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Application of HY-1 CZI satellite images in monitoring of green tide in Yellow Sea

  • role： First author第一作者
  • Affiliation：

    CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

    Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：573483182@qq.com
  • Introduction：王鑫华，研究方向为海洋遥感。E-mail： 573483182@qq.com
WANG Xinhua123,  
  • Affiliation：

    CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

    Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LIU Hailong123,  
  • role： Corresponding author通信作者
  • Affiliation：

    CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

    Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：qgxing@yic.ac.cn
  • Introduction：邢前国，研究方向为海洋环境遥感、数值模拟与评估。E-mail： qgxing@yic.ac.cn
XING Qianguo123*,  
  • Affiliation：

    National Satellite Ocean Application Service, Ministry of Natural Resources Beijing 100081, China

LIU Jianqiang4,  
  • Affiliation：

    National Satellite Ocean Application Service, Ministry of Natural Resources Beijing 100081, China

DING Jing4,  
  • Affiliation：

    CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

    Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China

JIN Song12

résumé

The green tide in the Southern Yellow Sea have appeared 1.5 decades since 2007, resulting in a great losses to the marine ecological environment and government finance. With the help of multiple satellite images, remote sensing technique play an import part in monitoring green tide outbreak process. Yet selecting an appropriate sensor is a precondition in quantifying the severity of green tide. HY-1C satellite is great superior to other sensors for its relatively high spatial resolution (50 m), wide swath width (950 km) as well as short revisit time (3 days). Here, we introduce the green tide in GF-6 WFV images (16 m) to evaluate the capability of CZI images in the monitoring of green tide, and then compare the CZI results with the traditional MODIS images (250 m). And the GF-6 WFV images is also applied to evaluate the omission rate and accuracy of green tide extraction in the CZI and MODIS images. Based on the convert parameter between MODIS and CZI green tide mapping result, we get high frequency green tide outbreak process in 2019, 2020 and 2021.In this study, dynamic threshold is introduced to extract green tide from the DVI results and the coverage area of green tide will be obtained by adding up the area of pixel. Besides, the ratio of coverage area to affected area is used as the aggregation density of green tide. The relationship between the ratio of coverage area of green tide from MODIS and CZI images and aggregation density of green tide is also analyzed. And we give the linear relationship of coverage area of green tide obtained from satellite images with different resolution.Results indicate that the average omission rate of CZI images (6.64%) is much lower than that of MODIS images (34.08%). In addition, the coverage area of green tide acquired from MODIS and CZI images is high linearly correlated, so both of them can be linear transformed. With the combination of CZI images and MODIS images, the daily coverage areas of green tide in the Yellow Sea in 2019, 2020 and 2021 are retrieved. The CZI-based maximum daily coverage areas of green tide were 2290 km², 336 km² and 1949 km², respectively, which are consistent with the evolutions in the countermeasures adopted by managers to control the green tide.This study shows that, CZI image has the advantages of lower omission rate and higher accuracy of coverage area in monitoring of green tide in contrast to the MODIS image. And the defect of observation frequency in CZI image will be improved by the linear conversion of coverage area of green tide from MODIS and CZI images. Then, the high-frequency and high-precision of the observation of green tide will be realized.

mots-clés

green tide;Ulva prolifera;HY-1 CZI;MODIS;coverage area;omission rate;the Yellow Sea

