HY-1卫星CZI影像在黄海绿潮监测的应用
Application of HY-1 CZI satellite images in monitoring of green tide in Yellow Sea
- 2023年27卷第1期 页码:146-156
纸质出版日期: 2023-01-07
DOI: 10.11834/jrs.20235003
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纸质出版日期: 2023-01-07 ,
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王鑫华,刘海龙,邢前国,刘建强,丁静,金松.2023.HY-1卫星CZI影像在黄海绿潮监测的应用.遥感学报,27(1): 146-156
Wang X H,Liu H L,Xing Q G,Liu J Q,Ding J and Jin S. 2023. Application of HY-1 CZI satellite images in monitoring of green tide in Yellow Sea. National Remote Sensing Bulletin, 27(1):146-156
海洋一号C(HY-1C)卫星搭载的海岸带成像仪CZI(Coastal Zone Imager)广泛应用于中国近海生态灾害监测。本研究以16 m分辨率的高分六号WFV(Wide Field of View)影像提取的绿潮覆盖面积作为参考值,从绿潮漏检率和覆盖面积定量评估了CZI影像的绿潮监测能力,并与250 m分辨率的MODIS提取结果进行了对比分析。结果表明,CZI影像的绿潮平均漏检率只有MODIS影像的1/5左右,绿潮覆盖面积比MODIS影像小50%以上。MODIS和CZI影像的绿潮覆盖面积呈线性相关,将MODIS影像的绿潮覆盖面积转换为CZI结果;该结果显示,2019年、2021年度绿潮最大覆盖面积为2000 km²左右,是2020年同期的6倍左右。在绿潮监测方面,相比于MODIS,CZI影像的绿潮漏检率较低且覆盖面积更接近真实参考值。本研究建立了CZI与目前较常用的MODIS绿潮覆盖面积的转换关系,可弥补CZI观测频次的缺陷,进而实现绿潮高频次观测。
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.
绿潮浒苔HY-1C CZIMODIS漏检率覆盖面积黄海
green tideUlva proliferaHY-1 CZIMODIScoverage areaomission ratethe Yellow Sea
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
安德玉, 邢前国, 魏振宁, 李琳. 2018. 黄海典型漂浮大型藻类光谱特征分析. 海洋与湖沼, 49(5): 1054-1060 [DOI: 10.11693/hyhz20171200331http://dx.doi.org/10.11693/hyhz20171200331]
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 [DOI: 10.3390/rs12111766http://dx.doi.org/10.3390/rs12111766]
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 [DOI: 10.1016/j.isprsjprs.2018.08.001http://dx.doi.org/10.1016/j.isprsjprs.2018.08.001]
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 [DOI: 10.1080/01431161.2012.663112http://dx.doi.org/10.1080/01431161.2012.663112]
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 [DOI: 10.1080/01431161.2018.1457228http://dx.doi.org/10.1080/01431161.2018.1457228]
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 [DOI: 10.1016/j.rse.2009.05.012http://dx.doi.org/10.1016/j.rse.2009.05.012]
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 [DOI: 10.1029/2009JC005561http://dx.doi.org/10.1029/2009JC005561]
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 [DOI: 10.1016/j.rse.2019.01.014http://dx.doi.org/10.1016/j.rse.2019.01.014]
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 [DOI: 10.1016/j.jag.2018.05.001http://dx.doi.org/10.1016/j.jag.2018.05.001]
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
蒋兴伟, 刘建强, 邹斌, 王其茂, 曾韬, 郭茂华, 朱海天, 邹亚荣, 唐军武. 2009. 浒苔灾害卫星遥感应急监视监测系统及其应用. 海洋学报, 31(1): 52-64 [DOI: 10.3321/j.issn:0253-4193.2009.01.007http://dx.doi.org/10.3321/j.issn:0253-4193.2009.01.007]
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 [DOI: 10.1109/JSTARS.2018.2806626http://dx.doi.org/10.1109/JSTARS.2018.2806626]
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 [DOI: 10.1080/07055900.2018.1509834http://dx.doi.org/10.1080/07055900.2018.1509834]
