A Wavelet based Algorithm to estimate Ocean Wave Group Parameters from Radar Images

In recent years new remote sensing techniques have been developed to measure two-dimensional (2D) sea surface elevation fields. The availability of these data has led to the necessity to extend the classical analysis methods for one-dimensional (1D) buoy time series to two dimensions. This paper is concerned with the derivation of group parameters from 2D sea surface elevation fields using a wavelet based technique. Wave grouping is known to be an important factor in ship and offshore safety as it plays a role in dangerous resonance phenomenons and the generation of extreme waves. Synthetic aperture radar (SAR) data are used for the analysis. The wavelet technique is introduced using synthetic ocean surfaces and simulated SAR data. It is shown that the group structure of the ocean wave field can be recovered from the SAR image if the nonlinear imaging effects are moderate. The method is applied to a global data set of European Remote Sensing satellite (ERS-2) wave mode data. Different group parameters including the area covered by the largest group and the number of groups in a given area are calculated for over 33,000 SAR images. Global maps of the parameters are presented. For comparison classical 1D grouping parameters are calculated from colocated wave model data showing good overall agreement with the wavelet derived parameters. ERS-2 image mode data are used to study wave fields in coastal areas. Waves approaching the island of Sylt in the North Sea are investigated, showing the potential of the wavelet technique to analyze the spatial wave dynamics associated with the bottom topography. Observations concerning changes of wavelength and group parameters are compared to linear wave theory

Ocean surface wave measurements using SAR wave mode data

Over the ocean, the SAR and ASAR instruments onboard ESA’s ERS and ENVISAT satellites are operated in wave mode whenever no other operation is requested. In wave mode, SAR collects data to form small images of 10 km x 5 km size every 200 or 100 km along the satellite’s orbit. Ocean wave parameters can be retrieved from these SAR/ASAR wave mode data over the global ocean with high quality. The wave parameters can be used for validation of numerical wave model forecasts and hindcasts, assimilation of models, observations and forecast of extreme ocean weather, as well as for global wave climate analysis. The main focus of the thesis is ocean wave information retrieval from SAR and ASAR wave mode data. This includes validation of published schemes for retrieving two-dimensional ocean wave spectra and development of the new empirical algorithm CWAVE_ENV for the retrieval of integral wave parameters directly from ASAR wave mode data without using other input as the first guess. Three months of ASAR wave mode data acquired globally from December 2006 to February 2007 are used to validate the algorithms of the nonlinear PARSA (Partition Rescaling and Shift Algorithm) and the quasi-linear WVW (used by ESA for Level 2 ASAR Wave Mode Wave Spectra) by comparing them to collocated in situ buoy measurements and numerical wave model results. The PARSA algorithm needs the SAR look cross spectra and first guess spectra from numerical wave model as input. The algorithm can yield the full two-dimensional ocean wave spectrum and the retrieved integral wave parameters agree with buoy measurements with a bias of only 0.09 m and a scatter index of 21%. The comparison with the forecast wave model of DWD is even better with a bias of -0.01 m and a scatter index of 16%. The quasi-linear ESA algorithm WVW has the advantage of not needing any priori. However, the retrieved wave spectra are limited to the domain of long wavelengths, mainly swell. Therefore the significant wave height (SWH) integrated from the WVW spectra has a higher bias of -0.19 m and a larger scatter index of 36% when compared to in situ buoy measurements. Furthermore, the underestimation of SWH increases with sea state. Around 25% ASAR wave mode cross spectra cannot be converted successfully by using the algorithm, probably because of the low signal to noise ratio. Based on the empirical algorithm CWAVE_ERS developed for reprocessed ERS-2 SAR wave mode data, the CWAVE_ENV algorithm is proposed in this thesis and implemented for the ASAR wave mode data. Using the same three months ASAR wave mode data and the collocated dataset, the empirical algorithm is validated. Validation, particularly compared to independent datasets, i.e., in situ buoy measurements and radar altimeters, proves that reliable and accurate sea state measurements can be achieved. The bias is only 0.06 m and the scatter index 24%, compared to the buoy measurements over deep water. The respective bias is -0.11 m and -0.13 m and the scatter index 13% and 17% when compared to the crossover measurements of the spaceborne radar altimeters on GFO and JASON, respectively. For a full year dataset, from June 2006 to May 2007, ASAR wave mode data were processed using the CWAVE_ENV algorithm leading to a global sea state analysis. Global 10-year returned extreme SWH is estimated to be 23.4 m using a lognormal probability density function (pdf) as the best fit for high sea state. Seasonal and annual maps for SWH, mean wave period, and wave steepness are compiled. In the winter season, the fetch-limit effects of the North Atlantic lead to high wave build up continuously from west to east, causing the gradual growth of swell. Compared to the results of reanalyzed wave model ERA-40 during 1971 - 2000, the annual mean wave height derived from ASAR wave mode data shows a similar pattern of high waves in the North Pacific, North Atlantic and the Southern Hemisphere. However, in the Northwestern Indian, a much stronger monsoon signal is observed in the ASAR results than the model results. With respect to the mean wave period, extreme swell is observed in the open sea south of Australia, which is around 1 s higher than the model results for the mean value. The SAR wave mode data are useful for global wave studies, while in the coastal regions, SAR data with higher resolution as well as larger coverage are required for investigating spatial changes of sea state. Wave refraction and diffraction around the Terceira island (located in the North Atlantic) is analyzed using the new high resolution TerraSAR-X data. Variations of wave height, peak wavelength and wave direction in the coastal wave processes are identified using the two-dimensional SAR image spectra

