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Large river bed sediment characterization with low-cost sidescan sonar

Large river bed sediment characterization with low-cost sidescan sonar

Presented at SEDHYD 2015

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Daniel Buscombe

April 02, 2015
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Transcript

  1. None
  2. Sampling riverbed substrates is tough when ... ... water is

    too turbid or deep to image the bed from aerial platforms ... too swift or deep to wade to obtain physical samples ... spatial variability is so great its impractical to sample using conventional methods
  3. Sampling riverbed substrates is tough when ... ... water is

    too turbid or deep to image the bed from aerial platforms ... too swift or deep to wade to obtain physical samples ... spatial variability is so great its impractical to sample using conventional methods
  4. Sampling riverbed substrates is tough when ... ... water is

    too turbid or deep to image the bed from aerial platforms ... too swift or deep to wade to obtain physical samples ... spatial variability is so great its impractical to sample using conventional methods
  5. Sidescan sonar

  6. Humminbird R fishfinder

  7. Low-cost, low-profile inexpensive sidescan sonar Operated by one person in

    any river or stream navigable by small boat. Sufficient quality for bed imaging in shallow streams Automate sonar processing, positioning, and substrate classification
  8. Colorado River Scans

  9. Penobscot River Scans Visual interpretations are labour-intensive, subjective and impractical

    for mapping large areas of riverbed
  10. Substrate and non-native fish Melis and Korman, 2011

  11. Colorado River in Marble Canyon Anima et al, 1998 USGS

    OFR: sidescan and video surveys Up to 100% change in sand coverage over a few 100 m Up to 100% change in sand coverage over 2 years
  12. Substrate and non-native fish

  13. Wavelet Texture Analysis

  14. Correction for shadow bias Object of given height, shadow lengthening

    goes with with grazing angle Buscombe et al., Journal of Hydraulic Engineering, in review
  15. Texture Lengthscale Mean texture lengthscale (m) w = (s σ2(s)

    δs) cos(θ) Scale (m) Normalized variance spectrum Grazing angle Buscombe et al., Journal of Hydraulic Engineering, in review
  16. Wavelet Texture Analysis

  17. Colorado River in Glen Canyon

  18. Colorado River in Marble Canyon at RM 30

  19. Colorado River in Marble Canyon at RM 30

  20. Colorado River in Marble Canyon at RM 61

  21. Colorado River in Marble Canyon at RM 61

  22. Summary Low-cost, low-profile, and highly portable sidescan sonar for imaging

    shallow riverine benthic sediments. A new automated, spatially explicit and physically based method for texture lengthscales in sidescan echograms Not a direct measure of grain size could provide a basis for objective, automated riverbed sediment classification
  23. Summary Low-cost, low-profile, and highly portable sidescan sonar for imaging

    shallow riverine benthic sediments. A new automated, spatially explicit and physically based method for texture lengthscales in sidescan echograms Not a direct measure of grain size could provide a basis for objective, automated riverbed sediment classification
  24. Summary Low-cost, low-profile, and highly portable sidescan sonar for imaging

    shallow riverine benthic sediments. A new automated, spatially explicit and physically based method for texture lengthscales in sidescan echograms Not a direct measure of grain size could provide a basis for objective, automated riverbed sediment classification
  25. Summary Low-cost, low-profile, and highly portable sidescan sonar for imaging

    shallow riverine benthic sediments. A new automated, spatially explicit and physically based method for texture lengthscales in sidescan echograms Not a direct measure of grain size could provide a basis for objective, automated riverbed sediment classification
  26. Thanks for listening Buscombe et al., Automated riverbed sediment classification

    using low-cost sidescan sonar. Journal of Hydraulic Engineering, in review dbuscombe@usgs.gov
  27. This slide is intentionally blank.

  28. Texture Lengthscale Autospectral variance (function of scale, s) σ2(s) =

    (n - ( nW 2 n (s) δy))2 W 2 n (s)δy Location along scan (pixel) Wavelet power spectrum (function of location and scale) Metres between pixels Buscombe et al., Journal of Hydraulic Engineering, in review
  29. Bed picking

  30. Shadow removal