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
梁超, 刘利, 刘建强, 邹斌, 邹亚荣, 崔松雪. 2020. 基于HY-1C CZI影像光谱指数重构数据MNF变换的红树林提取. 海洋学报, 42(4): 104-112 [DOI: 10.3969/j.issn.0253-4193.2020.04.012http://dx.doi.org/10.3969/j.issn.0253-4193.2020.04.012]
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 [DOI: 10.1016/j.marpolbul.2009.01.013http://dx.doi.org/10.1016/j.marpolbul.2009.01.013]
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
刘锦超, 刘建强, 丁静, 陆应诚. 2022. HY-1C 卫星 CZI 载荷的黄海绿潮提取研究. 海洋学报, 44(5): 1-11 [DOI: 10.12284/hyxb2022097http://dx.doi.org/10.12284/hyxb2022097]
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 [DOI: 10.1016/j.marpolbul.2017.09.060http://dx.doi.org/10.1016/j.marpolbul.2017.09.060]
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 [DOI: 10.1016/j.hal.2021.102001http://dx.doi.org/10.1016/j.hal.2021.102001]
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 [DOI: 10.1002/2017GL075525http://dx.doi.org/10.1002/2017GL075525]
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 [DOI: 10.1364/OE.26.026810http://dx.doi.org/10.1364/OE.26.026810]
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 [DOI: 10.1016/j.rse.2014.09.024http://dx.doi.org/10.1016/j.rse.2014.09.024]
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 [DOI: 10.1007/s13131-017-1141-9http://dx.doi.org/10.1007/s13131-017-1141-9]
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 [DOI: 10.3390/w13131761http://dx.doi.org/10.3390/w13131761]
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
魏振宁. 2019. 苏北浅滩海藻养殖区精细化遥感与筏架回收动态监测. 北京: 中国科学院大学(中国科学院烟台海岸带研究所)
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 [DOI: 10.1016/j.rse.2019.111279http://dx.doi.org/10.1016/j.rse.2019.111279]
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 [DOI: 10.1109/LGRS.2017.2737079http://dx.doi.org/10.1109/LGRS.2017.2737079]
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 [DOI: 10.1016/j.rse.2016.02.065http://dx.doi.org/10.1016/j.rse.2016.02.065]
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 [DOI: 10.1016/j.marpolbul.2018.05.035http://dx.doi.org/10.1016/j.marpolbul.2018.05.035]
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
邢前国, 郑向阳, 施平, 郝佳佳, 禹定峰, 梁守真, 刘东艳, 张渊智. 2011. 基于多源、多时相遥感影像的黄、东海绿潮影响区检测. 光谱学与光谱分析, 31(6): 1644-1647 [DOI: 10.3964/j.issn.1000-0593(2011)06-1644-04http://dx.doi.org/10.3964/j.issn.1000-0593(2011)06-1644-04]
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
叶娜, 贾建军, 田静, 苏红波, 雒伟民, 张峰, 肖康. 2013. 浒苔遥感监测方法的研究进展. 国土资源遥感, 25(1): 7-12 [DOI: 10.6046/gtzyyg.2013.01.02http://dx.doi.org/10.6046/gtzyyg.2013.01.02]
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 [DOI: 10.1364/OE.27.004528http://dx.doi.org/10.1364/OE.27.004528]
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
张海龙, 孙德勇, 李俊生, 丘仲锋, 王胜强, 何宜军. 2016. 基于GF1-WFV和HJ-CCD数据的我国近海绿潮遥感监测算法研究. 光学学报, 36(6): 0601004 [DOI: 10.3788/AOS201636.0601004http://dx.doi.org/10.3788/AOS201636.0601004]
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