Skip to main content

A New Algorithm for Retrieving Diffuse Attenuation Coefficient Based on Big LiDAR Bathymetry Data

  • Conference paper
  • First Online:
Cyberspace Safety and Security (CSS 2019)

Abstract

The diffuse attenuation coefficient, \( K_{d} \), is an inherent optical parameter of water. It is an important hydrologic index for both oceanography and biology. We proposed a new method to retrieve the diffuse attenuation coefficient from airborne LiDAR bathymetry data in this paper. Firstly, a formula was derived for calculating the values of \( K_{d} \) by using waveform from single laser shot. An algorithm was then deduced from the formula. \( K_{d} \) values could be retrieved from this algorithm. A case study on Wuzhizhou island in China was carried out to validate the method, it using a big Optech Aquarius bathymetry data. The results show well agreement with the MODIS products. Compared with other previous algorithms, this new algorithm could obtain the value of \( K_{d} \) from single laser shot, and the method could be applied to laser shots without bottom response even in deep or turbid water area.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Baker, K.S., Smith, R.C.: Quasi-inherent characteristics of the diffuse attenuation coefficient for irradiance. In: Ocean Optics VI, pp. 60–63 (1980)

    Google Scholar 

  2. Smith, R.C., Baker, K.S.: The bio-optical state of ocean waters and remote sensing. Limnol. Oceanogr. 23, 247–259 (1978)

    Article  Google Scholar 

  3. Guenther, G.C.: Airborne laser hydrography: System design and performance factors. National Ocean Service 1, National Oceanic and Atmospheric Administration, Rockville, MD (1985)

    Google Scholar 

  4. Mankovsky, V.I.: Relation between the diffuse attenuation coefficient and the Secchi depth. Oceanology 54, 32–37 (2014)

    Article  Google Scholar 

  5. Zaneveld, J.R.V.: A reflectivetube absorption meter. In: Proceedings Spie, vol. 1302, pp. 124–136 (1990)

    Google Scholar 

  6. Lee, Z.P., Darecki, M., Carder, K.L., et al.: Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods. J. Geophys. Res.-Oceans, vol. 110 (2005)

    Google Scholar 

  7. Jamet, C., Loisel, H., Dessailly, D.: Estimation of the Diffuse Attenuation Coefficient Kd(Lambda) with a Neural Network Inversion. In: IEEE International Geoscience and Remote Sensing Symposium (Igarss), pp. 114–117 (2011)

    Google Scholar 

  8. Yu, X., Salama, M.S., Shen, F., et al.: Retrieval of the diffuse attenuation coefficient from GOCI images using the 2SeaColor model: a case study in the Yangtze Estuary. Remote Sens. Environ. 175, 109–119 (2016)

    Article  Google Scholar 

  9. Billard, B., Abbot, R.H., Penny, M.F.: Airborne estimation of sea turbidity parameters from the WRELADS laser airborne depth sounder. Appl. Opt. 25, 2080–2088 (1986)

    Article  Google Scholar 

  10. Smart, J.H, Kang, H.K.K.: Comparisons between in-situ and remote sensing estimates of diffuse attenuation profiles. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 2964 (1996)

    Google Scholar 

  11. Wang, C.-K., Philpot, W.D.: Using airborne bathymetric lidar to detect bottom type variation in shallow waters. Remote Sens. Environ. 106, 123–135 (2007)

    Article  Google Scholar 

  12. Hofton, M.A., Minster, J.B., Blair, J.B.: Decomposition of laser altimeter waveforms. IEEE Trans. Geosci. Remote Sens. 38, 1989–1996 (2000)

    Article  Google Scholar 

  13. Allouis, T., Bailly, J.-S., Pastol, Y., et al.: Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near-infrared and green signals. Earth Surface Processes and Landforms (2010)

    Google Scholar 

  14. Marquardt, D.W.: An algorithm for least square estimation of non-linear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963)

    Article  Google Scholar 

  15. Wang, C., Li, Q., Liu, Y., et al.: A comparison of waveform processing algorithms for single-wavelength LiDAR bathymetry. ISPRS J. Photogrammetry Remote Sens. 101, 22–35 (2015)

    Article  Google Scholar 

  16. Ding, K., Li, Q., Zhu, J., et al.: An improved quadrilateral fitting algorithm for the water column contribution in airborne Bathymetric Lidar waveforms. Sensors 18(2), 552 (2018)

    Article  Google Scholar 

  17. NASA. http://oceancolor.gsfc.nasa.gov/cgi/l3. Accessed 27 Dec 2018

  18. Li, K., Tong, X., Zhang, Y., et al.: Inversion of diffuse attenuation coefficient spectral in the Yellow Sea/East China Sea and evaluation of laser bathymetric performance. J. Remote Sens. 19, 761–769 (2015)

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Shenzhen Future Industry Development Funding program (No. 201507211219247860), the Shenzhen Scientific Research and Development Funding Program (No. JCYJ20170302144002028), and the Social Science and Technology Development (Key) Project of Dongguan City (No.20185071401606).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kai Ding .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ding, K., Wang, C., Tao, M., Huang, P. (2019). A New Algorithm for Retrieving Diffuse Attenuation Coefficient Based on Big LiDAR Bathymetry Data. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11982. Springer, Cham. https://doi.org/10.1007/978-3-030-37337-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37337-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37336-8

  • Online ISBN: 978-3-030-37337-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics