基于摄像机与水位尺的昼夜海面水位及表面波参数测量

作者

  • S. A. Myslenkov Shirshov Institute of Oceanology, Russian Academy of Sciences; Lomonosov Moscow State University; Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences
  • S. M. Morozova Lomonosov Moscow State University
  • A. D. Rybalko Shirshov Institute of Oceanology, Russian Academy of Sciences
  • Yu. V. Murzakova Shirshov Institute of Oceanology, Russian Academy of Sciences
  • V. V. Ocherednik Shirshov Institute of Oceanology, Russian Academy of Sciences
  • V. V. Mashura Shirshov Institute of Oceanology, Russian Academy of Sciences
  • E. E. Kruglova Shirshov Institute of Oceanology, Russian Academy of Sciences
  • V. A. Mikhailov Lomonosov Moscow State University
  • K. P. Myslenkova Shirshov Institute of Oceanology, Russian Academy of Sciences; National Research University Higher School of Economics
  • I. A. Rybakov Institute of Botany and Phytointroduction
  • S. S. Mukhametov Lomonosov Moscow State University
  • A. G. Zatsepin Shirshov Institute of Oceanology, Russian Academy of Sciences

DOI:

https://doi.org/10.29006/1564-2291.JOR-2025.53(4).13

关键词:

海平面测量, 风浪, 波谱, 视频观测, 视频自动化处理, 水尺

摘要

在本研究中，我们验证了利用布设在近岸带的摄像机与水尺来定义海面高度、波浪参数及波谱的可行 性。该方法基于帧率为30 Hz的视频逐帧分析，并通过亮度剖面自动识别水-气界面。借助LED补光，该 算法可适用于昼夜条件，水位判定精度可达2 cm。该方法已在黑海、白令海、日本海和波罗的海进行了试 验，使用了多种水尺与摄像机配置。文中以黑海为例，给出了水位及波浪参数的测量结果（包括显著波高、 最大波高、平均周期与最大周期），并展示了数段频率为30 Hz的波形记录示例。 该方法的关键优点是成本低、实现简便：该系统可由商用现货部件组装而成，包括摄像机、金属或木质水 尺以及用于数据处理的普通个人计算机。本方法的局限性包括：需要对水尺进行稳固固定、生物污损的影 响、在偏远海域布放设备的困难，以及因波浪绕流导致的误差。然而，该方法可根据不同环境进行适配。 所得结果可服务于科研与应用场景，包括大陆架工程勘察与业务化海洋观测等。

参考


  1. Abuzjarov, Z. K., I. O. Dumanskaya, and E. S. Nesterov, 2009: Operational oceanographic support. Moscow, 288 p., https://www.morkniga.ru/p839146.htm.

  2. Birgand, F., A. M. Smith, and M. Bassiouni et al., 2022: Field performance of the GaugeCam image-based water level measurement system. PLoS Water, 1 (7), P e0000032, https://doi.org/10.1371/journal.pwat.0000032.

  3. Bostater, Jr. C. R. and B. Yang, 2014: Shallow water surface gravity wave imaging, spectra and their use in shallow water dredging operations. Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2014, SPIE, 9240, 126–134, https://doi.org/10.1117/12.2070184.

  4. Bostater, Jr. C. R. and B. Yang, 2018: Video Measurements and Analysis of Surface Gravity. Surface Waves: New Trends and Developments, 127–143, https://doi.org/10.5772/intechopen.73042.

  5. Buscombe, D. et al., 2020: Optical wave gauging using deep neural networks. Coastal Engineering, 155, 103593, EDN: PHRAVG, https://doi.org/10.1016/j.coastaleng.2019.103593.

  6. Closson, J., 2019: Measurements and Synthetic Imaging of Water Surface Gravity Waves using Optical Techniques, https://repository.fit.edu/etd/1227.

  7. Dolgikh, G. I., V. K. Fishchenko, and A. A. Goncharova, 2019: On the possibility of recording wave activity and sea level oscillations in coastal regions of the World Ocean based on Internet video analysis. Doklady Akademii Nauk., 488 (6), 667–672, EDN: OFMTVE, https://doi.org/10.31857/S0869-56524886667-672.

  8. Elagin, A. A. and A. L. Demidov, 2016: Analysis of methods for determining sea level fluctuations during hydrographic work. Vestnik of the Admiral S. O. Makarov State University of Maritime and Inland Shipping, 2 (36), 78–89, EDN: VTNQAZ, https://doi.org/10.21821/2309-5180-2016-8-2-78-89.

  9. Gruzinov, V. M., E. V. Borisov, and A. V. Grigoriev, 2012: Applied oceanography. Obninsk, Artefaks Publ., p. 384, EDN: QKLBPV

  10. Guimarães, P. V. et al., 2020: A data set of sea surface stereo images to resolve space-time wave fields. Scientific data, 7 (1), 145 p., EDN: XUXPPL, https://doi.org/10.1038/s41597-020-0492-9.

  11. Ivonin, D. V., et al., 2013: Wind wave monitoring system in the coastal zone of the Black Sea based on radar, direct observations and modeling: first results. Problems of Regional Ecology, 4, 172–183, EDN: RNETIN

  12. Iwahashi, M., S. Udomsiri, Y. Imai, and S. Muramatsu, 2007: Water level detection for functionally layered video coding. 2007 IEEE International Conference on Image Processing. IEEE, 321–324, https://doi.org/10.1109/ICIP.2007.4379157.