Oil spill and ship detection using high resolution polarimetric X-band SAR data

Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability

THE USE OF MARINE RADAR FOR INTERTIDAL AREA SURVEY AND MONITORING COASTAL MORPHOLOGICAL CHANGE

Surveying and monitoring the dynamic morphology of intertidal areas is a logistically challenging and expensive task, due to their large area and complications associated with access. This thesis describes a contribution to the nearshore survey industry; an innovative methodology is developed and subsequently applied to marine radar image data in order to map topography within the intertidal area. This new method of intertidal topographical mapping has a reasonable spatial resolution (5 m) and operates over a large radial range (~4 km) with the required temporal resolution to observe both event-based and long-term morphological change (currently bi-weekly surveys). This study uses nearly three years of radar image data collected during 2006-2009 from an installation on Hilbre Island at the mouth of the Dee estuary, northwest UK. The development of the novel 'radar waterline method' builds on previous waterline techniques and improves upon them by moving the analysis from the spatial to the temporal domain, making the analysis extremely robust and more resilient to poor quality image data. Results from radar topographical surveys are compared to those of a LiDAR survey during October 2006. The differences compare favourably across large areas of the intertidal zone, within the first kilometre 97% of radar-derived elevations lie within 1 m of LiDAR estimations. Concentrations of poor estimations are seen in areas that are shown to be shadowed from the radar antenna or suffering from pooling water during the ebb tide. The full three-year dataset is used to analyse changing intertidal morphology over that time period using radar-derived surveys generated every two weeks. These surveys are used to perform an analysis of changing sediment volume and mean elevation, giving an indication of beach 'health' and revealing a seasonal trend of erosion and accretion at several sites across the Dee estuary. The ability of the developed technique to resolve morphological changes resulting from storm events is demonstrated and a quantification of that impact is provided. The application of the technique to long-range (7.5 km) marine radar data is demonstrated in an attempt to test the spatial and operational limitations of this new method. The development of a mobile radar survey platform, the Rapidar allows remote areas to be surveyed and provides a platform for potential integration with other survey instruments. A description of the potential application to coastal management and monitoring is presented. Areas of further work intended to improve vertical elevation accuracy and robustness are proposed. This contribution provides a useful tool for coastal scientists, engineers and decision-makers interested in the management of coastal areas that will form part of integrated coastal management and monitoring operations. This method presents several key advantages over traditional survey techniques including; the large area of operation and temporal resolution of repeat surveys, it is limited primarily by topographical shadowing and low wind conditions limiting data collection