Detecting Sand Waves through Remote Sensing

Abstract

Sand waves can have a significant impact on offshore activities, and with the global push for sustainability and more renewable energy sources, these negative impacts will become more prevalent. Sand waves in the North Sea have a height up to a couple of meters and a wavelength between 100 and 1000 meters. The migration or change in asymmetry of sand waves can cause free spans in export cables leading to buckling or vibration causing failure. The migration can also cause cables to be exposed on the sea bed and in danger from offshore activities like anchors and fishing boats.

This research focuses on utilizing satellite data to determine sand wave characteristics. Satellites like Sentinel-1 and Sentinel-2 which are used in this research have global availability of data over multiple years at a 10 meter resolution. Sentinel-1 has a Synthetic Aperture Radar (SAR) which creates images by sending out microwave signals and recording the strength and the time delay of the returning signals. Sentinel-2 has an optical instrument which creates images by recording the reflected light from the sun on the earth's surface. Sand waves were detected through the change in Sea Surface Roughness due to the current interaction with the sea bed as described in the Alpers-Hennings Model. The difference in resulting sand wave characteristics between SAR and optical images were determined.

Three initial areas of interest off the coast of the Netherlands were chosen based on their different characteristics. Hoek van Holland contains short irregular sand waves. Holland Kust Zuid contains long regular sand waves. The third location is Alkmaar, which contains no sand waves and was used to determine the results of the methodology when a location has no sand waves present. The first step was to determine the environmental conditions necessary for sand waves to be visible in satellite images. Image collections for an area of interest over a set period of time were filtered based on the environmental parameters. These parameters include mean glint angle and cloud cover for optical images, and wind and current speed for both optical and SAR images. Threshold values were determined through literature. The wind speed should be between 3 and 12 m/s, the current speed should be greater than 0.4 m/s, and the mean glint angle for the latitude of the North Sea is less than 56 degrees. The image collection for the year 2021 for the three different areas of interest was filtered based on these parameters. The final collection after filtering was then manually checked for the visibility of sand waves. This number was lower than the initial filtered collection which means that there are additional factors that need to be accounted for.

A methodology was created to determine the sand wave characteristics over an area by applying a Fast Fourier Transformation (FFT) and calculating the sand wavelength and wave angle from the resulting signal. This was first applied to the three different areas of interest within the North Sea, Hoek van Holland, Holland Kust Zuid, and Alkmaar. Using the resulting image collection at these locations the sand wave characteristics were calculated. Although there are no sand waves at Alkmaar, due to the methodology, sand wave characteristics are still calculated. Also, the methodology works better at Holland Kust Zuid with long regular sand waves compared to at Hoek van Holland where sand waves are shorter and irregular.

To determine at which scale the satellite images should be viewed at to obtain information on the sand wave characteristics, the data was split into different area sizes and a FFT performed. The resulting average values and spread of the sand wavelength and wave angle were compared at different area sizes for optical and SAR images, as well as Multi-Beam Sonar (MBES). This resulted in an area of 5 by 5 kilometers which allows for the correct signals to be read across different possible sand wavelengths. Also, the effect of different sources of noise on the FFT and calculation of the sand wave characteristics for optical and SAR images was determined. From this it was seen that ships within a satellite image containing sand waves introduce a very strong signal within the FFT. This prevents the sand wave characteristics from being calculated correctly. Wind farms within a satellite image affect SAR images, and not optical images. Suspended sediment transport and clouds, which only occur within optical images, have different effects. Optical images are not affected by sediment transport because the current direction will always be perpendicular to sand wave crests and therefore does not influence the calculation of sand wave characteristics. Clouds, similarly to ships, introduce a strong signal within the FFT which blocks the signal of the sand waves. Dark patches (like rain cells), which effect SAR images, block the visibility of sand waves within satellite images and therefore there no signal corresponding to sand waves within the FFT can be found.

Then, the area of interest was increased to the entire southern North Sea. Satellite images over the North Sea were downloaded in 5 by 5 km tiles for a single satellite pass over per year where the environmental parameters met the conditions necessary to view sand waves. As for the local scale cases, a FFT was applied to each tile and the sand wave characteristics were calculated including sand wavelength, wave angle, density, and spatial frequency. The differences in characteristics were compared from year to year. Typically, the difference is less than 200 meters in sand wavelength, however there are points where the difference is much larger. This is due to noise affecting the calculated value. Additionally, the data for wind speed and current speed for the date and time of the satellite image was determined. From this it is seen that optical images can contain sand waves at lower wind and current speeds, at the lower end of the boundary conditions. Sand waves are only visible in SAR images when the current speed is very high. Also, the results were compared to the water depth of the North Sea. At depths less than 40 meters which occurs on the Dutch Continental Shelf sand waves are almost always visible with the correct environmental parameters. At depths greater than 40 meters, which occurs on the West side of the sand wave field, sand waves are not visible consistently. The calculated sand wavelength over the North Sea was also compared to wavelength obtained from MBES. This resulted in low correlation values for both optical and SAR. As a result, noise present in satellite images should be taken into account.

By both applying the methodology to smaller scale cases and to the entire North Sea, it is possible to detect sand waves in different environments. Using the change in sea surface roughness due to sand waves it is possible to see sand waves in both optical and SAR satellite images. By applying the Fourier transformation the average sand wave characteristics over an area can be calculated. The methodology is limited by environmental parameters and noise that can be present in the satellite images. SAR and optical images require specific wind and current conditions, while optical images also requires specific mean glint angles and a low cloud cover. Different sources of noise also have a negative impact, adding signals to the FFT that do not correspond to the sand waves. Although the methodology proposed in this thesis is successful in determining the average sand wave characteristics over an area, more research for this topic would increase the possibilities. This includes using additional conditions for filtering image collections for sand waves, removing sources of noise, utilizing higher resolution satellite data, and testing the methodology on different sand wave fields that have different characteristics, such as symmetry. There are still many possibilities that can be explored in using satellite images to determine sand wave characteristics.