High-resolution remote sensing of the transportation of floating macroalgae： case studies with the <italic style="font-style: italic">Ulva prolifera</italic> green tide

LIU Hailong; XING Qianguo; DING Jing; LIU Jianqiang; ZHENG Xiangyang; WU Lingjuan; LI Lin; LU Yingcheng

doi:10.11834/jrs.20235001

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High-resolution remote sensing of the transportation of floating macroalgae： case studies with the Ulva prolifera green tide
Vol. 27, Issue 1, Pages: 187-196(2023)
Published： 07 January 2023 ，
DOI： 10.11834/jrs.20235001

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刘海龙，邢前国，丁静，刘建强，郑向阳，吴玲娟，李琳，陆应诚.2023.漂浮大型藻运移高分辨率遥感观测：以黄海浒苔绿潮为例.遥感学报，27（1）： 187-196

Liu H L，Xing Q G，Ding J， Liu J Q， Zheng X Y， Wu L J， Li L and Lu Y C. 2023. High-resolution remote sensing of the transportation of floating macroalgae： case studies with the Ulva prolifera green tide. National Remote Sensing Bulletin， 27（1）：187-196
刘海龙，邢前国，丁静，刘建强，郑向阳，吴玲娟，李琳，陆应诚.2023.漂浮大型藻运移高分辨率遥感观测：以黄海浒苔绿潮为例.遥感学报，27（1）： 187-196 DOI： 10.11834/jrs.20235001.

Liu H L，Xing Q G，Ding J， Liu J Q， Zheng X Y， Wu L J， Li L and Lu Y C. 2023. High-resolution remote sensing of the transportation of floating macroalgae： case studies with the Ulva prolifera green tide. National Remote Sensing Bulletin， 27（1）：187-196 DOI： 10.11834/jrs.20235001.

摘要

遥感是海表漂浮大型藻监测的重要手段。本研究基于无人机、高分辨率卫星（Sentinel-2 MSI、GF-6 WVF、HY-1C/D CZI）获取的3组像对，对不同尺度的黄海漂浮大型藻浒苔斑块及其精细化运移过程进行了观测。其中，基于HY-1C/D双星组网实现了2.45 h内50 m分辨率级别的斑块点对点跟踪，在观测时段，风与海洋潮流的方向较为一致，二者效应叠加，使得浒苔的运移速度相对较高，平均运移速度为0.380 m/s。在10 m级分辨率的MSI、WVF影像上，浒苔斑块的水团锋面或辐聚区在不同观测角下，会导致更多或更少的入瞳太阳耀光呈现出亮、暗的区域由太阳耀光异常表征的这类辐聚区在近30 min内持续存在，且发生了明显迁移，其平均速度约为0.2 m/s。基于厘米级超高分辨率无人机图像可实现秒级至分钟级别的漂浮大型藻运移观测，平均速度为0.066 m/s，小斑块浒苔受风的影响，沿风向呈链条状分布特征，两者夹角小于15°。结果显示，浒苔斑块多呈条状，其延伸方向在不同尺度上表现出与风向的一致性，显示出风对浒苔斑块运移的影响；基于太阳海表耀光表征的海流辐聚区及浒苔斑块的空间位置变化，体现了海流动力过程对浒苔运移的影响。本研究显示，通过高时空分辨率的光学像对可对海表漂浮大型藻的运移精准观测，可望用于其运移物理动力机制的研究。

Abstract

Remote sensing is an important tool for monitoring floating macroalgae over the sea surface. In this study

three pairs of images obtained by a drone and high-resolution satellites (Sentinel-2 MSI

GF-6 WVF

HY-1C/D CZI) were selected to monitor the movements of floating green tide macroalgae (

