Image Re-processing of Satellite Imageries

  • Moses Eterigho EmetereEmail author
Part of the Studies in Big Data book series (SBD, volume 54)


In this chapter, the focus is re-processing satellite imageries. Satellite imagery are images of Earth or other planets collected by Imaging satellites. The quality of satellite imagery is judged by its different types of resolution. The different open source libraries and programming language for analysing big data of images was illustrated. An illustration on how to design a mini-project was carried out. The technicalities of various types of results were adequately explained.


  1. Acker, J. G., & Leptoukh, G. (2007). Online analysis enhances use of NASA earth science data. EOS, 88, 14–17.CrossRefGoogle Scholar
  2. Bond, T. C., & Bergstrom, R. W. (2006). Light absorption by carbonaceous particles: an investigative review. Aerosol Science and Technology, 40, 27–67.CrossRefGoogle Scholar
  3. Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., et al. (2013). Bounding the role of black carbon in the climate system: A scientific assessment. Journal of Geophysical Research, 118(11), 5380–5552.Google Scholar
  4. Campbell, J. B. (2002). Introduction to remote sensing. New York London: The Guilford Press.Google Scholar
  5. Emetere, M. E. (2016a). Statistical examination of the aerosols loading over Mubi-Nigeria: The satellite observation analysis. Geographica Panonica, 20(1), 42–50.CrossRefGoogle Scholar
  6. Emetere, M. E. (2016b). Numerical modelling of West Africa regional scale aerosol dispersion. Thesis submitted to Covenant University.Google Scholar
  7. Emetere, M. E. (2017). Investigations on aerosols transport over micro- and macro-scale settings of West Africa. Environ. Eng. Res., 22(1), 75–86.CrossRefGoogle Scholar
  8. Emetere, M. E., & Akinyemi, M. L. (2017). Documentation of atmospheric constants over Niamey, Niger: a theoretical aid for measuring instruments. Meteorological Applications, 24(2), 260–267.CrossRefGoogle Scholar
  9. Emetere, M. E., Akinyemi, M. L., & Akinojo, O. (2015a). Parametric retrieval model for estimating aerosol size distribution via the AERONET, LAGOS station. Environmental Pollution, 207(C), 381–390.Google Scholar
  10. Emetere, M. E., Akinyemi, M. L., & Akin-Ojo, O. (2015b). Aerosol optical depth pollution in selected areas trends over different regions of Nigeria: Thirteen years analysis. Modern Applied Science., 9(9), 267–279.CrossRefGoogle Scholar
  11. Emetere, M. E., Esisio, F., Oladapo, F. (2017b). Satellite observation analysis of aerosols loading effect over Monrovia-Liberia. Journal of Physics: Conference Series, 852(1), art. no. 012009. DOI: 10.1088/1742-6596/852/1/012009.Google Scholar
  12. Emetere, M. E., Sanni, S. E., Emetere, J. M., & Uno, U. E. (2017a). Thermal Infrared remote sensing of hydrocarbon in Lagos-Southern Nigeria: Application of the thermographic model. International Geomate Journal, 13(39), 33–45.CrossRefGoogle Scholar
  13. Emetere, M. E., Sanni, S. E., & Tunji-Olayeni, P. (2017b). Atmospheric configurations of aerosols loading and retention over Bolgatanga-Ghana. Journal of Physics: Conference Series, 852(1), art. no. 012007. DOI: 10.1088/1742-6596/852/1/012007.Google Scholar
  14. GeoEye. (2018). GeoEye-1 satellite sensor (0.46 m). Accessed January 12, 2018.
  15. Koike, M., Moteki, N., Khatri, P., Takamura, T., Takegawa, N., Kondo, Y., et al. (2014). Case study of absorption aerosol optical depth closure of black carbon over the East China Sea. Journal of Geophysical Research, 119(1), 122–136.Google Scholar
  16. Lacis, A. A., & Mishchenko, M. I. (1995). Climate forcing, climate sensitivity, and climate response: A radiative modeling perspective on atmospheric aerosols. In R. Charlson & J. Heintzenberg (Eds.), Aerosol forcing of climate (pp. 11–42). New York: John Wiley.Google Scholar
  17. Landinfo. (2018). Satellite imagery resolution comparison. Accessed January 12, 2018.
  18. Lindén, J., Thorsson, S., Boman, R., Holmer, B. (2012). Urban climate and air pollution in Ouagadougou, Burkina Faso: An overview of results from five field studies (pp. 1–88). University of Gothenburg.
  19. NASA. (2015). NASA satellite camera provides “EPIC” view of earth. Accessed January 4, 2018.
  20. Odient. (2018). Odeint solving ODE’s in C++. Accessed February 17, 2018.
  21. Omotosho, T. V., Emetere, M. E., & Arase, O. S. (2015). Mathematical projections of air pollutants effects over Niger Delta region using remotely sensed satellite data. International Journal of Applied Environmental Sciences, 10(2), 651–664.Google Scholar
  22. Rafferty, J.P. (2010). Storms, violent winds, and earth’s atmosphere. The Rosen Publishing Group (p. 95). SBN-13: 978-1615301140, ISBN-10: 1615301143.Google Scholar
  23. Senghor, H., Machu, É., Hourdin, F., Gaye, A. T. (2017). Seasonal cycle of desert aerosols in western Africa: analysis of the coastal transition with passive and active sensors. Atmospheric Chemistry and Physics, 17, 8395–8410.Google Scholar
  24. Tegen, I., Peter, H., Mian, C., Fung, I., Jacob, D, Penn, J. (1997). Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimates from model results. Journal of Geophysical Research, 102(D20), 23895–23915.Google Scholar
  25. Tirabassi, T., Moreira, D. M., Vilhena, M. T., da Costa, C. P. (2010). Comparison between Non-Gaussian puff model and a model based on a time-dependent solution of advection-diffusion equation. Journal of Environmental Protection, 1, 172–178.Google Scholar
  26. Walcek, C.J. (2004). A Gaussian dispersion/plume model explicitly accounting for wind shear. Accessed January 9, 2018.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of PhysicsCovenant UniversityOtaNigeria
  2. 2.Department of Mechanical Engineering ScienceUniversity of JohannesburgJohannesburgSouth Africa

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