Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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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 -

원격탐사 기반 맥류 작황 추정을 위한 최적 식생지수 선정 - UAV와 현장 측정자료를 활용하여 -

  • Na, Sang-il (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Park, Chan-won (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Cheong, Young-kuen (National Institute of Crop Science, Rural Development Administration) ;
  • Kang, Chon-sik (National Institute of Crop Science, Rural Development Administration) ;
  • Choi, In-bae (National Institute of Crop Science, Rural Development Administration) ;
  • Lee, Kyung-do (National Institute of Agricultural Sciences, Rural Development Administration)
  • 나상일 (농촌진흥청 국립농업과학원) ;
  • 박찬원 (농촌진흥청 국립농업과학원) ;
  • 정영근 (농촌진흥청 국립식량과학원) ;
  • 강천식 (농촌진흥청 국립식량과학원) ;
  • 최인배 (농촌진흥청 국립식량과학원) ;
  • 이경도 (농촌진흥청 국립농업과학원)
  • Received : 2016.09.20
  • Accepted : 2016.10.28
  • Published : 2016.10.31
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Abstract

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of barley and wheat growth prediction equation by using UAV derived vegetation index. UAV imagery was taken on the test plots six times from late February to late June during the barley and wheat growing season. The field spectral reflectance during growing period for the 5 variety (Keunal-bori, Huinchalssal-bori, Saechalssal-bori, Keumkang and Jopum) were measured using ground spectroradiometer and three growth parameters, including plant height, shoot dry weight and number of tiller were investigated for each ground survey. Among the 6 Vegetation Indices (VI), the RVI, NDVI, NGRDI and GLI between measured and image derived showed high relationship with the coefficient of determination respectively. Using the field investigation data, the vegetation indices regression curves were derived, and the growth parameters were tried to compare with the VIs value.

무인항공기 영상은 작물의 생육단계에 따라 고해상도로 신속한 수집이 가능하기 때문에 정밀농업 관리를 위한 공간 변이 분석에 활용되고 있다. 본 연구의 목적은 무인항공기를 이용한 최적 식생지수를 선정하여 맥류 작황 추정식을 유도하는 것이다. 무인항공기 영상은 맥류 생육 기간인 2월 하순부터 6월 하순까지 6회에 걸쳐 촬영하였으며, 같은 기간 동안 5개 품종(큰알보리, 흰찰쌀보리, 새찰쌀보리, 금강밀, 조품밀)을 대상으로 휴대용 분광복사계를 이용하여 현장분광반사율을 측정하고 초장, 지상부건물중, 단위면적당 경수 등 생육인자를 조사하였다. 그 결과, 6개의 식생지수 중 RVI, NDVI, NGRDI 및 GLI가 생육인자와 높은 상관관계를 나타내었다. 또한 현장 생육조사 자료를 사용하여 식생지수와 생육인자의 비교를 시도하였다.

