Study on Vegetation Classification Based on Spectral Knowledge Base

  • Peng Liu
  • Jingcheng ZhangEmail author
  • Bin Wang
  • Xuexue Zhang
  • Kaihua Wu
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)


A framework about spectral based vegetation classification was proposed, which serves as a core methodology of the vegetation spectral knowledge base. The hyperspectral reflectances of 13 types of plants were measured by an ASD FieldSpec 4 spectroradiometer. Two forms of spectral features were used for representing the key spectral characteristics of plants, including Vegetation index (VI) and spectral shape features. Based on these spectral features, a sensitivity analysis was performed to identify the most important features for establishing the classifier. The analysis of variance (ANOVA) and the cross-correlation analysis were applied to derive the sensitivity of features and remove features that have high correlations. Then, a classification method for differentiating plants was established by coupling some spectral similarity measures (e.g., ED) with some classification methods (e.g., BPANN and SVM). The results of discrimination analysis showed that a highest accuracy was produced by SVM with the OAA over 99% when using 7 sensitive VIs. The results suggested the framework about spectral based vegetation classification can form a basis for spectral knowledge base and application technology and further achieve a wide range of plant classification based on remote sensing.


Vegetation classification Hyperspectral Feature extraction Classification algorithm 



This work was supported by Zhejiang public welfare programme of agriculture technology (2016C32087), National Natural Science Foundation of China (41671415; 41601461) and Graduate Scientific Research Foundation of Hangzhou Dianzi University (CXJJ2017068).


