Remote Sensing Of Floating Macroalgae Blooms In The Yellow Sea Using Multi-source Satellite Observations

Ulva prolifera green tide and Sargassum golden tide are two kinds of macro floating algae disasters in the coastal areas of China in recent years since 2007,large-scale green tide disasters have occurred in the Yellow Sea every summer and autumn,covering an area of tens of thousands of square kilometers.However,since June 2013,a large number of floating Sargassum has been found in the sea around Qingdao and Rizhao,which not only caused a new problem of harmful algal blooms called Sargassum golden tide,but also increased the difficulty of monitoring and controlling the green tide.Therefore,effective measures should be taken to monitor,prevent and control floating algae such as Ulva prolifera and Sargassum.Remote sensing technology has an advantage in providing synoptic and periodical information on green tides such as spatial distribution,area,and drift trajectories,and plays an important role in the detection,dynamic tracking,and post-disaster assessment of bloom events.In this study,a simple and effective Green Tide Index based on so-called Tasselled Cap Transformation method,named as TCT-GTI,is designed along with the band characteristics of the GOCI sensor and a Sargassum Index based on Tasselled Cap Transformation method?TCT-SI?algorithm was proposed based on domestic satellite GF-1.What's more,the Sargassum and Ulva prolifera Index?SUI?was proposed based on the high spatial resolution-Landsat 8 satellite for the mixing area of Ulva prolifera and Sargassum,which is expected to carry out real-time monitoring of Ulva prolifera and Sargassum in the Yellow Sea and Bohai sea of China.In addition,the effects of Marine meteorological factors including wind field on macroalgae were also analyzed.The main research conclusions are as follows:?1?In this study,a simple and effective Green Tide Index based on so-called Tasselled Cap Transformation method,named as TCT-GTI,is designed.By judging the green tide extraction results,and then comparing with the two existing remote sensing algorithms?AFAI and IGAG algorithms?,the TCT-GTI algorithm showed relatively high accuracy and credibility.The TCI-GTI algorithm has been applied to extract the green tide information in the Yellow Sea by using the multi-view GOCI images during the whole 2017 year.We have analyzed the daily characteristics of the coverage areas,and studied the drift trajectories of the bloom events in 2017.The obtained results showed that the green tide coverage reached the maximum at 12:30,which might be affected by photosynthesis and other factors.The Ulva prolifera mat experienced a drift trajectory from northwest to northeast,drifting from the offshore waters along City Yancheng in Jiangsu Province,to the northwest of the south Yellow Sea.Then,it continued to move northeastward,and reached the south bank of the Shandong Peninsula.?2?By analyzing the spectral characteristics of Rayleigh corrected reflectance(R_rc)of Sargassum and seawater in satellite images and extracting the differences of their spectral characteristics,an algorithm of Sargassum Index from TCT?TCT-GTI?was proposed.The results showed that the Sargassum remote sensing monitoring results based on the TCT-GTI algorithm have high accuracy and high reliability,while the NDVI algorithm is more susceptible to the impact of the environment,and some water bodies will be misjudged as sargasso.?3?By analyzing the R_rc spectral characteristics of Sargassum,Ulva prolifera and seawater in satellite images,it is found that the spectral characteristics of seawater,Sargassum,and Ulva prolifera are significantly different.In visible light band,the reflectivity of seawater is generally lower than that of Sargassum and Ulva prolifera.The spectrum of Ulva prolifera is between 510-580nm,forming a reflection peak band.The reflection peak of Sargassum is between 580-650nm.There are differences between 510-650nm.In the near-infrared band,the reflectances of Ulva prolifera and Sargassum are very high,and the reflectance of seawater is generally low.The near-infrared bands of seawater,Sargassum and Ulva prolifera are most different.Therefore,the proposed SUI index further distinguishes Ulva prolifera and Sargassum.This paper uses two steps.The first step separates seawater from large algae?Ulva prolifera and Sargassum?,and the second step identifies Ulva prolifera and Sargassum.For the second step,a new Sargassum and Ulva prolifera identification algorithm was established.This algorithm has achieved good results in the empirical study of Landsat 8satellite images to identify Sargassum and Ulva prolifera,and it can effectively identify the mixed phenomenon of Sargassum and Ulva prolifera.This paper developed a valid accurate and easy to operate Ulva prolifera identification algorithm,Sargassum recognition algorithm and Ulva prolifera and Sargassum distinguish algorithm.It is conducive to reducing economic losses to the greatest extent,and at the same time providing technical support for monitoring,forecasting,and early warning of disasters of Ulva prolifera and Sargassum.