Science in China Series D: Earth Sciences

, Volume 48, Issue 2, pp 241–249

Principle and application of three-band gradient difference vegetation index

  • Tang Shihao 
  • Zhu Qijiang 
  • Wang Jindi 
  • Zhou Yuyu 
  • Zhao Feng 
Article

Abstract

Vegetation index is a simple, effective and experiential measurement of terrestrial vegetation activity, and plays a very important role in qualitative and quantitative remote sensing. Aiming at shortages of current vegetation indices, and starting from the analysis of vegetation spectral characteristics, we put forward a new vegetation index, the three-band gradient difference vegetation index (TGDVI), and established algorithms to inverse crown cover fraction and leaf area index (LAI) from it. Theoretical analysis and model simulation show that TGDVI has high saturation point and the ability to remove the influence of background to some degree, and the explicit functional relation with crown cover fraction and LAI can be established. Moreover, study shows that TGDVI also has the ability to partly remove the influence of thin cloud. Experiment in the Shunyi District, Beijing, China shows that reasonable result can be reached using the vegetation index to retrieve LAI. We also theoretically analyzed the reason why the normalized difference vegetation index (NDVI) owns the low saturation point, and show that it is determined by the definition of NDVI and the characteristic of vegetation spectra, and is unavoidable to some degree. Meanwhile, through model simulation, we also indicate that the relationship between simple ratio vegetation index (SR) and LAI closes to a piecewise linear one instead of a linear one, which is mainly caused by the influence of background and different change rates of reflectance in red and infrared bands with LAI increasing.

Keywords

vegetation index remote sensing LAI crown cover fraction remote sensing inversion 

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References

  1. 1.
    Liu Yujie, Yang Hongdong, Principle and Algorithm of Remote Sensing Information Processsing for MODIS (in Chinese), Beijing: Science Pres, 2001, 232–233Google Scholar
  2. 2.
    Nilson, T., A theoretical analysis of the frequency of gaps in plant stands, Agric. Meteorol, 1971, 8: 25–38.CrossRefGoogle Scholar
  3. 3.
    Roselyne, L., Chen, J. M., Roujean, J. et al., Retrieval of vegetation clumping index using hot spot signatures measured by polder instrument, Remote Sens. Environ., 2002, 79: 84–95.CrossRefGoogle Scholar
  4. 4.
    Chen, J. M., Rich, P. M., Gower, S. T. et al., Leaf area index of boreal forests: Theory, techniques, and measurements, Journal of Geophysical Research, 1997, 102(D24): 29429–29443.CrossRefGoogle Scholar
  5. 5.
    Campbell, G. S., Derivation of an angle density function for canopies with ellipsoidal leaf angle distribution, Agric. Forest Meteorol., 1990, 49: 173–176.CrossRefGoogle Scholar
  6. 6.
    Qin Wenhan, Xiang Yueqin, An analytical model for bidirectional reflectance factor of multicomponent vegetation canopies, Science in China, Ser. C, 1997, 40(3): 305–315.CrossRefGoogle Scholar
  7. 7.
    Verhoef, W., Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model, Remote Sens. Environ., 1984, 16: 125–141.CrossRefGoogle Scholar
  8. 8.
    Goel, N. S., Strebel, D. E., Simple beta distribution representation of leaf orientation in vegetation canopies, Agron. J., 1984, 76: 800–803.Google Scholar
  9. 9.
    Zhang Renhua, Sun Xiaomin, Zhu Zhilin et al., A remote sensing model of CO2 flux for wheat and studying of regional distribution, Science in China, Ser. D, 1999, 42(3): 325–336.Google Scholar
  10. 10.
    Zhang Renhua, Experimental Remote Sensing Modeling and Surface Foundations (in Chinese), Beijing: Sciences Press, 1996, 104–113.Google Scholar
  11. 11.
    Chen, J. M., Evaluation of vegetation indices and a modified simple ratio for boreal application, Canadian Journal of Remote Sensing, 1996, 22(3): 229–242.Google Scholar
  12. 12.
    Green, A. A., Berman, M., Switzer, P. et al., A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(1): 65–74.CrossRefGoogle Scholar

Copyright information

© Science in China Press 2005

Authors and Affiliations

  • Tang Shihao 
    • 1
    • 2
  • Zhu Qijiang 
    • 1
  • Wang Jindi 
    • 1
  • Zhou Yuyu 
    • 1
  • Zhao Feng 
    • 1
  1. 1.Research Center for Remote Sensing and GIS, Department of GeographyBeijing Normal UniversityBeijingChina
  2. 2.National Satellite Meteorological CenterChina Meteorological AdministrationBeijingChina

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