Modeling of Tidal Current
Effects on Oil Spills Movements on Malacca Straits
Maged Marghany Faculty of Science and Environmental Studies Dept. of Environmental Studies Universiti Putra Malaysia, Malasia E-Mail:magedupm@hotmail.com Modeling of oil spills movements by remote sensing is
in early stage of investigation Remote sensing techniques are used as
limited techniques for mapping of oil spill patterns. Up to now scientists
could not use the full capability of remote sensing for modeling and
predicting oil spil movements. Using remote sensing techniques for oil
spill detection just focused on the method detection improvements has done
most of studies. These studies cannot provide any sufficient information
for in identification the effects of physical parameters (wind, current,
waves…. Etc.,) on the oil spill patterns. The most important of remote
sensing techniques is the modeling of oil spill patterns and predict the
oil spill movements automatically through the image. Certain techniques
such as the modeling of physical parameters such as current will be
adequate techniques for predicting the effects of currents on oil spills
transports along the coastal waters. The best sensor could be used in this
certain research is radar data. This is because of the fact data radar
data provide a different signature for oil spills compare to the
surrounding water (Hoveland et al., 1994 and Jojhannessen et al.,
1994) The modeling of current effects on oil spills, in particular, is of critical importance in wide view of emergency response activities after a major oil spill. This information, along with additional information on local environmental conditions after a model output, can be used to devise protection responses and cleanup strategies. Knowing the extent and trajectory of an oil spill can increase the efficiency of the emergency response effort (Stringer, 1992) Shattri and Tec (1998) tried to model oil spills movement by using the combination of wind and current effects. They used the following formula, Oil slick movement = 0.033 * Wind speed + 0.56* Current speed. However, the constant values 0.033 and 0.56 should not be used. This is because that these values got from another study under a different conditions for wind and current . In additon, Shattri and Tee (1998) should solve this equation and a get a different constant values by using regression statistical model. Furthermore, shattri and Tee (1998) could not be identified the oil spill patterns. Finally, Shattri and Tee (1998) should model current speed and wind direction from the optical remotely sensed data were used to apply the previous equation. Recently, Maged and Ibrahim (1999) modeled the effects of tidal current movement on oil spill width from Radarsat. They found that the strong tidal current increased the oil spill widths. Maged (1999) studied the ship effects on oil spill spreading. Maged (1999) found that the increases of oil spill lengths are due to the effect of ship wakes. As the ships created a turbulent area along the oil spill tracks. Maged (1999) concluded that turbulent zone generated by ship wake could be stretched the oil spills which induced a long length. The ship speeds could help to increase the width of oil spills, which occurred, with the rotation of ships to change their directions. This study aims to model the effect of tidal current components on oil spill patterns ( width and length) on Malacca Straits. This is because of the fact that tidal current movements considered significant in narrow waterway such as the Malacca Straits, which is a near 12 hourly sinusoidal phenomenon arising from the semi-diurnal motion of the M2 and S2 tidal components. These tidal currents could make accidental oil spills arrive either at the Malaysian coastline or the island of Sumatra in a matter of a day or two. Methodlogy Study Area The study area is located in the Malacca Straits between 103 16' to 103 48'E and 1 16'N to 2 13' N. According to Wyrtki 91961) the water movements are in general direction towards the northwest direction and are strongly related to the surface gradient of the sea level. Furthermore, Wyrki (1961) stated that the period of strongest flow is from January to April, during the northeast monsoon with current velocity of 0.95 m/s. Oil spills Detection The detection of oil spills depends on the shape, size gradient and texture. In this study, textures analysis will be used for oil spill detection. Texture analysis exploits the fractal behavior of the natural surfaces. This is because of the fact that both the sea surface and its backscattered signal can be modeled as fractals. According to Benelli and Garzelli (1999) the fractal dimension could be estimated from power spectra. This means that fractal dimentsion can be characterized by a random-phase Fourier description in the form of power density. The power density can given by P(W1,W2) = 1/[ÖW12 + W22]b (1) Where W is the frequency domain of the Fourier Transform and b = 2H+2 as H is Hurst or persistence parameter, controlling the roughness of the surface. H is I corresponding to smooth surface and H is zero which corresponding to a very rough texture. The fractal dimension D and persistence parameter are related by D=3-H (2) Thus D can be computed by applying a linear regression on log [P(W1,W2)] us. Log [ÖW12, ÖW22]. Furthermore, H can be computed from the linear regression of ratios of powers. Oil Width Detection There were several steps used to detect oil spills through Radarsat image, which was taken on 26 of October 1997. digital image processing steps used to oil spills which included texture analysis and speckle filtering. According to Maged et al., (1996) texture statistics such as homogeneity, contrast and entropy with window size of 7x7 pixels and lines gives more detail on Radarsat image. After the texture methods applied the speckles filter such as LEE filter and GAMMA Filter have been applied on the image. LEE filter was used to detect the shape of oil spills. The oil spills width is calculated by using the cnage of DN values along the side of spills. D = DN 1-DNI -1* pixel size (3) (Maged, 1999) where I is row of image matrix and DN is a digital number values in I and I-1. Current Speed Model In order to model the current velocity, the azimuth velocity should be estimated. The azimuth velocity component of moving target is obtained by estimating its displacement vector Dx can be given by Vx = -Dx dx v2 / DflR (4) Where dx is the pixel spacing in azimuth direction and Df is the difference between the look center frequencies of two successive images and lis the wavelength and R is the distance antenna and the target (Martin, 1997). The current speed dectected from RADARSAT image then related to tidal current modeled by using M2 components fromtidal table (1997) by statistical regression model. Using Lagrangian model does tidal current components simulated from Radarsat image. These tidal current components are divided into their x and y (Figure 1) component as: U = Ui + Vj (5) U and V are determined from surrounding grid nodes by means of Lagrangian interpolation (Hadi et al., 1996). Then these components are used by regression model to investigate their effects on oil spill patterns. Results and Discussion Figure 2a shows that the possible oil spills occurrence along the coastal water of Malacca Straits. The composite image of texture analysis. LEE filter and Gamma filter show that a heavy ship traffics near to Johor Barua which it may be caused oil spills (Figure 2b). These oil spills moved towards the northwest (Figure 2c). The result of LEE filter shows that the oil spills curved along the coastal water of Malacca Strits (Figure 2c). It is interesting to find that the LEE filter is suitable for oil spills detection. This is because of the fact that LEE filter can reduce the noise variance beside multiplicative noise and additive noise. According to Maged et al., (1996) the GAMMA filter could be used to detect the surrounding area while LEE filter could be used to detect the target linearity. It is observed that the linear movement of oil spills could be detected by LEE filter (Figure 2c). Table 1 shows the summaries of fractal dimension results. The sea surface is dominated by steady value of fractal dimension of 2.63 while the oil spills have Table 1: Fractal Dimension Estimation
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