2024-03-29T13:50:24Zhttp://doidb.wdc-terra.org/oaip/oaioai:doidb.wdc-terra.org:51352014-04-24T15:27:24ZDOIDBDOIDB.TR32DB
10.5880/TR32DB.KGA94.8
Kooistra, Lammert
Suomalainen, Juha
Iqbal, Shahzad
Franke, Jappe
Wenting, Philip
Bartholomeus, Harm
Mücher, Sander
Becker, Rolf
Crop monitoring using a light-weight hyperspectral mapping system for unmanned aerial vehicles: firs results for the 2013 season
Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten
2014
UAV
Hyperspectral
Remote Sensing
Vegetation Index
Chlorophyll
Potato
550 Earth sciences
Bendig, Juliane
Bareth, Georg
CRC/TR32 Database (TR32DB)
University of Cologne, Regional Computing Centre (RRZK)
2014-04-14
eng
Workshop paper
8 Pages
11504 Kilobytes
application/pdf
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
To investigate the opportunities of unmanned aerial vehicles (UAV) in operational crop monitoring, we have developed a light-weight hyperspectral mapping system (<2 kg) suitable to be mounted on small UAVs. It is composed of an octocopter UAV-platform with a pushbroom hyperspectral mapping system consisting of a spectrograph, an industrial camera functioning as frame grabber, storage device, and computer, a separate INS and finally a photogrammetric camera. The system is able to produce georeferenced and georectified hyperspectral data cubes in the 450-950 nm spectral range at 10-100 cm resolution. The system is tested in an agronomic experiment for a potato crop on a 12 ha experimental field in the south of the Netherlands. In the experiment UAV-based hyperspectral images were acquired on a weekly basis together with field data on chlorophyll as indicator for the nitrogen situation of the crop and LAI as indicator for biomass status. Initially, the quality aspects of the developed light-weight hyperspectral mapping system will be presented with regard to its radiometric and geometric quality. Next we would like to present the relations between sensor derived spectral measurements and crop status variables for a time-series of measurements over the growing season. In addition, the spatial variation of crop characteristics within the field can be adopted for variable rate application of fertilizers within the field. The outcome of the experiments should guide the operational use of UAV based systems in precision agriculture systems.