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Selection of Optimal Vegetation Indices and Regression Model for Estimation of Rice Growth Using UAV Aerial Images

  • Lee, Kyung-Do (Department of Agricultural Environment, National Institute of Agricultural Sciences, RDA) ;
  • Park, Chan-Won (Department of Agricultural Environment, National Institute of Agricultural Sciences, RDA) ;
  • So, Kyu-Ho (Department of Agricultural Environment, National Institute of Agricultural Sciences, RDA) ;
  • Na, Sang-Il (Department of Agricultural Environment, National Institute of Agricultural Sciences, RDA)
  • Received : 2017.07.02
  • Accepted : 2017.11.04
  • Published : 2017.10.31

Abstract

Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to select optimal vegetation indices and regression model for estimating of rice growth using UAV images. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110 and Cannon IXUS camera during farming season in 2016 on the experiment field of National Institute of Crop Science. Before heading stage of rice, there were strong relationships between rice growth parameters (plant height, dry weight and LAI (Leaf Area Index)) and NDVI (Normalized Difference Vegetation Index) using natural exponential function ($R{\geq}0.97$). After heading stage, there were strong relationships between rice dry weight and NDVI, gNDVI (green NDVI), RVI (Ratio Vegetation Index), CI-G (Chlorophyll Index-Green) using quadratic function ($R{\leq}-0.98$). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images.