  13. Kosnik, M. V. and V. A. Dulov, 2011: Extraction of short wind wave spectra from stereo images of the sea surface. Measurement science and technology, 22 (1), 015504, EDN: VDELTU, https://doi.org/10.1088/0957-0233/22/1/015504.

  14. Lin, Y. T., Y. C. Lin, and J. Y. Han, 2018: Automatic water-level detection using single-camera images with varied poses. Measurement, 127, 167–174, https://doi.org/10.1016/j.measurement.2018.05.100.

  15. Medvedeva, A. Yu., S. A. Myslenkov, I. P. Medvedev, V. S. Arkhipkin, V. A. Krechik, and S. A. Dobrolyubov, 2016: Modeling of wind waves in the Baltic Sea on rectangular and unstructured grids based on NCEP/CFSR reanalysis. Proceedings of the Russian Hydrometeorological Center, 362, 37–54, EDN: XBWOVN

  16. Medvedev, I. P. and V. S. Arkhipkin, 2015: Sea level fluctuations in Golubaya Bay (Gelendzhik). Vestnik Moskovskogo universiteta. Series 5. Geography, 3, 70–78, EDN: UJXNYZ

  17. Myslenkov, S. A. and V. A. Mikhaylov, 2019: Measurement of wave parameters in the Blue Bay (Gelendzhik) using a video camera and a measuring rod. Comprehensive Studies of the World Ocean: Proceedings of the 4th All-Russian Scientific Conference of Young Scientists, Sevastopol, April 22–26, Sevastopol, Marine Hydrophysical Institute of RAS, 2019, 372–373, EDN: FMKWWN

  18. Rybalko, A., S. Myslenkov, and S. Badulin, 2023: Wave buoy measurements at short fetches in the Black Sea nearshore: Mixed Sea and energy fluxes. Water, 15 (10), 1834, EDN: BXQHYX, https://doi.org/10.3390/w15101834.

  19. Smolov, V. E. and A. F. Rozvadovsky, 2020: Application of the Arduino platform for wind wave recording. Marine Hydrophysical Journal, 4 (214), 467–479, EDN: AKIIBG, https://doi.org/10.22449/0233-7584-2020-4-467-479.

  20. SP 11-114-2004. Engineering surveys on the continental shelf for the construction of offshore oil and gas field facilities. Moscow: FGUP “PNIIS” of Gosstroy of Russia, 2004, P. 88, https://docs.cntd.ru/document/1200037608.

  21. Vandaele, R., S. L. Dance, and V. Ojha, 2023: Calibrated river-level estimation from river cameras using convolutional neural networks. Environmental Data Science, 2, e11, EDN: KVQFAK, https://doi.org/10.1017/eds.2023.6.

  22. Voinov, G. N., N. V. Lebedev, and S. M. Morozova, 2024: Tides along the western coast of Novaya Zemlya. Rossiyskaya Arktika, 6 (1), 33–47, EDN: QLKIMW, https://doi.org/10.24412/2658-4255-2024-1-33-47.

  23. Wanek, J. M. and C. H. Wu, 2006: Automated trinocular stereo imaging system for three-dimensional surface wave measurements. Ocean engineering, 33 (5–6), 723–747, https://doi.org/10.1016/J.OCEANENG.2005.05.006.

  24. Yurovsky, Y. Y. and O. B. Kudinov, 2025: Methods and Errors of Wave Measurements Using Conventional Inertial Motion Units. Physical oceanography, 32 (1), 64, 63–83, EDN: WTGOVI

  25. Zaytsev, A. I., et al., 2024: Technical, hardware and software solutions for monitoring natural processes in the southern part of the Sea of Okhotsk. Vestnik Rossiyskoy akademii nauk, 94 (8), 760–770, EDN: FCLDKZ, https://doi.org/10.31857/S0869587324080071.

  26. Zakharchuk, E. A., V. N. Sukhachev, and N. A. Tikhonova, 2021: Storm surges in the Gulf of Finland, Baltic Sea. Vestnik Sankt-Peterburgskogo universiteta. Earth Sciences, 66 (4), 781–805, EDN: LLMELS, https://doi.org/10.21638/spbu07.2021.408.

  27. Zatsepin, A. G., V. V. Kovalenko, S. B. Kuklev, and V. A. Telegin, 2024: Coastal operational oceanography as a modern method for monitoring the shelf-slope zone of seas and oceans. Oceanology, 64 (6), 1023–1025, EDN: FHMKYW, https://doi.org/10.31857/S0030157424060143.

  28. Zhang, Z. et al., 2019: In-situ water level measurement using NIR-imaging video camera. Flow Measurement and Instrumentation, 67, 95–106, https://doi.org/10.1016/j.flowmeasinst.2019.04.004.

Downloads

已出版

2025-12-21

期次

卷 53 期 4 (2025): 海洋学研究期刊

栏目

海洋技术与仪器设备

License

Transfer of copyrights occurs on the basis of a license agreement between the Author and Shirshov Institute of Oceanology, RAS

同一作者最受欢迎的文章

1 2 3 > >>