Browse > Article
http://dx.doi.org/10.7745/KJSSF.2017.50.5.409

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)
Publication Information
Korean Journal of Soil Science and Fertilizer / v.50, no.5, 2017 , pp. 409-421 More about this Journal
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.
Keywords
UAV; Vegetation indices; Rice growth monitoring;
Citations & Related Records
Times Cited By KSCI : 11  (Citation Analysis)
연도 인용수 순위
1 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).   DOI
2 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).   DOI
3 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).   DOI
4 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).   DOI
5 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).   DOI
6 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
7 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.
8 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.   DOI
9 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.   DOI
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: 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.
11 Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127-150.   DOI
12 Vincini, M., E. Frazzi, and P. D'Alessio. 2008. A broad-band leaf chlorophyll index at the canopy scale. Prec. Agric. 9:303-319.   DOI
13 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.   DOI
14 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.   DOI
15 Richardson, A.J. and J.H. Everitt. 1992. Using spectral vegetation indices to estimate rangeland productivity. Geocarto Int. 1:63-77.
16 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.   DOI
17 Chae, J.C., S.J. Park, B.H. Kang, and S.H. Kim. 2006. Crop cultivation. Hyangmunsa. Seoul. 434.
18 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.
19 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.   DOI
20 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.   DOI
21 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).
22 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).
23 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).
24 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).
25 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).   DOI
26 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.   DOI
27 Jordan, C.F. 1969. Derivation of leaf area index from quality of light on the forest floor, Ecology. 50:663-666.   DOI
28 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).
29 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).
30 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).
31 Korean Statistical Information Service Homepage. http://www.kosis.kr/Acessed 12 May 2017.
32 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).
33 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).   DOI
34 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).   DOI
35 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).   DOI
36 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.
37 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.