (3520x2344) 
Flying velocity 
3 m/s 
Table 2: Flight parameters for the image observation of the 
maize field in 2005 and 2006 
4.2 Field work 
After anthesis, digital pictures were captured with a still-video 
camera (Canon D20) mounted on our mini UAV-system 
(Eisenbeiss, 2007). 
In the first step, a flight planning was performed for the 
autonomous flight. The image resolution was defined to have 3 
cm per pixel in object space. Furthermore, the maximum flying 
height was set to 100m above ground, due to the nearness to 
Zurich airport. With the selected camera the remaining parameters 
were calculated (see Table 2). 
After definition of the flight parameters, the area of interest was 
roughly identified in the Swiss National Map 1: 25 000 provided 
by swisstopo. With the defined flight parameters, a trajectory was 
calculated by starting at one comer of the field and 3D- 
coordinates of the flight trajectory were established. The 
autonomous flight was done using our UAV system. Following 
the flight trajectory in the stop mode for experiment A, the 
camera captured the images automatically on the predefined 
positions in the cruising flight mode for experiment B. In the stop 
mode the helicopter flew autonomously to each image acquisition 
point and the operator triggered the image via radio link, while 
during the cruising mode the image acquisition was completely 
autonomous. 
4.3 Data Processing 
Due to the autonomous flight with stop points we could capture 
all images for experiment A in 20 minutes. By using the cruising 
flight modus we reduced the flight time for experiment В to 5 
minutes. 
For the image triangulation for experiment A 2-3 tie points were 
measured manually, while for the experiment В the orientation 
values calculated using the Kalman filter implemented in the 
navigation unit on board the mini UAV were used. For 5-10 
percent of the images the 2-3 manually measured points were not 
enough, since during the image acquisition the light breeze moved 
the leaves of the plants slightly. Therefore, the measurement of 
more manual points was essential (Eisenbeiss, 2007). The 
manually measured points (experiment A) and the approximate 
positions of the camera (experiment B) served as initial values for 
automated tie point extraction. For the generation of the tie points, 
the standard procedure implemented in LPS (Leica 
Photogrammetry Suite) was used. Therefore, the initial 
approximations of each image location within the projects had to 
be defined. Hence, for area В the function for exterior orientation 
parameter input and for area A the function for manual tie point 
measurement were selected. After the generation of tie points, the 
control points were manually measured in the images. 
Since the maize fields had a highly repetitive structure in the 
image, it was necessary to detect blunders before doing image 
orientation. In LPS, a blunder can be detected only by manual 
checking until the project has proceeded to the point where a 
bundle adjustment is possible. Therefore the blunder detection 
with ORIMA (Leica Geosystems) integrated in LPS and ISDM 
(Image Station Digital Mensuration, Intergraph) was attempted. 
These software packages allow a relative orientation for each 
stereo pair, multi-image blocks and the detection of blunders. 
Finally, with both software packages the blunders were detected 
and the orientation of the UAV-images was performed with an