Advertisement

Journal of the Indian Society of Remote Sensing

, Volume 46, Issue 12, pp 2093–2106 | Cite as

Investigating Effects of Seafloor Topography on Sea Surface Currents in the Caspian Sea and Northern Indian Ocean

  • Emad Ghaleh Noei
  • Mahdi Hasanlou
  • Mohammad Ali Sharifi
Research Article
  • 56 Downloads

Abstract

Seafloor topography certainly has an impact on ocean circulation in different ways. Due to this assumption, the sea surface currents calculated by optical flow (Horn–Schunck) and geostrophic currents methods are analyzed to observe this impact. Pair of sea surface temperature imageries, calculated sea surface height and sea level anomaly are showed beside depth map in areas with meaningful bathymetric features such as underwater mountains and pools. The reason for the formation of some eddies in the Caspian Sea and Indian Ocean is concluded from the location of pools and knolls. In this study, in addition to introducing new time span for calculating geostrophic currents, Ocean Surface Current Analyses Real-Time (OSCAR) currents are applied to validate our estimated currents. Variety of products such as sea surface temperature imageries, OSCAR currents, depth map, calculated results like sea level anomaly and absolute dynamic topography and estimated currents via optical flow and geostrophic currents have been collected in this paper to make very detailed investigation on depth effect on mentioned water parameters. Results show that impacts of knolls and pools are meaningfully clear in optical flow and geostrophic currents in shaping and rationing water motions.

Keywords

Seafloor topography Sea surface temperature Altimetry Sea surface height Optical flow Geostrophic currents 

