Comparison of Spectral Indices and Principal Component Analysis for Differentiating Lodged Rice Crop from Normal Ones

  • Zhanyu Liu
  • Cunjun Li
  • Yitao Wang
  • Wenjiang Huang
  • Xiaodong Ding
  • Bin Zhou
  • Hongfeng Wu
  • Dacheng Wang
  • Jingjing Shi
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 369)

Abstract

Hyperspectral reflectance of normal and lodged rice caused by rice brown planthopper and rice panicle blast was measured at the canopy level. Over one decade broad- and narrow-band vegetation indices (VIs) were calculated to simulate Landsat ETM+ with in situ hyperspectral reflectance. Principal component analysis (PCA) was utilized to obtain the front two principal components (PCs). Probabilistic neural network (PNN) was employed to classify the lodged and normal rice with VIs and PCs as the input vectors. PCs had 100% of overall accuracy and 1 of Kappa coefficient for the training dataset. While PCs had the greatest average overall accuracy (97.8%) and Kappa coefficient (0.955) for the two testing datasets than VIs consisting of broad- and narrow-bands. The results indicated that hyperspectral remote sensing with PCA and artificial neural networks could potentially be applied to discriminate lodged crops from normal ones at regional and large spatial scales.

Keywords

Hyperspectral remote sensing Lodged rice Principal component analysis (PCA) Vegetation indices (VIs) Artificial neural networks (ANN) 

