Application of Enhanced-2D-CWT in Topographic Images for Mapping Landslide Risk Areas

  • Victor Vermehren Valenzuela
  • Rafael Dueire Lins
  • Hélio Magalhães de Oliveira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7950)


There has been lately a number of catastrophic events of landslides and mudslides in the mountainous region of Rio de Janeiro, Brazil. Those were caused by intense rain in localities where there was unplanned occupation of slopes of hills and mountains. Thus, it became imperative creating an inventory of landslide risk areas in densely populated cities. This work presents a way of demarcating risk areas by using the bidimensional Continuous Wavelet Transform (2D-CWT) applied to high resolution topographic images of the mountainous region of Rio de Janeiro.


Landslides LiDAR DEM 2D-CWT spectral power wavelet Fourier 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Filho, A.R., Cortez, A.T.C.: A problemática socioambiental da ocupação urbana em áreas de risco de deslizamento da Suíça Brasileira. Revista Brasileira de Geografia Física 03, 33–40 (2010)Google Scholar
  2. 2.
    Florenzano, T. G. (Org.).: Métodos e Modelos de Previsão de Movimentos de Massa. Oficina de Textos, São Paulo (2008)Google Scholar
  3. 3.
    Parise, M.: Landslide Mapping Techniques and Their Use in the Assessment of the Landslide Hazard. Phys. Chem. Earth (C) 26(9), 697–703 (2001)Google Scholar
  4. 4.
    Marcos, et al.: LiDAR: Princípios e aplicações florestais. Pesquisa Florestal Brasileira, Colombo 30(63), 231–244 (2010)CrossRefGoogle Scholar
  5. 5.
    Chamoli, A.: Wavelet Analysis of Geophysical Time Series. e-Journal Earth Science India 2(IV), 258–275 (2009)Google Scholar
  6. 6.
    Booth, A.M., Roering, J.J., Perron, J.T.: Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon. Geomorphology 109, 132–147 (2009)CrossRefGoogle Scholar
  7. 7.
    Lang, W.C., Forinash, K.: Time-frequency analysis with the continuous wavelet transform. Am. J. Phys. 66(9) (1998)Google Scholar
  8. 8.
    Kirby, L.F.: Which wavelet best reproduces the Fourier power spectrum? Computers & Geosciences 31, 846–864 (2005)CrossRefGoogle Scholar
  9. 9.
    TOPODATA/INPE – Brazilian Geomorphometrics Database, (last accessed on January 10, 2013)
  10. 10.
    Perron, J.T., Kirchner, J.W., Dietrich, W.E.: Spectral signatures of characteristic spatial scales and nonfractal structure in landscapes. Journal of Geophysical Research 113, F04003 (2008)Google Scholar
  11. 11.
    Burrough, P.A., McDonell, R.A.: Principles of Geographical Information Systems, p. 190. Oxford University Press, New York (1998)Google Scholar
  12. 12.
    Tanennbaum, B.: Image Processing ToolboxTM 4 User’s Guide. The MathWork Inc., MA (2012)Google Scholar
  13. 13.
    Addison, P.S.: The Illustrated Wavelet Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance. Institute of Physics Publishing, Bristol (2002)CrossRefGoogle Scholar
  14. 14.
    Strang, G.: Wavelet transforms versus Fourier transforms. Bull. Amer. (1993)Google Scholar
  15. 15.
    Torrence, C., Compo, G.P.: A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79(1), 61–78 (1998)CrossRefGoogle Scholar
  16. 16.
    Proakis, J.G., Manolakis, D.G.: Digital Signal Processing, Principles, Algorithms and applications, 4th edn. Pearson (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Victor Vermehren Valenzuela
    • 1
    • 2
  • Rafael Dueire Lins
    • 2
  • Hélio Magalhães de Oliveira
    • 2
  1. 1.Universidade Estadual do AmazonasManausBrazil
  2. 2.Universidade Federal de PernambucoRecifeBrazil

Personalised recommendations