References

  1. AVISO. (2016). DT CorSSH and DT SLA product handbookAviso, France. http://boocu.com/dt-corssh-and-dt-sla-product-handbook-aviso/. Accessed Dec 2016.
  2. Belibassakis, K. A., Gerostathis, T. P., & Athanassoulis, G. A. (2011). A coupled-mode model for water wave scattering by horizontal, non-homogeneous current in general bottom topography. Applied Ocean Research, 33(4), 384–397.  https://doi.org/10.1016/j.apor.2011.05.004.CrossRefGoogle Scholar
  3. Breaker, L. C., Loor, H. R., & Carroll, D. (2016). Trends in sea surface temperature off the coast of Ecuador and the major processes that contribute to them. Journal of Marine Systems, 164, 151–164.  https://doi.org/10.1016/j.jmarsys.2016.09.002.CrossRefGoogle Scholar
  4. Bruhn, A., Weickert, J., & Schnörr, C. (2005). Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. International Journal of Computer Vision, 61(3), 211–231.  https://doi.org/10.1023/B:VISI.0000045324.43199.43.CrossRefGoogle Scholar
  5. Cohen, I., & Herlin, I. (1996). Optical flow and phase portrait methods for environmental satellite image sequences. In B. Buxton & R. Cipolla (Eds.), Computer visionECCV’96 (pp. 141–150). Berlin Heidelberg: Springer. http://link.springer.com/chapter/10.1007/3-540-61123-1_134. Accessed February 13, 2015.
  6. Deser, C., Alexander, M. A., Xie, S.-P., & Phillips, A. S. (2010). Sea surface temperature variability: Patterns and mechanisms. Annual Review of Marine Science, 2(1), 115–143.  https://doi.org/10.1146/annurev-marine-120408-151453.CrossRefGoogle Scholar
  7. Deser, C., & Blackmon, M. L. (1993). Surface climate variations over the North Atlantic Ocean during winter: 1900–1989. Journal of Climate, 6(9), 1743–1753.  https://doi.org/10.1175/1520-0442(1993)006%3c1743:SCVOTN%3e2.0.CO;2.CrossRefGoogle Scholar
  8. Fleet, D., & Weiss, Y. (2006). Optical flow estimation. In N. Paragios, Y. Chen, & O. Faugeras (Eds.), Handbook of mathematical models in computer vision (pp. 237–257). Boston, MA: Springer.  https://doi.org/10.1007/0-387-28831-7_15.CrossRefGoogle Scholar
  9. GEBCO. (2016). The general bathymetric chart of the oceans (GEBCO). http://www.gebco.net/. Accessed July 17, 2017.
  10. Ghalenoei, E., & Hasanlou, M. (2017). Monitoring of sea surface currents by using sea surface temperature and satellite altimetry data in the Caspian Sea. Earth Observation and Geomatics Engineering, 1(1), 36–46.  https://doi.org/10.22059/eoge.2017.226309.1001.CrossRefGoogle Scholar
  11. Ghalenoei, E., Hasanlou, M., Sharifi, M. A., Vignudelli, S., & Foroughi, I. (2017). Spatiotemporal monitoring of upwelled water motions using optical flow method in the Eastern Coasts of Caspian Sea. Journal of Applied Remote Sensing, 11(3), 036016.  https://doi.org/10.1117/1.JRS.11.036016.CrossRefGoogle Scholar
  12. Ghalenoei, E., Sharifi, M. A., & Hasanlou, M. (2014). Investigation of optical flow techniques for extracting non-rigid sea surface currents. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W3, 121–126.  https://doi.org/10.5194/isprsarchives-xl-2-w3-121-2014.CrossRefGoogle Scholar
  13. Ghalenoei, E., Sharifi, M. A., & Hasanlou, M. (2015). Estimating and fusing optical flow, geostrophic currents and sea surface wind in the waters around Kish Island. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1-W5, 221–226.  https://doi.org/10.5194/isprsarchives-xl-1-w5-221-2015.CrossRefGoogle Scholar
  14. Gong, X., & Bansmer, S. (2015). Horn–Schunck optical flow applied to deformation measurement of a birdlike airfoil. Chinese Journal of Aeronautics, 28(5), 1305–1315.  https://doi.org/10.1016/j.cja.2015.07.005.CrossRefGoogle Scholar
  15. Griffiths, L. S., & Porter, R. (2012). Focusing of surface waves by variable bathymetry. Applied Ocean Research, 34, 150–163.  https://doi.org/10.1016/j.apor.2011.08.004.CrossRefGoogle Scholar
  16. Gille, S. T., & Llewellyn Smith, S. G. (2003). Bathymetry and ocean circulation. In Charting the secret world of the ocean floor, the GEBCO project 19032003.Google Scholar
