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Extraction of Artemia slicks from HY-1C CZI images： Taking Ebinur Lake as an example

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Hubei LuoJia Laboratory, Wuhan 430079, China

  • Email：yelanchuimeng@163.com
  • Introduction：王欣，研究方向为水环境定量遥感。E-mail： yelanchuimeng@163.com
WANG Xin12,  
  • Affiliation：

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

LIU Jianqiang3,  
  • Affiliation：

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

DING Jing3,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Hubei LuoJia Laboratory, Wuhan 430079, China

TIAN Jingyi12,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Hubei LuoJia Laboratory, Wuhan 430079, China

SUN Xianghan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Hubei LuoJia Laboratory, Wuhan 430079, China

  • Email：tianliqiao@whu.edu.cn
  • Introduction：田礼乔，研究方向为水体光学装备研发与组网观测、国产卫星水色遥感大数据处理与基于深度学习算法的水环境参数反演与遥感监测应用等。E-mail： tianliqiao@whu.edu.cn
TIAN Liqiao12*

реферат

Artemia is a kind of small crustacean that lives in high salinity water, which can be used as an excellent fishery feed and an important component of carbon flux and biological chain in salt lakes. Because of its nonnegligible ecological and economic value, it is of great significance to develop a high-precision extraction method of Artemia based on remote sensing data for biological resource monitoring and reasonable fishing. Taking Ebinur Lake as an example, this paper proposed an automatic method to extract Artemia based on the HY-1C Coastal Zone Imager (CZI) images and deep learning technology. Firstly, the spectral characteristics of HY-1C CZI and Landsat-8 OLI sensors in the Artemia endmember were analyzed and the Spectral Band Adjustment Factors (SBAF) were used to eliminate the response differences between the two sensors to construct the Artemia-water dataset containing 837 effective samples of 64×64 size. Secondly, 70% of the dataset was used to train the U-Shaped Fully Convolutional Neural Network (U-Net) with a depth of 5, and the remaining 20% and 10% of the data were used to verify and test the algorithm, respectively. The model iterated 6700 times in the training process, which took 35 minutes. During this period, we used the adaptive moment estimation (Adam) optimizer with an initial learning rate of 1×10-4, and the binary cross entropy as the loss function. The training batch size was set to 4 since the equipment limitation. Whenever the loss value of the verification dataset did not decline within the last 3 epochs, the learning rate was halved. The training would be terminated automatically if it did not decline within the last 10 epochs. Finally, the impact factors and application potential of this method were further analyzed and discussed. The experimental results demonstrated that, compared with the Support Vector Machine (SVM), the Maximum likelihood Classification (MLC), and the Normalized Difference Water index (NDWI) algorithms, the extraction Precision and F1 score of U-Net were 92.02% and 90.55%, respectively, which were about 11%—23% higher than other methods. Even in the face of complex water background interference, the proposed method showed better robustness since the extraction error of the U-Net was only 3.3%. In addition, the maximum extraction area of Artemia slicks in 10 CZI images from 2019 to 2021 was 9.27 km2, 5.8 times larger than the minimum. So was the water area of Ebinur Lake, which varied sharply between 497.34 km2 and 330.93 km2. The violent difference in Artemia extraction may be due to the variations of water area and other natural or human factors such as temperature, wind speed, precipitation, pollution, and overfishing. It is necessary to conduct a profound study on their correlation relationship. Therefore, the future effort still needs to establish further a more representative and inclusive remote sensing dataset of Artemia, and develop more reliable and practical algorithms to carry out more long time series and large-scale studies in Artemia extraction.

