Spatial Information Research

, Volume 26, Issue 4, pp 397–404 | Cite as

Automated extraction of various vegetative and water body indices from multisensor satellite data: a MATLAB approach

  • Girish Gopinath
  • S. Nimmi


The increasing availability of remote sensing imageries with different spatial, spectral, temporal and radiometric characteristics expands the horizon of our choices in imageries and vegetation mapping. Among the various techniques in vegetation mapping the use of vegetative indices confiscates the simplest and easiest one to understand and calculate. This work proposes automatic extraction of different vegetation and water indices from ResourceSat-1(IRS-P6) images. The concept paves to calculate indices for any ResourceSat image prearranged to the program which is written in MATLAB 2013a. The effortlessly accessible images from its onboard sensors as Medium Resolution Linear Imaging Self-Scanner (LISS-III) and Advanced Wide Field Sensor (AWiFS) were considered to calculate six major indices like Normalized Difference Vegetation Index, Infrared Percentage Vegetation Index, Ratio Vegetation Index, Green Normalized Difference Vegetation Index, Green Red Vegetation Index, and Normalized Difference Water Index automatically. The program was written for any ResourceSat (LISS-III and AWiFS) data so that further analysis as well as calculation of more indices can be done by any individual within MATLAB. The user-friendly and time preserving feature makes MATLAB more powerful that any common user can deal with the analysis without the presence of any GIS experts that other softwares do.


Automated extraction Vegetative indices Waterbody indices MATLAB 


  1. 1.
    Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Hindawi Journal of Sensors, 2017, 1–17.CrossRefGoogle Scholar
  2. 2.
    Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine, 11, 185–200.CrossRefGoogle Scholar
  3. 3.
    Vina, A., Henebry, G. M., & Gitelson, A. A. (2004). Satellite monitoring of vegetation dynamics: Sensitivity enhancement by the wide dynamic range vegetation index. Geophysical Reseach Letters, 31, L04503.Google Scholar
  4. 4.
    Morel, J., Todoroff, P., Bégué, A., Bury, A., Martiné, J.-F., & Petit, M. (2014). Toward a satellite-based system of sugarcane yield estimation and forecasting in smallholder farming conditions: A case study on reunion island. Remote Sensing, 6, 6620–6635. Scholar
  5. 5.
    Hunt Jr, E. R., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S. T., Perry, E. M., & Akhmedov, B. (2013). A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Publications from USDA-ARS/UNL Faculty. Paper 1156.Google Scholar
  6. 6.
    Plaza, A., Benediktsson, J. A., Boardman, J., Brazile, J., Bruzzone, L., Camps-Valls, G., et al. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113, 110–122.CrossRefGoogle Scholar
  7. 7.
    Xiao, A. J., & Moody, A. (2005). A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98, 237–250.CrossRefGoogle Scholar
  8. 8.
    Lee, Craig A., Gasster, Samuel D., Plaza, Antonio, Chang, Chein-I, & Huang, Bormin. (2011). Recent developments in high performance computing for remote sensing: a review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(3), 508–527.CrossRefGoogle Scholar
  9. 9.
    Crippen, R. E. (1990). Calculating the vegetation index faster. Remote Sensing of Environment, 34, 71–73.CrossRefGoogle Scholar
  10. 10.
    Motohka, S., Nasahara, K. N., Oguma, H., & Tsuchida, S. (2010). Applicability of green–red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2, 2369–2387.CrossRefGoogle Scholar
  11. 11.
    Robson, A., Abbott, C., Lamb, D., & Bramley, R. (2012). Developing sugar cane yield prediction algorithms from satellite imagery. In Australian Society of sugar cane technologists, pp. 1–11.Google Scholar
  12. 12.
    Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.CrossRefGoogle Scholar
  13. 13.
    McFeeters, S. K. (1996). The use of normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432.CrossRefGoogle Scholar
  14. 14.
    Barati, S., Rayegani, B., Saati, M., Sharifi, A., & Nasri, M. (2011). Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas Egypt. J. Remote Sens. Space Sci., 14, 49–56.Google Scholar
  15. 15.
    Vina, A., Gitelson, A. A., Nguy-robertson, A. L., & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115, 3468–3478. Scholar
  16. 16.
    Rouse, J. W., Haas, R. H., Schell, J. A., & Deering D. W. (1974). Monitoring vegetation sys-tems in the Great Plains with ERTS. In: S. C. Freden, F. P. Mercanti, & M. Becker (Eds.) Third earth resources technology satellite-1 symposium, Vol. 1: Technical presentations, NASA SP-351. National Aeronautics and Space Administration,Washington, DC, pp. 309–317.Google Scholar
  17. 17.
    Dymond, J. R., & Shepherd, J. D. (1999). Correction of the topographic effect in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 37(2), 2618–2620.CrossRefGoogle Scholar
  18. 18.
    Matsushita, B., Yang, W., Chen, J., Ondaand, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (evi) and normalized difference vegetation index (ndvi) to topographiceffects: A case study in high-density cypress forest. Sensors, 2007(7), 2636–2651.CrossRefGoogle Scholar
  19. 19.
    Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298.CrossRefGoogle Scholar
  20. 20.
    Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14(4), 711–722.CrossRefGoogle Scholar
  21. 21.
    Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, D. C., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Reseach Letters, 32, L08403.Google Scholar
  22. 22.
    Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416–426.CrossRefGoogle Scholar
  23. 23.
    Huete, A. R. (1988). A soil adjusted vegetation index SAVI. Remote Sensing of Environment, 25, 295–309.CrossRefGoogle Scholar
  24. 24.
    Parveen, R., Kulkarni, S., & Mytri, V. D. (2017). Study of IRS 1C-LISS III Image and Identification of land cover features based on Spectral Responses. Hyderabad: Geospatial World Forum.Google Scholar

Copyright information

© Korean Spatial Information Society 2018

Authors and Affiliations

  1. 1.Geomatics DivisionCWRDM, Government of KeralaCalicutIndia

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