  17. Gunduz, M., & Özsoy, E. (2014). Modelling seasonal circulation and thermohaline structure of the Caspian Sea. Ocean Science, 10(3), 459–471.  https://doi.org/10.5194/os-10-459-2014.CrossRefGoogle Scholar
  18. Horn, B. K., & Schunck, B. G. (1981). Determining optical flow (Vol. 0281, pp. 319–331).  https://doi.org/10.1117/12.965761.
  19. Huebner, C. S. (2016). Local motion compensation in image sequences degraded by atmospheric turbulence: A comparative analysis of optical flow vs. block matching methods (Vol. 10002, pp. 1–11). Bellingham: SPIE.  https://doi.org/10.1117/12.2240951.CrossRefGoogle Scholar
  20. Indian Ocean. (2017). Encyclopedia britannica. https://www.britannica.com/place/Indian-Ocean. Accessed July 20, 2017.
  21. Jin, F., Khellah, F. M., Fieguth, P. W., & Winger, L. (2001). Motion estimation of sparse, remotely-sensed fields. In Canadian conference on electrical and computer engineering, 2001 (Vol. 2, pp. 1135–1138). Presented at the Canadian conference on electrical and computer engineering, 2001.  https://doi.org/10.1109/ccece.2001.933601.
  22. Li, X., Li, C., Xu, Q., & Pichel, W. G. (2009). Sea surface manifestation of along-tidal-channel underwater ridges imaged by SAR. IEEE Transactions on Geoscience and Remote Sensing, 47(8), 2467–2477.  https://doi.org/10.1109/TGRS.2009.2014154.CrossRefGoogle Scholar
  23. Marcello, J., Eugenio, F., Marques, F., Hernandez-Guerra, A., & Gasull, A. (2008). Motion estimation techniques to automatically track oceanographic thermal structures in multisensor image sequences. IEEE Transactions on Geoscience and Remote Sensing, 46(9), 2743–2762.  https://doi.org/10.1109/TGRS.2008.919274.CrossRefGoogle Scholar
  24. Meinhardt-Llopis, E., Pérez, J. S., & Kondermann, D. (2013). Horn–schunck optical flow with a multi-scale strategy. Image Processing On Line, 3, 151–172.  https://doi.org/10.5201/ipol.2013.20.CrossRefGoogle Scholar
  25. Minnett, P. J., Brown, O. B., Evans, R. H., Key, E. L., Kearns, E. J., Kilpatrick, K., et al. (2004). Sea-surface temperature measurements from the moderate-resolution imaging spectroradiometer (MODIS) on Aqua and Terra. In Geoscience and remote sensing symposium, 2004. IGARSS’04. Proceedings. 2004 IEEE international (Vol. 7, pp. 4576–4579). Presented at the geoscience and remote sensing symposium, 2004. IGARSS’04. Proceedings. 2004 IEEE international.  https://doi.org/10.1109/igarss.2004.1370173.
  26. MODIS Web. (2016). NASA homepage. http://modis.gsfc.nasa.gov/about/. Accessed October 3, 2014.
  27. Marchesiello, P., & Estrade, P. (2009). Upwelling limitation by onshore geostrophic flow. Journal of Marine Research, 68(1), 37–62.  https://doi.org/10.1357/002224010793079004.CrossRefGoogle Scholar
  28. National Institute Of Oceanography (NIO). (2016). National institute of oceanography (NIO)Sea-floor data. http://www.nio.org/index/option/com_subcategory/task/show/title/Sea-floor%20Data/tid/2/sid/18/thid/113. Accessed July 17, 2017.
  29. Niu, Y., Dick, A., & Brooks, M. (2013). Linking local and global optical flow computation by subspace regularization. SPIE.  https://doi.org/10.1117/1.oe.52.3.037205.CrossRefGoogle Scholar
  30. NOAA. (2016). National geophysical data center, 2006. 2-minute gridded global relief data (ETOPO2) v2. National geophysical data center, NOAA. https://www.ngdc.noaa.gov,  https://doi.org/10.7289/v5j1012q.
  31. Pessoa, J., & Fonseca, N. (2013). Investigation of depth effects on the wave exciting low frequency drift forces by different approximation methods. Applied Ocean Research, 42, 182–199.  https://doi.org/10.1016/j.apor.2013.06.003.CrossRefGoogle Scholar
  32. Physical Oceanography Distributed Active Archive Center (PO.DAAC). (2015). Physical oceanography distributed active archive center (PO.DAAC). Firefox ESR v38.4.0 web page. NASA EOSDIS PO.DAAC, Pasadena, CA. https://podaac.jpl.nasa.gov/. Accessed December 23, 2015.
  33. Pinoli, J.-C. (2014). Mathematical foundations of image processing and analysis. Hoboken: Wiley.Google Scholar
  34. Popov, A., Miller, A., Miller, B., & Stepanyan, K. (2016). Optical flow and inertial navigation system fusion in the UAV navigation. In ResearchGate (p. 998606). Presented at the SPIE Security + Defence, SPIE.  https://doi.org/10.1117/12.2241204.