ключеви́че слова́

HY-1C Satellite;Coastal Zone Imager;Artemia slicks;Ebinur Lake;U-Net

References

  1. 1.
    Bruce L C and Imberger J. 2009. The role of zooplankton in the ecological succession of plankton and benthic algae across a salinity gradient in the Shark Bay solar salt ponds. Hydrobiologia, 626(1): 111-128
  2. 2.
    Chen Y S, Lin Z H, Zhao X, Wang G and Gu Y F. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2094-2107
  3. 3.
    Garcia-Pineda O, Staples G, Jones C E, Hu C M, Holt B, Kourafalou V, Graettinger G, DiPinto L, Ramirez E, Streett D, Cho J, Swayze G A, Sun S J, Garcia D and Haces-Garcia F. 2020. Classification of oil spill by thicknesses using multiple remote sensors. Remote Sensing of Environment, 236: 111421-111435
  4. 4.
    Gilchrist B M and Green J. 1960. The pigments of Artemia. Proceedings of the Royal Society B: Biological Sciences, 152(946): 118-136
  5. 5.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE: 770-778
  6. 6.
    Hill P R, Kumar A, Temimi M and Bull D R. 2020. HABNet: machine learning, remote sensing-based detection of harmful algal blooms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 3229-3239
  7. 7.
    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
  8. 8.
    Ioffe S and Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift//Proceedings of the 32nd International Conference on Machine Learning. Lille, ACM, 37: 448-456
  9. 9.
    Kanniah K D, Sheikhi A, Cracknell A P, Goh H C, Tan K P, Ho C S and Rasli F N. 2015. Satellite images for monitoring mangrove cover changes in a fast growing economic region in Southern Peninsular Malaysia. Remote Sensing, 7(11): 14360-14385
  10. 10.
    Li W, Liu W N, Jia Y P, Liu H Y and Tang Y. 2018. Information extraction of the Ebinur Lake Artemia based on object-oriented method. Remote Sensing for Land and Resources, 30(4): 176-181
  11. 11.
    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. Haiyangxuebao, 42(4): 104-112
  12. 12.
    Litvinenko L I, Litvinenko A I, Boiko E G and Kutsanov K. 2015. Artemia cyst production in Russia. Chinese Journal of Oceanology and Limnology, 33(6): 1436-1450
  13. 13.
    Liu C J, Duan P, Zhang F, Jim C Y, Tan M L and Chan N W. 2021. Feasibility of the spatiotemporal fusion model in monitoring Ebinur lake’s suspended particulate matter under the missing-data scenario. Remote Sensing, 13(19): 3952-3974
  14. 14.
    Liu H T, Jiang Z Y, An M, Bao L B, Zhang J H, Yuan M C, Zhang L, Zhu Y G, Liu P B and Liang K Q. 2004. Study on the resource quantity of Artemia sp lived in both Wenzi and Naoerte lakes. Chinese Journal of Fisheries, 17(1): 1-4
  15. 15.
    Liu S S, Liu X F, Jia Q X, Kong F J, Zheng M P and Lv G J. 2014. Assessment of spatial distribution and cysts resources of Artemia in late autumn in Dangxiong Co salt lake. Acta Ecologica Sinica, 34(1): 26-33
  16. 16.
    Ma T, Han C J, Chai Y H, Jia X Y, Gao J W, Shao P, Dou Y and Zhou W L. 2020. Effects of environmental factors on growth, body fatty acids and antioxidant capacity of Artemia parthenogenetic. Southwest China Journal of Agricultural Sciences, 33(10): 2370-2377
  17. 17.
    Marden B, Brown P and Bosteels T. 2020. Great Salt Lake Artemia: ecosystem functions and services with a global reach//Baxter B K and Butler J K eds. Great Salt Lake Biology. Cham: Springer: 175-237
  18. 18.
    McFeeters S K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432
  19. 19.
    Niu G C, Li J, Guo S, Pun M, Hou L and Yang L J. 2019. SuperDock: a deep learning-based automated floating trash monitoring system//2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). Dali: IEEE: 1035-1040
  20. 20.
    Pan Z K, Xu J S, Guo Y B, Hu Y M and Wang G X. 2020. Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net. Remote Sensing, 12 (10):1574-1590
  21. 21.
    Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422
  22. 22.
    Qi L, Hu C M, Mikelsons K, Wang M H, Lance V, Sun S J, Barnes B B, Zhao J and Van Der Zande D. 2020. In search of floating algae and other organisms in global oceans and lakes. Remote Sensing of Environment, 239: 111659-111678
  23. 23.
    Qi L, Yao Y, English D E, Ma R H, Luft J and Hu C M. 2021. Remote sensing of brine shrimp cysts in salt lakes. Remote Sensing of Environment, 266: 112695-112708
  24. 24.
    Rawat A, Kumar A, Upadhyay P and Kumar S. 2022. A comparative study of 1D-convolutional neural networks with modified possibilistic c-mean algorithm for mapping transplanted paddy fields using temporal data. Journal of the Indian Society of Remote Sensing, 50(2): 227-238
  25. 25.
    Ren M L, Yang W R, Jiang Z F, Guo Y, Ren B, Gao H, Zheng R X, Zhang B P and Xiong J Y. 1992. Artemia in Xinjiang Ebinur Lake. Urumchi: Xinjiang Science, Technology and Health Publishing House: 1-2
  26. 26.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234-241
  27. 27.