References

  1. 1.
    Everitt, J.H., Escobar, D.E., Summary, K.R., Davis, M.R.: Using airborne video, global positioning system, and geographical information system technologies for detecting and mapping citrus blackfly infestations. Southwest. Entomol. 19(2), 129–138 (1994)Google Scholar
  2. 2.
    McCartney, H.A., Fitt, B.D.L.: Dispersal of foliar fungal plant pathogens: mechanisms, gradients and spatial patterns. In: Gareth Jones, D. (ed.) Plant Disease Epidemiology, pp. 138–160. Kluwer Publishers, London (1998)Google Scholar
  3. 3.
    Pedigo, L.P.: Closing the gap between IPM theory and practice. J. Agri. Entomol. 12, 171–181 (1995)Google Scholar
  4. 4.
    Sōgawa, K.: The rice brown planthopper: feeding physiology and host plant interactions. Ann. Rev. Entomol. 27, 49–73 (1982)CrossRefGoogle Scholar
  5. 5.
    West, J.S., Bravo, C., Oberit, R., Lemaire, D., Moshou, D., McCartney, H.A.: The potential of optical canopy measurement for targeted control of field crop diseases. Ann. Rev. Phytopathol. 41, 593–661 (2003)CrossRefGoogle Scholar
  6. 6.
    Elvidge, C.D., Chen, Z.K.: Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remot. Sens. Environ. 54(1), 38–48 (1995)CrossRefGoogle Scholar
  7. 7.
    Holden, H., LeDrew, E.: Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal component analysis, and derivative spectroscopy. Remot. Sens. Environ. 65(2), 217–224 (1998)CrossRefGoogle Scholar
  8. 8.
    Karimi, Y., Prasher, S.O., Patel, R.M., Kim, S.H.: Application of support vector machine technology for weed and nitrogen stress detection in corn. Comput. Electron. Agr. 51(1-2), 99–109 (2006)CrossRefGoogle Scholar
  9. 9.
    Shi, J.J., Liu, Z.Y., Zhang, L.L., Zhou, W., Huang, J.F.: Hyperspectral recognition of rice damaged by rice leaf roller based on support vector machine. Ric. Sci. 23(3), 331–334 (2009)Google Scholar
  10. 10.
    Liu, Z.Y., Wang, D.C., Li, B., Huang, J.F.: Discrimination of lodged rice based on visible/near-infrared spectroscopy. J. Infra. Milli. Wav. 28(5), 321–324 (2009)CrossRefGoogle Scholar
  11. 11.
    Richardson, A.J., Everitt, J.H.: Using spectral vegetation indices to estimate rangeland productivity. Geocarto Int. 1, 63–77 (1992)CrossRefGoogle Scholar
  12. 12.
    Jordan, C.F.: Derivation of leaf area index from quality of light on the forest floor. Ecol. 50(4), 663–666 (1969)CrossRefGoogle Scholar
  13. 13.
    Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W.: Monitoring vegetation systems in the great plains with ERTS. In: 3rd ERTS Symposium, NASA, Washington, USA, pp. 48–62 (1973)Google Scholar
  14. 14.
    Huete, A.R.: A soil-adjusted vegetation index (SAVI). Remot. Sens. Environ. 25(3), 295–309 (1988)CrossRefGoogle Scholar
  15. 15.
    Rondeaux, G., Steven, M., Baret, F.: Optimization of soil-adjusted vegetation indices. Remot. Sens. Environ. 55(2), 95–107 (1996)CrossRefGoogle Scholar
  16. 16.
    Qi, J., Chehbouni, A., Huete, A., Kerr, Y., Sorooshian, S.: A modified soil-adjusted vegetation index (MSAVI). Remot. Sens. Environ. 48(2), 119–126 (1994)CrossRefGoogle Scholar
  17. 17.
    Broge, N.H., Leblanc, E.: Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remot. Sens. Environ. 76(2), 156–172 (2000)CrossRefGoogle Scholar
  18. 18.
    Crippen, R.E.: Calculating the Vegetation Index Faster. Remot. Sens. Environ. 34(1), 71–73 (1990)CrossRefGoogle Scholar
  19. 19.
    Bannari, A., Asalhi, H., Teillet, P.M.: Transformed difference vegetation index (TDVI) for vegetation cover mapping. In: Proceedings on CD-Rom, paper I2A35, International Geoscience and Remote Sensing Symposium, Toronto, Ontario, Canada, p. 1508 (2002)Google Scholar
  20. 20.
    Roujean, J.L., Boreon, F.M.: Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remot. Sens. Environ. 51(3), 375–384 (1995)CrossRefGoogle Scholar
  21. 21.
    Liu, H.Q., Huete, A.R.: A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transaction on Geosci. Remot. Sens. 33, 457–465 (1995)CrossRefGoogle Scholar
  22. 22.
    Pinty, B., Verstraete, M.M.: GEMI: A non-linear index to monitor global vegetation from satellites. Vegetatio. 101(1), 15–20 (1992)CrossRefGoogle Scholar
  23. 23.
    Crist, E.P., Laurin, R., Cicone, R.C.: Vegetation and soils information contained in transformed thematic mapper data. In: International Geoscience and Remote Sensing Symposium (IGARSS 1986), Zurich, Switzerland, pp. 1465–1470 (1986)Google Scholar
  24. 24.
    Gao, B.C.: NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space. Remot. Sens. Environ. 58(3), 257–266 (1996)CrossRefGoogle Scholar
  25. 25.
    Xiao, X., Boles, S., Frolking, S., Salas, W., Moore III, B., Li, C.: Observation of fl ooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. Int. J. Remot. Sens. 23(15), 3009–3022 (2002)CrossRefGoogle Scholar
  26. 26.
    Wang, L., Qu, J.: NMDI: A Normalized Multi-band Drought Index for Monitoring Soiland Vegetation Moisture with Satellite Remote Sensing. Geophy. Research Lett. 34, 204–208 (2007)Google Scholar
  27. 27.
    Kobayashi, T., Kanda, E., Kitada, K., Ishiguro, K., Torigoe, Y.: Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathol. 91(3), 316–323 (2001)CrossRefGoogle Scholar
  28. 28.
    Sivandm, S.N., Sumathi, S., Deepa, S.N.: Introduction to neural networks using Matlab 6.0. Tata Mcgraw-Hill Publishing Company Limited (2006)Google Scholar
  29. 29.
    Congalton, R.G.: A review of assessing the accuracy of classification of remotely sensed data. Remot. Sens. Environ. 37(1), 35–46 (1991)CrossRefGoogle Scholar
  30. 30.
    Asrar, G.: Theory and applications of optical remote sensing, pp. 119–125. John Wiley & Sons, Inc., New York (1989)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Zhanyu Liu
    • 1
    • 2
  • Cunjun Li
    • 3
  • Yitao Wang
    • 4
  • Wenjiang Huang
    • 3
  • Xiaodong Ding
    • 1
  • Bin Zhou
    • 1
  • Hongfeng Wu
    • 4
  • Dacheng Wang
    • 3
  • Jingjing Shi
    • 5
  1. 1.Institute of Remote Sensing and Earth SciencesHangzhou Normal UniversityHangzhouChina
  2. 2.Key Laboratory of Urban Wetland and Region ChangeHangzhouChina
  3. 3.Beijing Research Center for Information Technology in AgricultureBeijingChina
  4. 4.Institute of Scientific and Technological InformaticsHeilongjiang Academy of Land Reclamation SciencesHarbinChina
  5. 5.Institute of Agricultural Remote Sensing & Information TechnologyZhejiang UniversityHangzhouChina

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