The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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Phase Ü - Remote Sensing vs. Ground Truth (17 th Aug. 2007} 
I Remotely Sensed Resulte (NOVI) Ground Truth (Damaged Leaves [%|> | 
r=-0.83 
Figure 7: Percentage of damaged leaves vs. NDVI values - 
Results obtained with MSMS sensor (solid green line) and 
reference data (red dashed line) (preliminary results). 
4. CONCLUSIONS AND OUTLOOK 
In this paper we presented investigations using low-weight and 
low-cost multispectral sensors in combination with mini and 
micro UAVs for remote sensing applications in agronomical 
research. Field experiments including test flights with different 
UAV types and two different sensor constellations demonstrate 
the feasibility and a very promising potential of such very high- 
resolution systems. The investigated multispectral sensors 
consisted of a) an off-the-shelf multi-camera constellation 
which had to be flown in multiple flight missions and b) a 
prototype of a light-weight multi-channel sensor MSMS 
developed at FHNW. Despite the fact that both sensor 
constellations and the test flights were still far from ideal, an 
excellent agreement between the remotely sensed plant health 
status of grapevines with the detailed reference data provided 
by the agronomical specialists of Syngenta AG was found (with 
an overall correlation coefficient of approx. 0.9). The results 
also indicate that the quality of remotely sensed plant health 
assessment is at least equivalent to the current labour-intensive 
ground-based bonification. The main advantages of very high- 
resolution UAV-based remote sensing can be summarised as 
follows: 
• unparalleled very high temporal and spatial resolutions 
• flexible deployment and relatively simple operation of 
micro UAVs (no pilots required) 
• potential for very rapid data acquisition and processing 
Ongoing and future work includes the extension of the 
investigations towards speciality crops other than grapevines 
and towards specialty crop management in general. With 
respect to the MSMS sensor and the corresponding processing 
chain this includes the following development tasks and 
investigations: improvement of the current sensor, design and 
implementation of a robust processing chain addressing special 
issues such as reducing ambiguity problems in the image 
georeferencing process which are caused by the relatively poor 
direct georeferencing capabilities of micro UAVs and (Eugster 
2008) and the repetitive patterns found in typical fields or 
orchards. 
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