Ulva prolifera

) in the Yellow Sea with multiple spatial scales. Based on the HY-1C/D double-star network

the point-to-point tracking of patches with a resolution level of 50 m within 2.45 hours is realized. During the observation period

the direction of wind and ocean current is relatively consistent and the superposition of the two effects makes the movement speed of macroalgae relatively high

with an average speed of 0.380 m/s. On the 10 m resolution MSI and WVF images

the water mass front or convergence area of macroalgae patches at different observation angles will lead to more or less bright and dark areas of the incoming pupil solar flare. This kind of convergence area characterized by solar flare anomaly has continued to exist 30 minutes

and has undergone significant movement

with an average velocity of about 0.2 m/s. Based on centimeter-level ultra-high-resolution UAV images

the movement observation of floating macroalgae can be realized from seconds to minutes

with an average speed of 0.066 m/s. Influenced by wind

small patches of macroalgae are distributed in a chain shape along the wind direction

and the angle between them is less than 15°. The results suggested that the orientations of macroalgae patches were consistent with wind directions

indicating the influence of wind on the movement of floating macroalgae. The convergent zones and the position changes of macroalgae patches identified from sun glitter reflected the influence of ocean hydrodynamics on the movement of macroalgae patches. This study demonstrates that high-resolution optical images can be applied for accurately monitoring the movement of floating macroalgae

as well as the investigation of the corresponding physical dynamic mechanisms.

关键词

HY-1C/1D CZIGF-6Sentinel-2无人机运移漂浮大型藻绿潮浒苔黄海

Keywords

HY-1C/D CZIGF-6Sentinel-2dronemovementfloating macroalgaegreen tideUlva proliferathe Yellow Sea

references

Cai X Q. 2014. Remote Sensing Detection and Diurnal Variation Research of Green Macro-algae Bloom by Geostationary Ocean Color Imager. Qingdao: Ocean University of China

蔡晓晴. 2014. 基于静止轨道海洋水色卫星数据的绿潮遥感探测方法和逐时变化特征研究. 青岛: 中国海洋大学

Chen X Y, Zhang J, Cui T W and Song P J. 2018. Extraction of the green tide drift velocity in the Yellow Sea based on GF-4. Haiyang Xuebao, 40(1): 29-38

陈晓英, 张杰, 崔廷伟, 宋平舰. 2018. 基于高分四号卫星的黄海绿潮漂移速度提取研究. 海洋学报, 40(1): 29-38 [DOI: 10.3969/j.issn.0253-4193.2018.01.004http://dx.doi.org/10.3969/j.issn.0253-4193.2018.01.004]

Ciappa A, Pietranera L, Coletta A and Jiang X W. 2010. Surface transport detected by pairs of COSMO-SkyMed ScanSAR images in the Qingdao region (Yellow Sea) during a macro-algal bloom in July 2008. Journal of Marine Systems, 80(1/2): 135-142 [DOI: 10.1016/j.jmarsys.2009.02.010http://dx.doi.org/10.1016/j.jmarsys.2009.02.010]

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]

Gao S, Huang J, Bai T, Cao Y J, Wu L J and Zhong S. 2014. Analysis on drifting path of Green Tides in the Yellow Sea in 2008 and 2009. Marine Sciences, 38(2): 86-90

高松, 黄娟, 白涛, 曹雅静, 吴玲娟, 钟山. 2014. 2008年与2009年黄海绿潮漂移路径分析. 海洋科学, 38(2): 86-90 [DOI: 10.11759/hykx20111201003http://dx.doi.org/10.11759/hykx20111201003]

Guan W B, Bao M, Lou X L, Zhou Z X and Yin K D. 2022. Monitoring, modeling and projection of harmful algal blooms in China. Harmful Algae, 111: 8 [DOI: 10.1016/j.hal.2021.102164http://dx.doi.org/10.1016/j.hal.2021.102164]

Hu C M and He M X. 2008. Origin and offshore extent of floating algae in Olympic sailing area. Eos, Transactions American Geophysical Union, 89(33): 302-303 [DOI: 10.1029/2008EO330002http://dx.doi.org/10.1029/2008EO330002]

