The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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2.2 Remote Sensing Payload 
A market evaluation at the time of the study showed that there 
were no suitable multispectral sensors available which could 
have provided multi-channel imagery in the bands Red, NIR 
(and preferably also Green) at a total system weight of 1 kg or 
less. In order to proceed with the initial feasibility tests in the 
2006 vegetation period, it was decided to use a combination of 
two different sensors for RGB and NIR, which had to be flown 
in two subsequent flight missions. The two imaging sensors 
were: 
NIR sensor Sony SmartCam - 1/2" monochrome CCD with 
1280x1024 pixels; onboard CPU with 400 MHz CPU, 256 MB 
DDR-SDRAM, and Windows XP embedded; Compact Flash 
on-board storage up to 4 GB; camera body weight 400g (w/o 
optics or power supply); precision optical lenses from 
Schneider Kreuznach, corrected from 400 to 1000 nm; high- 
pass filter with a cut-on wavelength of 780 nm (to approx. 1000 
nm). 
Figure 2: Mini UAV of weControl (Zurich) 
with the RGB sensor Canon EOS 20D. 
RGB sensor Canon EOS 20D - commercial digital SLR camera; 
CMOS chip with 22.5 x 15.0 mm and 3504 x 2336 pixels; 
weight 770 g; radiometric resolution (RAW format) of 12 bits; 
standard lens with fixed focal length; IR blocking filter with 
cut-off wavelength at approx. 720 nm. 
The optical lenses of the two camera systems were chosen so 
that they would yield the same ground sampling distances 
(GSDs) from identical flying heights. 
2.3 Field Test Campaign 
Due to the available sensor constellation, the acquisition of the 
RGB and NIR imagery with a GSD of approx. 7 cm had to be 
carried out in two separate pre-programmed flights. The flights 
were planned as a photogrammetric strip with 4 images and an 
overlap of approx. 60%. The imagery was acquired at a flying 
height of 100 metres above ground resulting in image scales of 
approx. Ull'OOO (for the RGB imagery) and 1:13750 (for the 
NIR imagery). Precise ground control with an accuracy of 
approx. 2 cm horizontal and 3 cm vertical was established 
(Annen u. a., 2007). 
Figure 3: Section of the generated false colour true orthoimage. 
2.4 Processing and Results 
The goal of the processing phase was to derive plant health 
figures for each plot which can directly be related to those used 
by the field specialists, who normally express the plant status in 
percentage of damaged leaves. The processing steps can be 
summarised as follows (Brosi, 2006) and (Annen u. a., 2007): 
• extraction of raw imagery from both sensors 
• geo-referencing of Red and NIR imagery 
• co-registration of Red & NIR imagery by a true 
orthoimage 
• radiometric corrections using radiometric field targets 
• masking out of 'background' soil using a GIS-based 
geometric buffer for the grapevine rows 
• calculation of uncalibrated or 'raw' NDVI values 
• matching of raw NDVI values with leaf damage values of 
control plots by means of a weighted linear regression 
• calculation of leaf damage values for all test plots by 
means of linear interpolation 
The described sensor and flight constellation, namely the use of 
two different and radiometrically uncalibrated 'off-the-shelf 
sensors and the spatially disparate exposure centres for the RGB 
and NIR imagery, introduced a number of challenges into the 
processing chain. The very special challenge of co-registering 
the very high-resolution RGB and NIR channels acquired in 
separate flights to an accuracy of 1-2 pixels at a GSD of only 7 
cm was successfully met by generating a true orthoimage (see 
Figure 3). The underlying elevation data modelling the surface 
of the grapevines was derived using the GIS-based position 
information of the grapevine rows in combination with an 
average height for the grapevine cultivation. 
In the subsequent investigations a number of vegetation indices 
were successfully used to assess the plant health within a grape 
vine test field. However, it is shown in (Brosi, 2006) that 
excellent and robust results can be achieved by using the 
standard NDVI. In the case our grapevine test field the 
percentage of damaged leaves determined with remote sensing 
agreed to within 10% with the comprehensive ground truth 
information (see Figure 1). This indicates that the remotely 
sensed solution roughly yields the same accuracy level as the 
very labour intensive ground-based bonification. 
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