DOI: 10.5593/sgem2017/23/S10.041


J. Sedina, E.Housarova, P. Raeva
Monday 11 September 2017 by Libadmin2017

References: 17th International Multidisciplinary Scientific GeoConference SGEM 2017, www.sgem.org, SGEM2017 Conference Proceedings, ISBN 978-619-7408-03-4 / ISSN 1314-2704, 29 June - 5 July, 2017, Vol. 17, Issue 23, 331-338 pp, DOI: 10.5593/sgem2017/23/S10.041


Remotely Piloted Aircraft Systems (RPAS), which have become more popular in recent years, can obtain data on demand in a short time with a resolution within centimeters. Data collection is environmentally friendly and low-cost from an economical point of view; of course it is best for small areas, such as square km’s, only by using winged drones or in a few hectares by use of multi-copters. Both types have their advantages and disadvantages. Our laboratory of photogrammetry has, since 2012, focused on using RPAS (drones, UAV) for the mapping or monitoring of agriculture. For this purpose it is better to use winged drones – we have EBee drones at our disposal with new equipment including a thermal imager, multispectral imager, NIR, NIR red-edge and VIS camera. This is typical remote sensing equipment and is now usable on small areas for local case projects. Last year, we started new projects in precise agriculture research, and we tested the equipment on an agricultural site near Plana city (in the western part of the Czech Republic), near the village of Vysoké Sedlište. On the test site, we located fields of corn, rye and grassland. We collected data from the end of March till August in 2016 with a thermal and multispectral imager. A typical flight lasted 30 minutes, taking 200 multispectral images or 6000 thermal images (due to the order of magnitude, lower resolution images with 640x512 pixels were collected with 90% overlapping and were much faster by multispectral camera). Outputs from these instruments were thematic maps, NDVI progress, thermal index maps and unsupervised classification of five spectral channels using remote sensing software’s like Geomatica or Envi. An output showed unequal development of vegetation in different locations.

Keywords: RPAS, precision agriculture, NDVI, multispectral camera, thermal imager