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Offshore and onshore wave spectra along chendering coastline

Maged Mahmoud Marghany and Shattri B. Mansor
Faculty of Engineering, Universiti Putra Malaysia
43400 Serdasng, Selangor, Malaysia
E-mail: [email protected]

Abstract
Chendering coastline is exposed to damage attributed to erosion. This paper presents work done on utilizing radar data to study the wave spectra along the Chendering coastline. ERS-1 data was used to extract information on wave spectra and wind. The mathematical model was developed to estimate wave spectra from ERS-1 data. The ERS-1 wave spectra was used to model shoreline changes by investigating the volume transport. This helps to predict the erosion and accretion area. The mathematical model was applied to estimate ground wave spectra and sediment transport along the shoreline of chendering. The changes of the shoreline pattern was also identified by using an aerial photo of 1980 and 1994. The results detected from an aerial photo, ERS-1 and ground truth observations were related to mathematical model of wave spectra to detect the wave spectra effects on shoreline change.

The results shows the offshore wave spectra has longer wavelength than onshore wave spectra. The offshore wavelengths were found to be in the range between 525 m to 200 m while the onshore wavelength ranged between 150 m to 25m. Most of onshore wave spectra moved from the north-east to south-east especially during September and October 1993. A comparison the ERS-1 wind wave spectra energy with ground wind wave spectra energy was fond to be in a good correlation. The mathematical model of wave spectra and shoreline change showed that erosion rate increases on Chendering area as compared to other area. This reflected the natural processes of wave induced longshore current and sediment transport. The rate of change recorded on the study area was 4 m/yr.

Introduction
One transport application of ERS-1 ocean imagery is the extraction of wave spectra. The wave spectra can be extracted from ERS-1 which plays an important role in monitoring shoreline change. This is due to the fact that wave spectra is the main source for energy input in coastal zone. The variation of this energy induces a longshore drift a long the shoreline. The longshore drift resulting from waves can cause beach erosion or sedimentation (Komar, 1976). The coastline of Terengganu was reported to experience server erosion. But the erosion is limited to only certain sections. Among the areas reported to experience erosion are the areas near the mouth of the Terenggan estuary, Setiu estuary and Chendering (Loukman et al. 1995).

The widely accept hypothesis carried by researcher is that the erosion along Terenggganu’s coastline is mainly due to the large wave during the north-east monsoon. However, Komar (1976) stated that the erosion can not be studied during one season but it should be estimated by the net of the volume transport among the seasons.

Ibrahim and Sumsudin (1995, 1996) reported that wave spectra direction on the coastal water of Terengganu is dominated by north-east direction with the maximum wavelength of 300 m. However, they did not estimate the wave power spectra which contributes to the erosion or sedimentation. Other works have been done by the Raj (1982, 1985); Stanley Consultant et al., (1985) and Mazlan et al. (1989) to monitor the shoreline change on the coastal water of Kuala Terengganu comparing sequential aerial photographs, older maps and Landsat imagery. Furthermore, Mazlan et al. (1989) reported that the shoreline of Kuala Terengganu retread by 30 m/yr. However, it could be discounted. This is because of the fact that roads and other constructions could be nonexistent. But, all roads in study area till this moment are existent. This could be attributed to the difficulties to rectify both aerial photos and images.

On this basis, this study attempts to model the interaction of ERS-1 wave spectra with the shoreline change on the coastal water of Chendering.

Methodology

Study Area
The study area is located in the South China sea between 5° 2’N to 5° 20’N and 103° 10’E to 104° 55’E. According to Rosnan (1987); Maged (1994) this area lies in an equatorial region dominated by two monsoon seasons. The south-west monsoon lasts from May to September while the north-east monsoon lasts from October to March. The monsoon winds affect the direction and magnitude of the waves. According to Wong (1981) and Maged and Ibrahim (1996), strong waves are prevalent during the north-east monsoon when the prevailing wave direction is from the north from December to February, while during the south-east monsoon (May to September), the wave direction from the south. The rate of longshore drift based on wave effects is about 40,000 to 50,000 cubic meters per year (Stanley Consultants et al. 1985).

Ground Data
The wave data were collected by ship in the area bounded by 0-10 ° N and 100-110° E and were obtained from the marine Advisory Service of the Meteorological Office in London, United Kingdom. The ground truth data such as wind and wave situation (wave height and wave direction) were obtained from Malaysia Meteorological Service, Kuala Terengganu.

The Estimation of Wave Spectra
The ERS-1 satellite data were processed using PCI EASI/PACE image processing system. Digital image processing steps includes Fast Fourier Transform for ocean wave spectra generation.

In this study, a single SAR image frame comprising of 512 x 512 image pixels was extracted. Since each pixel represents a 12.5 m x 12.5 m area, the entire image frame corresponds to 6.4 km x 6.4 km patch on he ocean surface. This frame size provides a sufficiently large are that at least 10 cycles of very long surface waves, up to 640 m in length, can be included in a single image frame. They are also small enough that the ocean can be reasonably assumed homogenous within a frame ( Beal et al 1983).

The mean image intensity is subtracted from the image frame and divided in to the results. This image of fractional modulation is then Fourier transmitted and squared to produce image intensity variance spectrum as function of azimuth and wavelength.

