Advertisement

Assessment of Regression and Classification Methods Using Remote Sensing Technology for Detection of Coastal Depth (Case Study of Bushehr Port and Kharg Island)

  • Ali Moeinkhah
  • Alireza ShakibaEmail author
  • Zeinab Azarakhsh
Research Article

Abstract

Any decision-making and planning in coastal areas and ports require a series of basic information in which bathymetry is an important part of it. The concern of most scholars who deal with or research in this area is to access accurate, up-to-date, and cost-effective information. One of the methods to achieve such information is to use remote sensing technologies. The purpose of this study is to use Landsat 8 Operational Land Imager and Random Forest algorithm by using regression and classification methods for depth prediction in a part of Persian Gulf region (Bushehr port, Kharg Island, and its surroundings). For verification of depth prediction in these two methods, two indices of root mean square error and mean absolute error in the regression method and the Kappa index (KAPPA) for classification method were used. The results of these two methods show that in both regression and classification methods, the best combination of bands for depth prediction was the band combination (1–2–3–4) and the Landsat 8 satellite image has the ability to obtain depth with a fairly acceptable accuracy to depth of around 10 m. From the depths of 10 m onward, the measurement error will increase relative to the depth.

Keywords

Bathymetry Random forest algorithm Classification Regression Remote sensing Persian Gulf 

