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Environment
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Applications of Remote Sensing for Sargassum on Da Ya Bay
Li Tiefang, Yi Jianchun
Center for Remote, Zhongshan University, P.R.C
Liu Huai, Fang Hongda
Shouth China Sea Marine Environmental Monitoring Center, SOA
Dong Yuguo
Guangzhou Institute of New Techniques in Geologym
Academic Sinica,Chen Xuelian
Scientific Research Institute , Pearl River Water Resources
Commission, P.R.C.
Abstract
There is a lot of sargassum growing in the sea area of DaYa Bay. Sargassumk is a type of big algae with body length about 1~2 meters and the longest about 5~6 meters. The sargassum begins to grow in fall of every year, and breaks in April of May of the next year and floats away with sea currents. It is the major clogged material to the cooling system pipe of DaYa Bay Nuclear Power Station. To provide the design of the filter lack and drum with reliable data, it's necessary to investigate its culture regularity, distribution, output and floating quantity toward cooling system pipe on the Bay. As the Bay areas are very wide, it's quite difficult to determine the distributional range and the total production on the basis of conventional oceanic research, but remote sensing investigation is a very effective method. The paper discusses the methods of using LANDSAT TM data with the aid of on-the-spot research to recognize the sargassum distributional range and estimate its output.
The acquisition of remotely sensed sargassum information
- The Sargassum Distributional Characteristics
Sargassum is an algae of fixing life, grows on the low water line down to gravel 5 meters deep under water, and distributes in the states of piece or bar. The water body is usually cleaner when sargassum grows.
- Sargassum Wavebands Characteristics
In the spectrum ranges of 0.4 ~ 0.7 um, the sargassum has its own obvious absorption and reflection bands, which are important marks of recognizing sargassum. The average brightness curves on TM images are shown as Fig. 1, which shows that TM1 wavebands have deeper
Penetration and more information contents than that of TM2 wavebands. Although the noise on TM1 image caused by atmosphere is much greater than that on TM2, the noise only affects the shift of the brightness distributional curve, not on its form and its message recognization.
- The Acquisition of Sargassum Messages
Based on the sargassum distributional characteristics and waveband features, TM1 images in two periods, one of which is before sargassum grows (October), the other , after the sargassum grows up (January), are chosen to be processed differentially. The image brightness on gravel beach, on which there is no sargassum growing , is the highest. Otherwise, its image brightness will be much lower on the on the TM image when sargassum lives. The difference of brightness between two periods is very small in the area where no sargassum grows . As a matter of fact, the greater difference will occur only in the case of the current in various current speeds in this area from the above analysis, the sargassum massage could be picked up. The red algae and sea current have the same differential value of spectrum. Several differential values of typical bottom materials, current and algaes are shown below:
| Type |
Muddy bed material |
Sandy bed Material |
Current |
Sand beach |
Sargassum |
Red algae |
| |
material |
Material |
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Beach |
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| D.V. |
0~5 |
0~3 |
>10 |
0~3 |
8~10 |
8~10 |
| D.V --- Differential Values of Brghtness
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It can be seen from the above table that most of the no-sargassum messages could be removed after the different processing. The possible confusing messages will occur in red algae and currents. But when the states of differential spectrum, original spectrum of sargassum, red algae, current, terrain and topography are applied to the message processing , they could be obviously told apart. As red algae is a type of small algae growing on the water bed of mud with lower current speed, its imagery brightness value is much darker than that of sargassum living on the gravel beach. Basides , the current speed in the seas of red algae growing is slower than of that of sargassum growing , and its imagery texture smoother and colourgrade, darker. So, the sargassum distributional range can be decided and different growing densities can be distinguished. The comprehensive analysis model is given below:
Us(s) = Utm1(D)AUg(G)AUtm1(TM)AUt(T) ----------------(1)
Where:
Us(S) -- The sargassum discriminant function.
Utm1(D) - The differential value of images before and after sargassum growing.
Ug(G) -- The membership function of terrain and topography (gravel beach) for sargassum living.
Utm1(tm1)-The brightness function of TM1 image (original spectrum).
Ut(T) -- Characteristic function of imagery texture for sargassum growing (consist of the textures of piece or bar spottedly).
A --" AND" operator, means the artificial intelligence processing of compuper recognization for differential value processing.
The sargassum distributional range and growing densities determined by the above methods are as Fig 2.
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