A LIGHT-WEIGHT MULTISPECTRAL SENSOR FOR MICRO UAV - 
OPPORTUNITIES FOR VERY HIGH RESOLUTION AIRBORNE REMOTE SENSING 
S. Nebiker 3, *, A. Annen 3 , M. Scherrer b , D. Oesch c 
a FHNW, University of Applied Sciences Northwestern Switzerland, School of Architecture, Civil Engineering and 
Geomatics, Inst, of Geomatics Engineering, CH-4132 Muttenz, Switzerland - (stephan.nebiker, 
adrian.annen)@fhnw.ch 
b FHNW, University of Applied Sciences Northwestern Switzerland, School of Engineering, Institute of Automation, 
CH-5210 Brugg-Windisch, Switzerland - marco.scherrer@fhnw.ch 
c Syngenta Crop Protection AG, Agronomic Information Services, CH-4002 Basel, Switzerland - 
david. oesch@syngenta. com 
ThS 23 - UAV for Mapping 
KEY WORDS: UAV, airborne remote sensing, multi-spectral image, digital photogrammetry, calibration, crop management, multi- 
spectral remote sensing, imaging platforms, image interpretation 
ABSTRACT: 
In this paper we present the prototype of a light-weight multi-spectral sensor which can be flown on a micro UAV and we discuss 
the promising results from two field tests which show the excellent potential for assessing plant health in agronomical research. We 
start out by illustrating the gap between air- and space-based remote sensing (RS) on the one side and ground-based RS on the other. 
We highlight the need for (very) high resolution remote sensing offered by low altitude airborne platforms such as mini or micro 
UAVs (unmanned aerial vehicles). For this purpose, we first discuss the specific characteristics and requirements of typical 
applications requiring very high resolution RS. We then look into recent developments in light-weight UAV technologies and 
present the micro UAV which served as platform for the sensor development and tests at the University of Applied Sciences 
Northwestern Switzerland (FHNW). In the following section we provide a description and discussion of the 
MultiSpectralMicroSensor (MSMS), the prototype of a light-weight multispectral sensor developed at the FHNW. We further 
describe two field campaigns with two different types of UAV platforms and MS sensors and discuss the obtained results, which 
clearly demonstrate the excellent potential of very high-resolution micro UAV based remote sensing applications. 
1. INTRODUCTION 
Recent progress in the development of miniature flight control, 
propulsion and light-weight airframe technologies on the one 
hand and the continuing trend towards miniature imaging 
sensors on the other, bear the potential for creating a new 
generation of light-weight airborne remote sensing platforms 
offering very high spatial resolution and an unparalleled 
operational flexibility. While the development of Unmanned 
Aerial Vehicle (UAV) technologies was and still is driven by 
military applications (Bento, 2008), civilian applications are 
rapidly catching up and are encompassing fields such as disaster 
monitoring, fire detection, pipeline inspection, site inspection, 
real-time monitoring (Eugster & Nebiker, 2007), traffic 
monitoring, mapping, cultural heritage (Eisenbeiss, 2004), 
movie production, and increasingly forestry and agriculture. 
In agronomical research new substances and products such as 
herbicides, pesticides, fungicides or fertilisers are tested on field 
test sites. Today, these field tests include labour-intensive 
typically weekly visual inspections of leaf properties by 
experienced staff. In this qualitative method the assessment of 
plant health is often based on number, size and condition of 
plant leafs. Agronomical researchers and companies are in 
permanent search for new methods and procedures helping 
them to economise their field tests while maintaining or even 
* Corresponding author. 
improving the quality and reliability of today's field test 
procedures. Optical satellite-based remote sensing is 
successfully used in supporting large scale field tests. However, 
the prevailing small test plots with sizes around one square 
metre and the need for short and reliable revisit periods require 
new solutions. 
There is an abundance of literature on reflective optical remote 
sensing in agriculture, aiming at relating spectral reflectance 
properties of plants and soils to their agronomic and biophysical 
properties. Very comprehensive and valuable literature reviews 
include (Pinter et al., 2003) on remote sensing in crop 
management and (Dorigo et al., 2007) on remote sensing for 
agroecosystem modelling. The majority of operational 
procedures for estimating plant properties make use of the 
distinct dissimilarities in reflectance properties between the 
visible and NIR wavelengths. Vegetation indices (VI), 
computed as differences, ratios or linear combinations of 
reflected light in the visible and NIR wavebands, e.g. (Tucker, 
1979) or (Kurz, 2003), provide a very simple and x elegant 
method for representing these dissimilarities and are also Used 
in this study. 
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