References

  1. 1.
    An D Y, Xing Q G, Wei Z N and Li L. 2018. Spectral features and analysis of typical floating macroalgae in the Yellow Sea. Oceanologia et Limnologia Sinica, 49(5): 1054-1060
  2. 2.
    Cai L N, Zhou M R, Liu J Q, Tang D L and Zuo J C. 2020. HY-1C observations of the impacts of islands on suspended sediment distribution in Zhoushan Coastal waters, China. Remote Sensing, 12(11): 1766-1780
  3. 3.
    Cui T W, Liang X J, Gong J L, Tong C, Xiao Y F, Liu R J, Zhang X and Zhang J. 2018. Assessing and refining the satellite-derived massive green macro-algal coverage in the Yellow Sea with high resolution images. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 315-324
  4. 4.
    Cui T W, Zhang J, Sun L E, Jia Y J, Zhao W J, Wang Z L and Meng J M. 2012. Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation. International Journal of Remote Sensing, 33(17): 5513-5527
  5. 5.
    Harun-Al-Rashid A and Yang C S. 2018. Hourly variation of green tide in the Yellow Sea during summer 2015 and 2016 using Geostationary Ocean Color Imager data. International Journal of Remote Sensing, 39(13): 4402-4415
  6. 6.
    Hu C M. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment, 113(10): 2118-2129
  7. 7.
    Hu C M, Li D Q, Chen C S, Ge J Z, Muller-Karger F E, Liu J P, Yu F and He M X. 2010. On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea. Journal of Geophysical Research, 115(C5): C05017
  8. 8.
    Hu L B, Zeng K, Hu C M and He M X. 2019. On the remote estimation of Ulva prolifera areal coverage and biomass. Remote Sensing of Environment, 233: 194-207
  9. 9.
    Hu P, Liu Y H, Hou Y J and Yi Y Q. 2018. An early forecasting method for the drift path of green tides: a case study in the Yellow Sea, China. International Journal of Applied Earth Observation and Geoinformation, 71: 121-131
  10. 10.
    Jiang X W, Liu J Q, Zou B, Wang Q M, Zeng T, Guo M H, Zhu H T, Zou Y R and Tang J W. 2009. The satellite remote sensing system used in emergency response monitoring for Entermorpha prolifera disaster and its application. Acta Oceanologica Sinica, 31(1): 52-64
  11. 11.
    Li L, Xing Q G, Li X R, Yu D F, Zhang J and Zou J Q. 2018a. Assessment of the impacts from the world's largest floating macroalgae blooms on the water clarity at the West Yellow Sea using MODIS data (2002—2016). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5): 1397-1402
  12. 12.
    Li L, Zheng X Y, Wei Z N, Zou J Q and Xing Q G. 2018b. A spectral-mixing model for estimating sub-pixel coverage of sea-surface floating macroalgae. Atmosphere-Ocean, 56(4): 296-302
  13. 13.
    Liang C, Liu L, Liu J Q, Zou B, Zou Y R and Cui S X. 2020. Extracting mangrove information using MNF transformation based on HY-1C CZI spectral indices reconstruction data. Acta Oceanologica Sinica, 42(4): 104-112
  14. 14.
    Liu D Y, Keesing J K, Xing Q G and Shi P. 2009. World’s largest macroalgal bloom caused by expansion of seaweed aquaculture in China. Marine Pollution Bulletin, 58(6): 888-895
  15. 15.
    Liu J C, Liu J Q, Ding J and Lu Y C. 2022. A refined imagery algorithm to extract green tide in the Yellow Sea from HY-1C satellite CZI measurements. Acta Oceanologica Sinica, 44(5): 1-11
  16. 16.
    Miao X X, Xiao J, Pang M, Zhang X L, Wang Z L, Miao J W and Li Y. 2018. Effect of the large-scale green tide on the species succession of green macroalgal micro-propagules in the coastal waters of Qingdao, China. Marine Pollution Bulletin, 126: 549-556
  17. 17.
    Qi L and Hu C M. 2021. To what extent can Ulva and Sargassum be detected and separated in satellite imagery? Harmful Algae, 103: 102001
  18. 18.
    Qi L, Hu C M, Wang M Q, Shang S L and Wilson C. 2017. Floating algae blooms in the East China Sea. Geophysical Research Letters, 44(22): 11501-11509
  19. 19.
    Qiu Z F, Li Z X, Bilal M, Wang S Q, Sun D Y and Chen Y L. 2018. Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images. Optics Express, 26(21): 26810-26829
  20. 20.
    Son Y B, Choi B J, Kim Y H and Park Y G. 2015. Tracing floating green algae blooms in the Yellow Sea and the East China Sea using GOCI satellite data and Lagrangian transport simulations. Remote Sensing of Environment, 156: 21-33
  21. 21.
    Wang C Y, Chu J L, Tan M, Shao F J, Sui Y and Li S J. 2017. An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image. Acta Oceanologica Sinica, 36(11): 106-114
  22. 22.
    Wang X H, Xing Q G, An D Y, Meng L, Zheng X Y, Jiang B and Liu H L. 2021. Effects of spatial resolution on the satellite observation of floating macroalgae blooms. Water, 13(13): 1761
  23. 23.
    Wei Z N. 2019. Seaweed Cultivation Region Remote Sensing and Analysis on the Dynamics in Seaweed-Facility Recycling in Northern Jiangsu Shoal. Beijing: Yantai Institute of Coastal Zone Research, Chinese Academy of sciences
  24. 24.
    Xing Q G, An D Y, Zhen X Y, Wei Z N, Wang X H, Li L, Tian L Q and Chen J. 2019. Monitoring seaweed aquaculture in the Yellow Sea with multiple sensors for managing the disaster of macroalgal blooms. Remote Sensing of Environment, 231: 111279
  25. 25.
    Xing Q G, Guo R H, Wu L L, An D Y, Cong M, Qin S and Li X R. 2017. High-resolution satellite observations of a new hazard of golden tides caused by floating Sargassum in Winter in the Yellow Sea. IEEE Geoscience and Remote Sensing Letters, 14(10): 1815-1819
  26. 26.
    Xing Q G and Hu C M. 2016. Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: application of a virtual baseline reflectance height technique. Remote Sensing of Environment, 178: 113-126
  27. 27.
    Xing Q G, Wu L L, Tian L Q, Cui T W, Li L, Kong F Z, Gao X L and Wu M Q. 2018. Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign. Marine Pollution Bulletin, 133: 150-156
  28. 28.
    Xing Q G, Zheng X Y, Shi P, Hao J J, Yu D F, Liang S Z, Liu D Y and Zhang Y Z. 2011. Monitoring “green tide” in the yellow sea and the East China Sea using multi-temporal and multi-source remote sensing images. Spectroscopy and Spectral Analysis, 31(6): 1644-1647
  29. 29.
    Ye N, Jia J J, Tian J, Su H B, Luo W M, Zhang F and Xiao K. 2013. Advances in the study of Ulva polifera monitoring with remote sensing. Remote Sensing for Land and Resources, 25(1): 7-12
  30. 30.
    Zhang H L, Qiu Z F, Devred E, Sun D Y, Wang S Q, He Y J and Yu Y. 2019. A simple and effective method for monitoring floating green macroalgae blooms: a case study in the Yellow Sea. Optics Express, 27(4): 4528-4548
  31. 31.
    Zhang H L, Sun D Y, Li J S, Qiu Z F, Wang S Q and He Y J. 2016. Remote sensing algorithm for detecting green tide in China coastal waters based on GF1-WFV and HJ-CCD data. Acta Optica Sinica, 36(6): 36-44

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