Notes

References

  1. Alavipanah, K. (2013). Fundamentals of remote sensing and interpretation of satellite images and aerial photographs. Tehran: Tehran University Press.Google Scholar
  2. Alavipanah, K., Matinfar, H., & Rafie, A. (2009). Application of information technologies in earth sciences (digital soil science). Tehran: Tehran University Press.Google Scholar
  3. Al-Ghadban, A. J. M. G. (1990). Holocene sediments in a shallow bay, southern coast of Kuwait, Arabian Gulf. Marine Geology, 92(3–4), 237–254.Google Scholar
  4. Al-Naimi, N., Raitsos, D. E., Ben-Hamadou, R., & Soliman, Y. J. R. S. (2017). Evaluation of satellite retrievals of chlorophyll-a in the Arabian Gulf. Remote Sensing, 9, 301.  https://doi.org/10.3390/rs9030301.Google Scholar
  5. Bonyad, A., & Hajyghaderi, T. (2008). Inventorying and mapping of natural forest stands of zanjan province using landsat ETM+ image data. JWSS-Isfahan University of Technology, 11(42), 627–638.Google Scholar
  6. Breiman, L. J. M. L. (2001). Random forests. In Machine Learning, vol. 45 (pp. 5–32). The Netherlands: Kluwer Academic Publishers.Google Scholar
  7. Ceyhun, Ö., & Yalçın, A. (2010). Remote sensing of water depths in shallow waters via artificial neural networks. Estuarine, Coastal and Shelf Science, 89(1), 89–96.Google Scholar
  8. Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., et al. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792.Google Scholar
  9. Dekker, A. G., Phinn, S. R., Anstee, J., Bissett, P., Brando, V. E., Casey, B., et al. (2011). Intercomparison of shallow water bathymetry, hydro-optics, and benthos mapping techniques in Australian and Caribbean coastal environments. Limnology and Oceanography: Methods, 9(9), 396–425.Google Scholar
  10. Deng, Z., Ji, M., & Zhang, Z. (2008). Mapping bathymetry from multi-source remote sensing images: A case study in the Beilun estuary, Guangxi, China. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(Part B8), 1321–1326.Google Scholar
  11. Doxani, G., Papadopoulou, M., Lafazani, P., Pikridas, C., & Tsakiri-Strati, M. (2012). Shallow-water bathymetry over variable bottom types using multispectral Worldview-2 image. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39(8), 159–164.Google Scholar
  12. EL-Hattab, A. I. J. T. E. J. O. R. S., & Science, S. (2014). Single beam bathymetric data modelling techniques for accurate maintenance dredging. 17(2), 189–195.Google Scholar
  13. Eugenio, F., Marcello, J., & Martin, J. (2015). High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3539–3549.Google Scholar
  14. Fatemi, B., & Rezaei, Y. (2015). Basics of remote sensing (3rd ed.). Amsterdam: Azadeh Publications.Google Scholar
  15. Gao, J. (2009). Bathymetric mapping by means of remote sensing: Methods, accuracy and limitations. Progress in Physical Geography, 33(1), 103–116.Google Scholar
  16. Guo, L., Chehata, N., Mallet, C., & Boukir, S. (2011). Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), 56–66.Google Scholar
  17. Hare, R., Eakins, B., & Amante, C. J. T. I. H. R. (2011). Modelling bathymetric uncertainty. International Hydrographic Review, pp. 31–42.Google Scholar
  18. Hell, B. (2011). Mapping bathymetry: From measurement to applications. Stockholm: Department of Geological Sciences, Stockholm University.Google Scholar
  19. Hickman, G. D., & Hogg, J. E. (1969). Application of an airborne pulsed laser for near shore bathymetric measurements. Remote Sensing of Environment, 1(1), 47–58.Google Scholar
  20. Jakimow, B., Oldenburg, C., Rabe, A., Waske, B., van der Linden, S., & Hostert, P. (2012). Manual for application: ImageRF (1.1). Universtat Bonn and Humboldt Universitat zu Berlin, Germany.Google Scholar
  21. Kobryn, T. H., Wouters, K., Beckley, L. E., & Heege, T. (2013). Ningaloo reef: Shallow marine habitats mapped using a hyperspectral sensor halina. PLoS One, 8(7), e70105.Google Scholar
  22. Lyzenga, D. R. (1985). Shallow-water bathymetry using combined lidar and passive multispectral scanner data. International Journal of Remote Sensing, 6(1), 115–125.Google Scholar
  23. Lyzenga, D. R., Malinas, N. P., & Tanis, F. J. (2006). Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing, 44(8), 2251–2259.Google Scholar
  24. Ma, S., Tao, Z., Yang, X., Yu, Y., Zhou, X., & Li, Z. (2014). Bathymetry retrieval from hyperspectral remote sensing data in optical-shallow water. IEEE Transactions on Geoscience and Remote Sensing, 52(2), 1205–1212.Google Scholar
  25. Minghelli-Roman, A., Goreac, A., Mathieu, S., Spigai, M., & Gouton, P. (2009). Comparison of bathymetric estimation using different satellite images in coastal sea waters. International Journal of Remote Sensing, 30(21), 5737–5750.Google Scholar
