Analysis of Rice Field Drought Area Using Unmanned Aerial Vehicle (UAV) and Geographic Information System (GIS) Methods

무인항공기와 GIS를 이용한 논 가뭄 발생지역 분석

  • Park, Jin Ki (Crop Production Technology Research Division, National Institute of Crop Science) ;
  • Park, Jong Hwa (Department of Agricultural and Rural Engineering, Chungbuk National University)
  • Received : 2017.03.01
  • Accepted : 2017.03.23
  • Published : 2017.05.31


The main goal of this paper is to assess application of UAV (Unmanned Aerial Vehicle) remote sensing and GIS based images in detection and measuring of rice field drought area in South Korea. Drought is recurring feature of the climatic events, which often hit South Korea, bringing significant water shortages, local economic losses and adverse social consequences. This paper describes the assesment of the near-realtime drought damage monitoring and reporting system for the agricultural drought region. The system is being developed using drought-related vegetation characteristics, which are derived from UAV remote sensing data. The study area is $3.07km^2$ of Wonbuk-myeon, Taean-gun, Chungnam in South Korea. UAV images were acquired three times from July 4 to October 29, 2015. Three images of the same test site have been analysed by object-based image classification technique. Drought damaged paddy rices reached $754,362m^2$, which is 47.1 %. The NongHyeop Agricultural Damage Insurance accepted agricultural land of 4.6 % ($34,932m^2$). For paddy rices by UAV investigation, the drought monitoring and crop productivity was effective in improving drought assessment method.



Supported by : 한국연구재단


  1. Brown, J. F., B. D. Wardlow, T. Tadesse, M. J. Hayes, and B. C. Reed, 2008. The vegetation drought response index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GIScience and Remote Sensing 45(1): 16-46.
  2. Carlson, T. N., R. R. Gillies, and E. M. Perry, 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer soil water content and fractional vegetation cover. Remote Sensing Reviews 52: 45-59.
  3. Dijk, A., 1986. A crop condition and crop yield estimation method based on NOAA/AVHRR satellite data. Ph.D. Dissertation University of Missouri, Columbia.
  4. Johnson, G. E., V. R. Achutuni, S. Thiruvengadachari, and F. Kogan, 1993. The Role of NOAA Satellite Data in Drought Early Warning and Monitoring, D.A. Wilhite (Ed.), book Drought Assessment, Management, and Planning: Theory and Case Studies, Kluwer Academic Publishers, Boston/Dordrecht/London: 31-49.
  5. Kogan, F. N., 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research 15(11): 91-100.
  6. Kogan, F. N., 1997. Global drought watch from space. Bulletin of the American Meteorological Society 78: 727-736.
  7. KMA (Korea Meteorological Administration), 2015.
  8. McVicar, T. R. and D. L. Jupp, 1998. The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review. Agricultural Systems 57 (3): 399-468.
  9. Park, J. K. and J. H. Park, 2015. Crops classification using imagery of unmanned aerial vehicle (UAV). Journal of the Korean Society of Agricultural Engineers 57(6): 91-97 (in Korean).
  10. Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering, 1974. Monitoring vegetation systems in the Great Plains with ERTS, In: S.C. Freden, E.P. Mercanti, and M. Becker (eds) Third Earth Resources Technology Satellite-1 Syposium. Vol. I: Technical Presentations, NASA SP-351, NASA, Washington, D.C., 309-317.
  11. Tucker, C. J. and B. J. Choudhury, 1987. Satellite remote sensing of drought conditions. Remote Sensing of Environment 43: 243-251.
  12. Wang, L. and J. Qu, 2007. NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing, Geophysical Research Letters 34 (L20405): 1-5.
  13. White, D. H. and B. O'Meagher, 1995. Coping with exceptional droughts in Australia. In: Wilhite, D.A. (Ed.), Drought Network News 7. University of Nebraska, 13-17.
  14. Wilhite, D. A. and M. H. Glantz, 1985. Understanding the drought phenomenon: the role of definitions. Water International 10: 111-120.
  15. WMO (World Meteorological Organization), 1975. Droughts and Agriculture. WMO Technical Note 138.
  16. Xie, Z. C. Roberts, and B. Johnson, 2008. Object-based target search using remotely sensed data: A case study in detecting invasive exotic Australian Pine in south Florida, ISPRS Journal of Photogrammetry & Remote Sensing 63(6): 647-660.
  17. Zhang, C. and J. Kovacs, 2012. The application of small unmanned aerial systems for precision agriculture: a review, Precision Agriculture 13: 693-712.