Mathematical model of Wave spectra estimations
We considered the effects of the wind speed U on the azimuth cut-off wavelength. In this case, it can be used the following formula to estimate the wind speed ;

L= 0.53 (R/V) U (m) (1)

Where L is wavelength which extracted from ERS-1 . U is wind speed in meter and R/V is the scene range to platform velocity ratio. For the ERS-1, R/V is approximately 115 s ( Vachnon et al , 1993) .

Most important of wavelength and wind speed are to estimate the energy of wind wave spectral. This can be easily done by using the empirical formula of Pierson and Moskowitz ( 1964), the energy of wind wave spectra could be estimated from

E= 3.78 x 10-5 U 4 (2)

Equation (2) compares density energy of wave spectra extracted from ERS-1 and ground truth wind data.

Mathematical Model For Wave Transformation And Sediment Transport
Both of ERS-1 wave spectra and ground truth data were used to calculate the volume change rate of sediment transport. This has been done by using ACESS 1.07 software.

Method to Detect Shoreline Change
Shoreline changed was delineated by overlaying the aerial-photograph onto the topographic map.

Results and discussion

Wave Spectra Extracted from ERS-1
The power spectral images are divided into offshore wave spectra and onshore wave spectra (Fig. 1). It can be seen that the offshore wave spectra has longer wavelength than onshore wave spectra. The offshore wavelength were found to be in the range between 525 m to 200 m while the onshore wavelength ranged between 150 m to 25 m. This is due to the dissipative of wave energy from offshore to onshore. The offshore wave spectra moved from the south-east to north-east during August 1993. Most of the onshore wave spectra changed its direction near the onshore zone. This is due to the fact that wave undergone refraction due to the bottom topography effects on wave motions. Furthermore, waves tend to coverage around the headland of Chendering and diverge closed to the Marang river mouth.

Figure (2) shows the maximum wind wave energy spectra of ERS-1 coincides with the maximum wind ground truth wave spectra. The maximum ERS-1 wind wave spectra energy and ground truth wind wave data are in September 1993. The minimum wind wave spectra energy is in October 1993. This shows the correlation between gound truth data and ERS-1 data is good.

Shoreline Change
Shoreline Change Based on Volume Sediment Transport

Figure (3a) shows the shoreline change eroded by -58m/month during the north-east monsoon. During the south-east monsoon (May to August) the accretion occurred with maximum value of 53 m/yr. This is because of the fact that the highest wave energy input in the shoreline during north-east monsoon (Loukman et al. 1995). During the transition period April, September and October the erosion occurred with maximum value of 10 m/month. This means a weak erosion occurred in transition period. This is due to a weak south-east monsoon wind still prevail and the wind in the process of changing its pattern from south-west to north-east direction (Maged, 1994). Therefore, the wind in these months will tend to be weak. This could be induced a low wave energy. This could be seen in the month of October (1993) as the wave energy spectra was lower than August 1993 and September (1993).

The rate of change of the shoreline extracted from ERS-1 satellite date and ground truth data is shown in Figure 3b. Figure 3b shows the accretion on August 1993 with 6.7 m/month. The erosion occurred in September 1993 and October 1993 with rate change of -11 m/month and -8 m/month, respectively. The ground truth data has a good correlation with ERS-1 data (Fig.3b).

Shoreline Change estimation from Aerial photo
The extent of shoreline changes can be identified from aerial photo and topographic map. The results of shoreline changes can be divided into two period : 1959 to 1980 and 1959-1994.

Figure 4 shows that the rate of shoreline change in Chendering increased since 1980 to 1994. The rate of shoreline change during 1952 to 1980 was 1.5 m/yr. While during 1952-1994 this rate increased to 4 m/yr. The highest rate of shoreline change observed in Chendering as compared to other area. It can be suggested that the location of the breaker water trapped all the sediment moved from the north to south during the north-east monsoon. This means that there is no sediment transport across the basin from the tip of the breakwater to the shore south of the basin. This could be induced accretion on the north of the headland and erosion in Chendering (Stanley Consultants et al. 1985).

Conclusion
It has been shown that ERS-1 can be used to model the interaction of wave spectra with the shoreline change on the coastal water of Chendering. This can be done with the assistance of other sources of data such as ground truth data and aerial photo.

In conclusion, it can be observed that shoreline change is based on the wave energy and the artificial construction such breakwater. This is due to the fact that heights wave energy can be induced erosion. While the lowest wave energy will caused accretion. The breakwater can change the direction of littoral drift. This will caused erosion at the down of littoral drift and accretion on the upper drift. This is true if the breakwater face the direction of littoral drift.

Acknowledgements

The authors would like to thank UTM Center for Remote Sensing for the data and the assistance.

References

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Figure 1. Wave spectra in the Month of August 1993.


Figure 2. A composition between ERS-1 Wind Wave Spectra Energy and Truth Wind Wave Energy during August 1993, September 1993 and October 1993, Respectively.


Figure 3. Shoreline Change Based on the Change Rate of Sediment Transport (a) Ship Observation and (b) ERS-1 Data and Truth Data

Figure 4. Shoreline Change during (a) 1952-1980 and (b) 1952-1994