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Environment
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Applications of Remote Sensing for Sargassum on Da Ya Bay
Sargassum products estimation
- Sargassum Output Estimation Model
The different growing densities of sargassum have difference in brightness and differential value. The higher of the sargassum growing grades are, the darker the brightness, and the larger their differential vlaues. On the other hand, the lower sargassum growing grades are, the brightener the and the smaller the differential value. In order to estimate the sargassum output, the on-the-spot sampling and weighing are carried based upon different densities shown on the image to decide the quantities of the density grades on the image. The sargassum output Gs follows:
Gs = (C1. L1. + C2.L2 + C3.L3) . Pa. gs -----------------------(2)
Where:
C1,C2,C3-- The pixels colour grade (Red, Green and Blue).
L1,L2,L3-- The rate of sargassum length at the sampling area.
pa -- The spatial -resolution of remotely sensed image (m2)
gs -- The average output in unit area at sampling seas.
The above sargassum output estimating model is the accompaniment of remote sensing technique with actual field sampling and surveying . L1, L2, and L3, could be regarded as the weight coefficients on the sargassum desnities Ci, which coame from the remotely sensed images , also as the calibration value. From the equation (2), the sargassum output in all of DaYa Bay is about 1700T
- The Predication of Broken Sargassum Drifting Route.
The sargassum, which breaks in April or May every year, would float away with tidal currents in side the Bay. The drifting route and hold-up time are the key problem for the clogging of cooling system pipe of the power station. The drifting route has much to do with tidal currents and ti usually mapped out thought the analysis of very few points at actual station surveying. Because the ordinary current analysis method couldn't reflect at the current state and it's details inside the Bay length, it's quite difficult to foretell the broken sargassum drifting routes at every mouth. The TM image at tidal flood and ebb are chosen to pick up surface current message to analyze the broken sargessum's floating routes. The trace and state of surface current message to analyze the broken sargassum's floating routes. The trace and state of surface currents, which are shown in great details on remotely sensed images, are the dynamic factors of sargassum drifting. This is because the surface current field (speed and direction) has an association with the brightness and texture on remotely sensed images. [1], [2]
As a matter of fact, the higher the current sped is, the much rougher the surface state and the brighter the imagery brightness. In the contrary, the slower the current speed is, the darker the imagery brightness. Based upon the statistic results of TM imagery at the same tidal and meteorological condition with the surveying of current speed and direction on-the-sopt, the corrlelation coefficient of pixle's brightness with the current speed is about 0.7 ~ 0.8. Also, where water flows, the surface roughness, suspended sediment, water colour and so on, would be the marks of current trace shown on the image , and the state of the interaction of different water bodies or curren5 system, such as circumfluent, eddy, shearing, and so on, would be depicted on TM imagery explicitly. As a result, the surface current field of flood and ebb current inside the Bay could be mapped out on Fig. 3. By the analysis of sargassum floating direction and holdup condition at every bay mouth, the sargassum, which is dangerous for the cooling system pipe of the power station , is estimated at about 85% of the total output on DaYa Bay. They are mainly from the west of Central Islets.
Fig. 3 Current field map of Da Ya Bay's tidal flood and ebb current
Results and Discussion
So far, remote sensing for sargassum has been applied only in 2 or 3 countries in the world. The
Characteristics of the method discussed in the paper are as following:
- The scientific combination of oceanic remote sensing and biological remote sensing with under-water topography.
- The sargassum output estimating model established by the proper combination of remote sensing method with the ordinary oceanographic research.
- The recognization and analysis combining water surface remote sensing massage with under water remote sensing information.
Thanks to the combination of multi-massages with different processing methods, the remote sensing of sargassum on DaYa Bay has good results and it would provide the engineering design of the filter lock and drum with reliable data.
Acknowledgments
The original TM images were provided by the Remote Sensing Satellite Ground Station of Chinese Academy of Science. This research was also partly supported by Mr. Ou Huamin, Mr. Lin Zuheng, Ms. Li Jianrong, Mr. Jiang Yuejin, of South China Sea Environmental Monitoring Center of SOA and Mr. Li Yinxi, Mr. Ma Yachuan, of Remote Sensing Satellite Ground Station of Chinese Academy of science.
References
- Robert H. Stewart, Methods of Satellite Oceanography, Published under the auspices of the California, p108-113.
- A.P. Crecknel, Remote Sensing In Meteorology, Oceanography and Hydrology, first published by Ellis Horwoods Limited in 1981, p 178-204.
- Ifrmer, seaweeds Northe Brittary Harvesting Area, France Department Environment Littoral Service Applications data Teledetection 1987, p1-6.
- Pan Youlian, Chlorophy1 and Primary Productivity, Marine Science, No.1, 1987 (In Chinese).
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