  26. Mishra, D., Narumalani, S., Lawson, M., & Rundquist, D. (2004). Bathymetric mapping using IKONOS multispectral data. GIScience & Remote Sensing, 41(4), 301–321.Google Scholar
  27. Mishra, D. R., Narumalani, S., Rundquist, D., Lawson, M., & Perk, R. (2007). Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach. Journal of Geophysical Research: Oceans, 112(C8), C08014.Google Scholar
  28. Mohamed, H., Negm, A., Zahran, M., Saavedra, O. C. J. I. J. O. E. S., & Development (2016). Bathymetry determination from high resolution satellite imagery using ensemble learning algorithms in Shallow Lakes: case study El-Burullus Lake. 7(4), 295.Google Scholar
  29. Moore, G. K. J. H. S. J. (1980). Satellite remote sensing of water turbidity/Sonde de télémesure par satellite de la turbidité de l’eau. Hydrological Sciences Bulletin, 25(4), 407–421.Google Scholar
  30. Nosrati, K., Zahtabiyan, G., Moradi, I., & Shahbazi, A. (2007). Evaluation of the method of random septic tuning for the production of meteorological data. Geographical Research in Iran (62), 1–9.Google Scholar
  31. Novo, E., Hansom, J., & Curran, P. J. R. S. (1989). The effect of sediment type on the relationship between reflectance and suspended sediment concentration. International Journal of Remote Sensing, 10(7), 1283–1289.Google Scholar
  32. Parsafar, N., & Marofi, S. (2011). Estimation of different soil temperature depths from air temperature using regression, neural network and fuzzy network (Case study: Kermanshah). Journal of Water and Soil Science in Iran, 3(2).Google Scholar
  33. Pattanaik, A., Sahu, K., & Bhutiyani, M. (2015). Estimation of shallow water bathymetry using IRS-multispectral imagery of Odisha Coast, India. Aquatic Procedia, 4, 173–181.Google Scholar
  34. Perrone, T. J. G. S. (1979). Winter Shamal in the Persian Gulf. Monterey: Naval Environmental Prediction Research Facility.Google Scholar
  35. Pushparaj, J., & Hegde, A. V. (2017). Estimation of bathymetry along the coast of Mangaluru using Landsat-8 imagery. The International Journal of Ocean and Climate Systems, 8(2), 71–83.Google Scholar
  36. Reynolds, R. J. M. E. U. N. (1993). Physical oceanography of the Persian Gulf, Strait of Hormuz, and the Gulf of Oman Results from the Mt. 11973. Submitted to the Marine Pollution Bulletin, Pergamon Press, London.Google Scholar
  37. Siermann, J., Harvey, C., Morgan, G., & Heege, T. (2014). Satellite derived bathymetry and digital elevation models (DEM). In IPTC 2014: International petroleum technology conference. Google Scholar
  38. Smith, W. H., & Sandwell, D. T. J. S. (1997). Global sea floor topography from satellite altimetry and ship depth soundings. Science, 277(5334), 1956–1962.Google Scholar
  39. Stumpf, R. P., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1 part 2), 547–556.Google Scholar
  40. Su, H., Liu, H., & Heyman, W. D. (2008). Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Marine Geodesy, 31(4), 281–298.Google Scholar
  41. Su, H., Liu, H., Wang, L., Filippi, A. M., Heyman, W. D., & Beck, R. A. (2014). Geographically adaptive inversion model for improving bathymetric retrieval from satellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 465–476.Google Scholar
  42. Su, H., Liu, H., & Wu, Q. (2015). Prediction of water depth from multispectral satellite imagery—The regression kriging alternative. IEEE Geoscience and Remote Sensing Letters, 12(12), 2511–2515.Google Scholar
  43. Tyagi, P., & Bhosle, U. (2011). Atmospheric correction of remotely sensed images in spatial and transform domain. International Journal of Image Processing, 5(5), 564–579.Google Scholar
  44. Vahtmäe, E., & Kutser, T. (2016). Airborne mapping of shallow water bathymetry in the optically complex waters of the Baltic Sea. Journal of Applied Remote Sensing, 10(2), 025012.Google Scholar
  45. Winterbottom, S. J., & Gilvear, D. J. (1997). Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and aerial photography. River Research and Applications, 13(6), 489–499.Google Scholar
  46. Wyllie, K., Weber, T. C., & Armstrong, A. (2015). Using multibeam echosounders for hydrographic surveying in the water column: Estimating wreck least depths. Hydrographic Conference 2015. National Harbor, MD.Google Scholar
  47. Zheng, P., Deng, Z., & Ye, X. (2014). Retrieval study of lake water depth by using multi-spectral remote sensing in Bangong Co Lake. Sciences in Cold and Arid Regions, 6(3), 266–272.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  • Ali Moeinkhah
    • 1
  • Alireza Shakiba
    • 2
    Email author
  • Zeinab Azarakhsh
    • 2
  1. 1.Department of Environment and EnergyIslamic Azad University, Science and Research BranchTehranIran
  2. 2.Department of Earth ScienceShahid Beheshti UniversityTehranIran

Personalised recommendations