Hu C M, Li X F, Pichel W G and Muller-Karger F E. 2009. Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery. Geophysical Research Letters, 36(1): L01604 [DOI: 10.1029/2008GL036119http://dx.doi.org/10.1029/2008GL036119]

Hu C M, Li D Q, Chen C S, Ge J Z, Frank E. Muller-Karger, 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: Oceans, 115(C5) [DOI: 10.1029/2009JC005561http://dx.doi.org/10.1029/2009JC005561]

Lee J H, Pang I C, Moon I J and Ryu J H. 2011. On physical factors that controlled the massive green tide occurrence along the southern coast of the Shandong Peninsula in 2008: a numerical study using a particle-tracking experiment. Journal of Geophysical Research Oceans, 116(C12): C12036 [DOI: 10.1029/2011JC007512http://dx.doi.org/10.1029/2011JC007512]

Liu B Q, Yang H, Ding X W and Li X F. 2014. Tracking the internal waves in the South China Sea with environmental satellite sun glint images. Remote Sensing Letters, 5(7): 609-618 [DOI: 10.1080/2150704X.2014.949365http://dx.doi.org/10.1080/2150704X.2014.949365]

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]

Morand P and Merceron M. 2004. Coastal eutrophication and excessive growth of macroalgae. Recent Research Developments in Environmental Biology, 1(Part Ⅱ): 395-449

Qiao F L, Wang G S, Lü X G and Dai D J. 2011. Drift characteristics of green macroalgae in the Yellow Sea in 2008 and 2010. Chinese Science Bulletin, 56(21): 2236-2242 [DOI: 10.1007/s11434-011-4551-7http://dx.doi.org/10.1007/s11434-011-4551-7]

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]

Xing Q G, An D Y, Zheng 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, Loisel H, Schmitt F, Shi P, Liu D and Keesing J. 2009. Detection of the green tide at the Yellow Sea and tracking its wind-forced drifting by remote sensing//EGU General Assembly Conference Abstracts. Vienna: EGU: 577

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]

Xu Q, Zhang H Y, Cheng Y C, Zhang S S and Zhang W. 2016. Monitoring and tracking the green tide in the Yellow Sea with satellite imagery and trajectory model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11): 5172-5181 [DOI: 10.1109/JSTARS.2016.2580000http://dx.doi.org/10.1109/JSTARS.2016.2580000]

Yang G Y, Xing Q G, Zhao C H, Meng M M and Li J H. 2021. Detection of floating green algae based on UAV RGB optical camera. Acta Laser Biology Sinica, 30(4): 316-324

杨国英, 邢前国, 赵春晖, 孟苗苗, 李敬虎. 2021. 基于无人机RGB光学相机的漂浮绿藻探测研究. 激光生物学报, 30(4): 316-324 [DOI: 10.3969/j.issn.1007-7146.2021.04.004http://dx.doi.org/10.3969/j.issn.1007-7146.2021.04.004]

Yuan C, Zhang J Y, Xiao J, Fu M Z, Zhang X L, Cui T W and Wang Z L. 2020. The spatial and temporal distribution of floating green algae in the Subei Shoal in 2018 retrieved by Sentinel-2 images. Haiyang Xuebao, 42(8): 12-20

袁超, 张靖宇, 肖洁, 傅明珠, 张学雷, 崔廷伟, 王宗灵. 2020. 基于哨兵2号卫星遥感影像的2018年苏北浅滩漂浮绿藻时空分布特征研究. 海洋学报, 42(8): 12-20 [DOI: 10. 3969/j.issm.0253-4193.2020.08.002http://dx.doi.org/10.3969/j.issm.0253-4193.2020.08.002]

Zheng X Y, Xing Q G, Li L and Shi P. 2011. Numerical simulation of the 2008 green tide in the Yellow Sea. Marine Sciences, 35(7): 82-87

郑向阳, 邢前国, 李丽, 施平. 2011. 2008年黄海绿潮路径的数值模拟. 海洋科学, 35(7): 82-87

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