    Shen Y F, Liu J Q, Ding J, Jiao J N, Sun S J and Lu Y C. 2020. HY-1C COCTS and CZI observation of marine oil spills in the South China Sea. Journal of Remote Sensing(Chinese), 24(8): 933-944
  28. 28.
    Shi J, Jiao J N, Lu Y C, Zhang M W, Mao Z H and Liu Y X. 2018. Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 251-259
  29. 29.
    Sun X H, Liu J Q, Wang J R, Tian L Q, Zhou Q and Li J. 2021. Integrated monitoring of lakes’ turbidity in Wuhan, China during the COVID-19 epidemic using multi-sensor satellite observations. International Journal of Digital Earth, 14(4): 443-463
  30. 30.
    Stappen G V, Fayazi G and Sorgeloos P. 2001. International study on Artemia LXIII. Field study of the Artemia urmiana (Günther, 1890) population in Lake Urmiah, Iran//Melack J M, Jellison R, Herbst D B eds. Saline Lakes. Dordrecht: Springer: 133-143
  31. 31.
    Visitacion M R, Alnin C A, Ferrer M R and Suñiga L. 2019. Detection of algal bloom in the coastal waters of Boracay, Philippines using normalized difference vegetation index (NDVI) and Floating Algae Index (FAI). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W19: 479-486
  32. 32.
    Wang M Q and Hu C M. 2021. Satellite remote sensing of pelagic Sargassum macroalgae: The power of high resolution and deep learning. Remote Sensing of Environment, 264:112631-112644.
  33. 33.
    Wang J Z, Ding J L, Li G N, Liang J, Yu D L, Aishan T, Zhang F, Yang J M, Abulimiti A and Liu J. 2019. Dynamic detection of water surface area of Ebinur Lake using multi-source satellite data (Landsat and Sentinel-1A) and its responses to changing environment. Catena, 177: 189-201
  34. 34.
    Wang Q, Zhang X D, Chen G Z, Dai F, Gong Y F and Zhu K. 2018. Change detection based on Faster R-CNN for high-resolution remote sensing images. Remote Sensing Letters, 9(10): 923-932
  35. 35.
    Wu F, Li J J, Zhang B, Wang C, Zhang H, Chen F L, Li L and Xu L. 2021. Inundation monitoring of immovable cultural relics with time-series SAR images. National Remote Sensing Bulletin, 25(12): 2431-2440
  36. 36.
    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
  37. 37.
    Xu J C and Yi C. 2012. Artemia in Ebinur Lake and its application in fishery. Animals Breeding and Feed, (9): 83-85
  38. 38.
    Yamano H, Sakuma A and Harii S. 2020. Coral-spawn slicks: reflectance spectra and detection using optical satellite data. Remote Sensing of Environment, 251: 112058-112065
  39. 39.
    Yang J T, Wang X L, Li B Q and Xin N H. 2019. The investigation and research on the biological resources of salina in Xinjiang Aibi Lake. Journal of Salt Science and Chemical Industry, 48(2): 35-38
  40. 40.
    Yao H G, Wang C, Yu J, Bai X J and Li W. 2020. Recognition of small-target ships in complex satellite images. National Remote Sensing Bulletin, 24(2): 116-125
  41. 41.
    Zhang F, Tiyip T, Johnson V C, Kung H T, Ding J L, Sun Q, Zhou M, Kelimu A, Nurmuhammat I and Chan N W. 2015. The influence of natural and human factors in the shrinking of the Ebinur Lake, Xinjiang, China, during the 1972—2013 period. Environmental Monitoring and Assessment, 187(1): 4128-4141
  42. 42.
    Zhang J, Chen L Q and Chen X L. 2016. Monitoring the cyanobacterial blooms based on remote sensing in Lake Erhai by FAI. Journal of Lake Sciences, 28(4): 718-725
  43. 43.
    Zhang L P, Zhang L F and Du B. 2016. Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2): 22-40
  44. 44.
    Zhang Q, Yuan Q Q, Li J, Li Z W, Shen H F and Zhang L P. 2020a. Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 162: 148-160
  45. 45.
    Zhang X, Han LX, Han L H and Zhu L. 2020b. How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery? Remote Sensing, 12 (3):417-445.
  46. 46.
    Zhang T T, Hu H, Ma X S and Zhang Y B. 2020c. Long-term spatiotemporal variation and environmental driving forces analyses of algal blooms in Taihu Lake based on multi-source satellite and land observations. Water, 12(4): 1035-1051
  47. 47.
    Zheng Y Q, Yang X F and Li Z W. 2020. Detection of severe convective cloud over sea surface from geostationary meteorological satellite images based on deep learning. Journal of Remote Sensing(Chinese) , 24(1): 97-106
  48. 48.
    Zhou Q, Liu J Q, Wang J R, Deng S Q and Tian L Q. 2020. Water turbidity monitoring of Zhiyin and Huangjia lakes in Wuhan for COVID-19 epidemic using HY-1C CZI data. Geomatics and Information Science of Wuhan University, 45(5): 676-681
  49. 49.
    Zhou Q, Tian L Q, Li J, Song Q J and Li W K. 2018. Radiometric cross-calibration of Tiangong-2 MWI visible/NIR channels over aquatic environments using MODIS. Remote Sensing, 10(11): 1803-1824
  50. 50.
    Zhao X, Liu R J, Ma Y, Xiao Y F, Ding J, Liu J Q and Wang Q B. 2022. Red Tide Detection Method for HY-1D Coastal Zone Imager Based on U-Net Convolutional Neural Network. Remote Sensing, 14 (1):88-107 [DOI ]

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