Keywords

References

  1. Agriculture, Forestry and Food Livestock key statistics, 2015. Ministry of Agriculture, Food and Rural Affairs.
  2. Becker-Reshef, I., E. Vermote, M. Lineman, and C. Justice, 2010. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data, Remote Sensing of Environment, 114: 1312-1323. https://doi.org/10.1016/j.rse.2010.01.010
  3. Broge, N.H. and E. Leblanc, 2000. Comparing predictive power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76: 156-172. https://doi.org/10.1016/S0034-4257(00)00197-8
  4. Candiago, S., F. Remondino, M.D. Giglio, M. Dubbini, and M. Gattelli, 2015. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images, Remote Sensing, 7: 4026-4047. https://doi.org/10.3390/rs70404026
  5. Curran P.J., 1983. Estimating Green LAI from Multispectral Aerial Photography, Photogrammetric Engineering and Remote Sensing, 49: 1709-1720.
  6. Dieter H., Z. Werner, S. Gunter, and S. Peter, 2005. Monitoring of gas pipelines - a civil UAV application, Aircraft Engineering and Aerospace Technology, 77: 352-360. https://doi.org/10.1108/00022660510617077
  7. Feng, Q., J. Liu, and J. Gong, 2015. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis, Remote Sensing, 7: 1074-1094. https://doi.org/10.3390/rs70101074
  8. Fuyi, T., B.B. Chun, M.Z. Mat Jafri, M.Z., L.H. San, K. Abdullah, and M. Tahrin, 2012. Land cover/use mapping using multi-band imageries captured by Cropcam Unmanned Aerial Vehicle Autopilot(UAV) over Penang Island, Malaysia, Proc. of SPIE, 8540: 1-6.
  9. Hassan, F.M., Lim, H.S., and Mat Jafri, M.Z., 2011. CropCam UAV for Land Use/Land Cover mapping over Penang Island, Malaysia, Pertanika Journal of Science & Technology, 19: 69-76.
  10. Herwitz, S.R., John, L.F., Dunagan, S.E., Higgins, R.G., Sullivan, D.V., Zheng, J., Lobitz, B.M., Leung, J.G., Gallmeyer, B.A., Aoyagi, M., Slye, R.E. and Brass, J.A., 2004. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support, Computers and Electronics in Agriculture, 44: 49-61. https://doi.org/10.1016/j.compag.2004.02.006
  11. Hong, S.Y., K.A. Sudduth, N.R. Kitchen, C.W. Fraisse, H.L. Palm, and W.J. Wiebold, 2004. Comparison of remote sensing and crop growth models for estimating within-field LAI variability, Korean Journal of Remote Sensing, 20(3): 175-188. https://doi.org/10.7780/kjrs.2004.20.3.175
  12. 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, Korean Jounal of Remote Sensing, 28(5): 509-520 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2012.28.5.4
  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. Jung, K.S., Y.S. Kim, and S.R. Oh, 2015. Technical Development of Flood Damage Estimation using UAV, Magazine of Korea water resources association, 48(1): 51-59 (in Korean with English abstract).
  15. Lee, K.D., S.I. Na, S.C. Baek, K.D. Park, J.S. Choi, S.J. Kim, H.J. Kim, H.S. Yun, and S.Y. Hong, 2015, Estimating the amount of nitrogen in hairy vetch on paddy fields using unmaned aerial vehicle imagery, Journal of Soil Science and Fertilizer, 48(5): 384-390 (in Korean with English abstract).
  16. Lee, J.W., G.A. Park, H.K. Joh, K.H. Lee, S.I. Na, J.H. Park, and S.J. Kim, 2011. Analysis of relationship between vegetation indices and crop yield using KOMPSAT(KoreaMulti-Purpose SATellite)-2 imagery and field investigation data, Journal of The Korean Society of Agricultural Engineers. 53(3): 75-82 (in Korean with English abstract). https://doi.org/10.5389/KSAE.2011.53.3.075
  17. Louhaichi, M., M.M. Borman, and D.E. Johnson, 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16: 65-70. https://doi.org/10.1080/10106040108542184
  18. Na, S.I., S.C. Baek, S.Y. Hong, K.D. Lee, and K.C. Jang, 2015. A Study on the Application of UAV for the Onion and Garlic Growth Monitoring, Proc. of The Korean Society of Soil Science and Fertilizer, Wanju, May 21-22, pp. 225.
  19. Na, S.I., S.Y. Hong, Y.H. Kim, K.D. Lee, and S.Y. Jang, 2013. Prediction of rice yield in Korea using paddy rice NPP index: Application of MODIS data and CASA model, Korean Journal of Remote Sensing, 29(5): 461-476 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2013.29.5.2
  20. Na, S.I., S.Y. Hong, Y.H. Kim, and K.D. Lee, 2014. Estimation of corn and soybean yields based on MODIS data and CASA model in Iowa and Illinois, USA, Journal of Soil Science and Fertilizer, 47(2): 92-99 (in Korean with English abstract).
  21. Pearson, R.L. and L.D. Miller, 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Proc. of the Eighth International Symposium on Remote Sensing of Environment, II, Michigan, Ann Arbor, pp. 1357-1381.
  22. Ren J.Q., Z.X. Chen, Q.B. Zhou, and H.J. Tang, 2008. Regional yield estimation for winter wheat with MODIS NDVI data in Shandong, China, International Journal of Applied Earth Observation and Geoinformation, 10: 403-413. https://doi.org/10.1016/j.jag.2007.11.003
  23. 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.
  24. Tomas, J.R. and H.W. Gausman, 1977. Leat reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agronomy Jornal, 69: 799-802. https://doi.org/10.2134/agronj1977.00021962006900050017x
  25. Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  26. Vincini, M., E. Frazzi, and P. D'Alessio, 2008. A broad-band leaf chlorophyll index at the canopy scale. Precision Agriculture, 9: 303-319. https://doi.org/10.1007/s11119-008-9075-z

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