Acknowledgement

Supported by : Rural Development Administration

References

  1. Bendig, J., A. Bolten, S. Bennertz, J. Broscheit, S. Eichfuss, and G. Bareth. 2014. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging, Remote Sens. 6(11):10395-10412. https://doi.org/10.3390/rs61110395
  2. Chae, J.C., S.J. Park, B.H. Kang, and S.H. Kim. 2006. Crop cultivation. Hyangmunsa. Seoul. 434.
  3. Cohen, W.B. 1991. Response of vegetation indices to change in three measures of leaf water stress. Photogramm. Eng. Remote Sensing. 57(2):195-202.
  4. Gitelson, A.A., Y.J. Kaufman, and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289-298. https://doi.org/10.1016/S0034-4257(96)00072-7
  5. Gitelson, A.A., Y.J. Kaufman, R. Stark, and D. Rundquist. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80:76-87. https://doi.org/10.1016/S0034-4257(01)00289-9
  6. Gitelson, A.A., Y. Gritz, and M.N. Merzlyak. 2003. Relationships between leaf chlorophyll content and spectral reflectance algorithms for non-destructive chlorophyll assessment in higher plants. J. Plant Physiol. 160:271-282. https://doi.org/10.1078/0176-1617-00887
  7. Hong, S.Y., J.T. Lee, S.K. Rim, W.K. Jung, and I.S. Jo. 1998. Estimation of paddy rice growth increment by using spectral reflectance signature. Korea. J. Remote Sensing. 14(1):83-94 (in Korean).
  8. Hong, S.Y., J.Y. Hong, Y.H. Kim, and Y.S. Oh. 2007. Measurement of backscattering coefficients of rice canopy using a ground polarimetric scatteromneter system. Korea. J. Remote Sensing. 23(2):145-152 (in Korean).
  9. Hong, S.Y. and S.K. Rim. 2000. Monitoring of rice growth by RADARSAT and Landsat TM data. Korean J. Agric. For. Meteorol. 2(1):9-15 (in Korean).
  10. Hong, S.Y., Y.H. Kim, E.Y. Choe, Y.S. Zhang, Y.K. Sonn, C.W. Park, K.H. Jung, B.K. Hyun, S.K. Ha, and K.C. Song, 2010. Geographic information system and remote sensing in soil science. Korean J. Soil Sci. Fert. 43(5):684-695 (in Korean).
  11. Hong, S.Y., J.N. Hur, J.B. Ahn, J.M. Lee, B.K. Min, C.K. Lee, Y.H. Kim, K.D. Lee, S.H. Kim, G.Y. Kim, and K.M. Shim, 2012. Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea, Korea. J. Remote Sensing. 28(5):509-520 (in Korean). https://doi.org/10.7780/kjrs.2012.28.5.4
  12. Hunt, E.R., P.C. Doraiswamy, J.E. McMurtrey, C.S.T. Daughtry, E.M. Perry, and B. Akhmedov. 2013. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Appl. Earth Obs. Geoinf. 21:103-112. https://doi.org/10.1016/j.jag.2012.07.020
  13. Jordan, C.F. 1969. Derivation of leaf area index from quality of light on the forest floor, Ecology. 50:663-666. https://doi.org/10.2307/1936256
  14. Kim, Y.H. and S.Y. Hong. 2007. Estimation of rice grain protein contents using ground optical remote sensors. Korea. J. Remote Sensing. 24(6):551-558 (in Korean).
  15. Kim, Y.H., S.Y. Hong, and H.Y. Lee. 2010. Construction of X-band automatic radar scatterometer measurement system and monitoring of rice growth. Korea. J. Soil Sci. Fert. 43(3):374-383 (in Korean).
  16. Kim, S.H. 2016. A study on the diffusion of Korean agricultural ICT and role of the agricultural cooperative federation using the theory of technology adoption life cycle and chasm. Cooperative management review 45:1-27 (in Korean).
  17. Korean Statistical Information Service Homepage. http://www.kosis.kr/Acessed 12 May 2017.
  18. Lee, B.O., J.W. Yoon, J.H. Yang, and C.Z. Jin. 2016a. Strategies for the value innovation of agriculture in Korea. J. Agri. Life Environ. Sci. 28(1):43-51 (in Korean).
  19. Lee, G.S., S.G. Kim, and Y.W. Choi. 2015a. A comparative study of image classification method to detect water body based on UAS. J. the Korean Assoc. Geogr. inf. Stud. 18(3):113-127 (in Korean). https://doi.org/10.11108/kagis.2015.18.3.113
  20. Lee, K.D., S.I. Na, S.C. Baek, K.D. Park, J.S. Choi, S.J. Kim, H.J. Kim, H.S. Choi, and S.Y. Hong. 2015b. Estimating the amount of nitrogen in hairy vetch on paddy fields using unmanned aerial vehicle imagery. Korean J. Soil Sci. Fert. 48(5):384-390 (in Korean). https://doi.org/10.7745/KJSSF.2015.48.5.384
  21. Lee, K.D., Y.E. Lee, C.W. Park, S.Y. Hong, and S.I. Na. 2016b. Study on reflectance and NDVI of aerial images using a fixed-wing UAV "Ebee". Korean J. Soil Sci. Fert. 49(6):731-742 (in Korean). https://doi.org/10.7745/KJSSF.2016.49.6.731
  22. Louhaichi, M., M.M. Borman, and D.E. Johnson, 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto Int. 16:65-70.
  23. Lyon, J.G., D. Yuan, R.S. Lunetta, and C.D. Elvidge. 1998. A change detection experiment using vegetation indices. Photogramm. Eng. Remote Sens. 64(2):143-150.
  24. Na, S.I., S.Y. Hong, Y.H. Kim, K.D. Lee, and S.Y. Jang, 2013a. Estimating leaf area index of paddy rice from RapidEye imagery to assess evapotranspiration in Korean paddy fields. Korean J. Soil Sci. Fert. 46(4):245-252 (in Korean). https://doi.org/10.7745/KJSSF.2013.46.4.245
  25. Na, S.I., S.Y. Hong, Y.H. Kim, K.D. Lee, and S.Y. Jang. 2013b. Prediction of rice yield in Korea using paddy rice NPP index - Application of Modis data and CASA model. Korea. J. Remote Sensing 29(5):461-476 (in Korean). https://doi.org/10.7780/kjrs.2013.29.5.2
  26. Na, S.I., S.Y. Hong, C.W. Park, K.D. Kim, and K.D. Lee. 2016a. Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors, Korean J. Soil Sci. Fert. 49(5):420-428 (in Korean). https://doi.org/10.7745/KJSSF.2016.49.5.420
  27. Na, S.I., S.Y. Hong, C.W. Park, K.D. Kim, and K.D. Lee. 2016b. Mapping the spatial distribution of IRG growth based on UAV. Korean J. Soil Sci. Fert. 49(5):495-502 (in Korean). https://doi.org/10.7745/KJSSF.2016.49.5.495
  28. Na, S.I., C.W. Park, Y.K, Cheong, C.S. Kang, I.B. Choi, and K.D. Lee. 2016c. Selection of Optimal Vegetation Indices for Estimation of Barley & Wheat Growth based on Remote Sensing - An Application of Unmanned Aerial Vehicle and Field Investigation Data -. Korea. J. Remote Sensing 32(5):483-497 (in Korean). https://doi.org/10.7780/kjrs.2016.32.5.7
  29. Park, J.K., H.J. Lee, and J.W. Hwang. 2005. An analysis of adoption possibility for precision agriculture in Korean rice farms. Korean J. Econ. 46(4):1-23
  30. Pearson, R.L. and L.D. Miller. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In: Proceedings of the Eighth International Symposium on Remote Sensing of Environment. Environmental Research Institute of Michigan, Ann Arbor, MI, 1357-1381.
  31. Rasmussen, J., N. Georgios, J. Nielsen, J. Svensgaard, R.N. Poulsen, and S. Chritensen. 2016. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Eur. J. Agron. 74:75-92. https://doi.org/10.1016/j.eja.2015.11.026
  32. Richardson, A.J. and J.H. Everitt. 1992. Using spectral vegetation indices to estimate rangeland productivity. Geocarto Int. 1:63-77.
  33. Rouse, J.W., R.H. Haas, J.A. Schell, and D.W. Deering, 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Freden, S.C., Mercanti, E.P., Becker, M.(Eds.), Third Earth Resources Technology Satellite-1 Symposium, Technical Presentations, NASA SP-351. National Aeronautics and Space Administration, Washington, DC, 309-317.
  34. Torres-Sanchez, J., J.M. Pena, A.I. de Castro, and F. Lopez-Granados. 2014. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric. 103:104-113. https://doi.org/10.1016/j.compag.2014.02.009
  35. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  36. Vincini, M., E. Frazzi, and P. D'Alessio. 2008. A broad-band leaf chlorophyll index at the canopy scale. Prec. Agric. 9:303-319. https://doi.org/10.1007/s11119-008-9075-z
  37. Xiang, H. and L. Tian. 2011. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosyst. Eng. 108(2):174-190. https://doi.org/10.1016/j.biosystemseng.2010.11.010