  1. Schmidt, K.S., Skidmore, A.K.: Spectral discrimination of vegetation types in a coastal wetland. Remote Sens. Environ. 85, 92–108 (2003)CrossRefGoogle Scholar
  2. Pu, R.: Broadleaf species recognition with in situ hyperspectral data. Int. J. Remote. Sensing. 30(11), 2759–2779 (2009)CrossRefGoogle Scholar
  3. Allard, D., D‘Or, D., Froidevaux, R.: An efficient maximum entropy approach for categorical variable prediction. Eur. J. Soil Sci. 62, 381–393 (2011)CrossRefGoogle Scholar
  4. Peñuelas, J., Baret, F., Filella, I.: Semi-imperical indices to assess carotenoids/chlorophyll, a ratio from leaf spectral reflectance. Photosynthetica 31, 221–230 (1995a)Google Scholar
  5. Abdel-Rahman, E.M., Ahmed, F.B., Van den Berg, M.: Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. Int. J. Appl. Earth Obs. Geoinf. 12, S52–S57 (2010)CrossRefGoogle Scholar
  6. Gong, P., Pu, R., Yu, B.: Conifer species recognition: An exploratory analysis of in situ hyperspectral data. Remote Sens. Environ. 62, 189–200 (1997)CrossRefGoogle Scholar
  7. Prospere, K., Mclaren, K., Wilson, B.: Plant species discrimination in a tropical wetland using in situ hyperspectral data. J. Remote Sens. 6(9), 8494–8523 (2014)CrossRefGoogle Scholar
  8. Pu, R.: Mapping urban forest tree species using IKONOS imagery: preliminary results. J. Environ. Monit. Assess. 172(1–4), 199–214 (2011)CrossRefGoogle Scholar
  9. Bue, B.D., Thompson, D.R., Sellar, R.G., Podest, E.V., Eastwood, M.L., Helmlinger, M.C., et al.: Leveraging in-scene spectra for vegetation species discrimination with mesma-mda. ISPRS J. Photogramm. Remote Sens. 108, 33–48 (2015)CrossRefGoogle Scholar
  10. Pu, R., Landry, S.: A comparative analysis of high spatial resolution ikonos and worldview-2 imagery for mapping urban tree species. Remote Sens. Environ. 124(9), 516–533 (2012)CrossRefGoogle Scholar
  11. Zeng, S., Kuang, R., Xiao, Y., Zhao, Z.: Measured hyperspectral data classification of poyang lake wetland vegetation. Remote Sens. Inf. 32(5), 75–81 (2017)Google Scholar
  12. Yu, J., Li, X., Zhang, Q., Shi, H., Xue, J., Chu, J.: Typical vegetation classification of Taihu lakeside based on measured hyperspectral data. Jiangsu Agric. Sci. 45(5), 240–244 (2017)Google Scholar
  13. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002)CrossRefGoogle Scholar
  14. Delalieux, S., Somers, B., Hereijgers, S., Verstraeten, W.W., Keulemans, W., Coppin, P.: A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards. Remote Sens. Environ. 112, 3762–3772 (2008)CrossRefGoogle Scholar
  15. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W.: Monitoring vegetation systems in the great plains with ERTS. In: Proceedings Third ERTS Symposium, vol. 1, 48–62 (1973)Google Scholar
  16. Koppe, W., Li, F., Gnyp, M.L., Miao, Y., Jia, L., Chen, X., et al.: Evaluating multispectral and hyperspectral satellite remote sensing data for estimating winter wheat growth parameters at regional scale in the north china plain. Photogramm.-Fernerkund. – Geoinf. 3, 167–178 (2010)CrossRefGoogle Scholar
  17. Berg, A.K.V.D., Perkins, T.D.: Non-destructive estimation of anthocyanin content in autumn sugar maple leaves. Hortic. Sci. 40(3), 685–686 (2005)Google Scholar
  18. Barnes, J.D., Balaguer, L., Manrique, E., Elvira, S., Davison, A.W.: A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environ. Exp. Bot. 32, 85–100 (1992)CrossRefGoogle Scholar
  19. Peñuelas, J., Filella, I., Lloret, P., Muñoz, F., Vilajeliu, M.: Reflectance assessment of mite effects on apple trees. Int. J. Remote Sens. 16, 2727–2733 (1995b)CrossRefGoogle Scholar
  20. Gamon, J.A., Penuelas, J., Field, C.B.: A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41, 35–44 (1992)CrossRefGoogle Scholar
  21. Galvão, L.S., Formaggio, A.R., Tisot, D.A.: Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 hyperion data. Remote Sens. Environ. 94, 523–534 (2005)CrossRefGoogle Scholar
  22. Schlerf, M., Atzberger, C., Hill, J.: Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens. Environ. 95, 177–194 (2005)CrossRefGoogle Scholar
  23. Fensholt, R., Sandholt, I.: Derivation of a shortwave infrared water stress index from MODIS near-and shortwave infrared data in a semiarid environment. Remote Sens. Environ. 87(1), 111–121 (2003)CrossRefGoogle Scholar
  24. Peñuelas, J., Piñol, J., Ogaya, R., Filella, I.: Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int. J. Remote Sens. 18, 2869–2875 (1997)CrossRefGoogle Scholar
  25. Merton, R., Huntington, J.: Early simulation of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. In: Summaries of the Eight JPL Airborne Earth Science Workshop, 9–11 February, pp. 299–307. JPL Publication 99-17, Pasadena (1999)Google Scholar
  26. Demetriades-Shah, T.H., Steven, M.D., Clark, J.A.: High-resolution derivative spectra in remote-sensing. Remote Sens. Environ. 33(1), 55–64 (1990)CrossRefGoogle Scholar
  27. Tsai, F., Philpot, W.: Derivative analysis of hyperspectral data. Remote Sens. Environ. 66(1), 41–51 (1998)CrossRefGoogle Scholar
  28. Kong, X., Shu, N., Huang, W., Fu, J.: The research on effectiveness of spectral similarity measures for hyperspectral image. In: IEEE 2010 3rd International Congress on Image and Signal Processing (CISP2010), pp. 2269–2273 (2010)Google Scholar
  29. Congalton, R.G., Mead, R.A.: A quantitative method to test for consistency and correctness in photointerpretation. Photogramm. Eng. Remote. Sens. 49(1), 69–74 (1983)Google Scholar
  30. Story, M., Congalton, R.G.: Accuracy assessment: A user’s perspective. Photogramm. Eng. Remote. Sens. 52(3), 397–399 (1986)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Peng Liu
    • 1
  • Jingcheng Zhang
    • 1
    Email author
  • Bin Wang
    • 1
  • Xuexue Zhang
    • 1
  • Kaihua Wu
    • 1
  1. 1.College of Life Information Science and Instrument EngineeringHangzhou Dianzi UniversityHangzhouChina

Personalised recommendations