  35. Rasmusson, E. M., & Carpenter, T. H. (1982). Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Monthly Weather Review, 110(5), 354–384.  https://doi.org/10.1175/1520-0493(1982)110%3c0354:VITSST%3e2.0.CO;2.CrossRefGoogle Scholar
  36. Reddy, T. E., & Arrigo, K. R. (2006). Constraints on the extent of the Ross Sea phytoplankton bloom. Journal of Geophysical Research: Oceans, 111(C7).  https://doi.org/10.1029/2005jc003339.
  37. Rio, M.-H., Santoleri, R., Bourdalle-Badie, R., Griffa, A., Piterbarg, L., & Taburet, G. (2016). Improving the altimeter-derived surface currents using high-resolution sea surface temperature data: A feasability study based on model outputs. Journal of Atmospheric and Oceanic Technology, 33(12), 2769–2784.  https://doi.org/10.1175/JTECH-D-16-0017.1.CrossRefGoogle Scholar
  38. Saraceno, M., Strub, P. T., & Kosro, P. M. (2008). Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges. Journal of Geophysical Research: Oceans, 113(C11), C11013.  https://doi.org/10.1029/2008JC004756.CrossRefGoogle Scholar
  39. Seminoff, J. A., Zárate, P., Coyne, M., Foley, D. G., Parker, D., Lyon, B. N., et al. (2008). Post-nesting migrations of Galápagos green turtles Chelonia mydas in relation to oceanographic conditions: Integrating satellite telemetry with remotely sensed ocean data. Endangered Species Research, 4(1–2), 57–72.CrossRefGoogle Scholar
  40. Sheen, K. L., White, N., Caulfield, C. P., & Hobbs, R. W. (2011). Estimating geostrophic shear from seismic images of oceanic structure. Journal of Atmospheric and Oceanic Technology, 28(9), 1149–1154.  https://doi.org/10.1175/JTECH-D-10-05012.1.CrossRefGoogle Scholar
  41. Sindhu, B., Suresh, I., Unnikrishnan, A. S., Bhatkar, N. V., Neetu, S., & Michael, G. S. (2007). Improved bathymetric datasets for the shallow water regions in the Indian Ocean. Journal of Earth System Science, 116(3), 261–274.  https://doi.org/10.1007/s12040-007-0025-3.CrossRefGoogle Scholar
  42. Sokolov, S., & Rintoul, S. R. (2007). On the relationship between fronts of the Antarctic Circumpolar Current and surface chlorophyll concentrations in the Southern Ocean. Journal of Geophysical Research: Oceans, 112(C7), C07030.  https://doi.org/10.1029/2006JC004072.CrossRefGoogle Scholar
  43. Speich, S., Lutjeharms, J. R. E., Penven, P., & Blanke, B. (2006). Role of bathymetry in Agulhas Current configuration and behaviour. Geophysical Research Letters, 33(23), L23611.  https://doi.org/10.1029/2006GL027157.CrossRefGoogle Scholar
  44. Stewart, R. H. (2009). Introduction to Physical Oceanography. College Station, TX: Orange Grove Texts Plus.Google Scholar
  45. Venegas, R. M., Strub, P. T., Beier, E., Letelier, R., Thomas, A. C., Cowles, T., et al. (2008). Satellite-derived variability in chlorophyll, wind stress, sea surface height, and temperature in the northern California Current System. Journal of Geophysical Research: Oceans, 113(C3).  https://doi.org/10.1029/2007jc004481.
  46. Vignudelli, S., Kostianoy, A. G., Cipollini, P., & Benveniste, J. (Eds.). (2011). Coastal altimetry. Berlin, Heidelberg: Springer. http://www.coastalt.eu/files/Coastal_Altimetry_TOC.htm. Accessed October 10, 2014.
  47. Wilkin, J. L., & Zhang, W. G. (2007). Modes of mesoscale sea surface height and temperature variability in the East Australian Current. Journal of Geophysical Research: Oceans, 112(C1), C01013.  https://doi.org/10.1029/2006JC003590.CrossRefGoogle Scholar
  48. Yang, Q., Parvin, B., Mariano, A. J., Ryan, E. H., Evans, R., & Brown, O. B. (2004). Seasonal and interannual studies of vortices in sea surface temperature data. International Journal of Remote Sensing, 25(7–8), 1371–1376.  https://doi.org/10.1080/01431160310001592319.CrossRefGoogle Scholar
  49. Zonn, I. S., Kosarev, A. N., Glantz, M., & Kostianoy, A. G. (2010). The Caspian sea encyclopedia (2010th ed.). Berlin: Springer.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehranIran

Personalised recommendations