Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images

Wiehle, Stefan; Lehner, Susanne

doi:10.1155/2015/450857

Cited by 20 publications

(12 citation statements)

References 14 publications

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“…Compared to our results, Bruno et al 2016 [58] report a higher mapping accuracy (~2 m) for shoreline extraction in the Apulia region (Italy) from X-band SAR images from the COSMO-SkyMed constellation. A recent study [59] that used TerraSAR-X in the German Wadden Sea did not quantify accuracy, but their results seem comparable to ours.…”

Section: Comparison To Extracted Shorelines Of Otherssupporting

confidence: 79%

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

et al. 2017

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This paper presents a semi-automated approach to detecting coastal shoreline change with high spatial-and temporal-resolution using X-band synthetic aperture radar (SAR) data. The method was applied at the Sand Motor, a "mega-scale" beach nourishment project in the Netherlands. Natural processes, like waves, wind, and tides, gradually distribute the highly concentrated sand to adjacent beaches. Currently, various in-situ techniques are used to monitor the Sand Motor on a monthly basis. Meanwhile, the TerraSAR-X satellite collects two high-resolution (3 × 3 m), cloud-penetrating SAR images every 11 days. This study investigates whether shorelines detected in TerraSAR-X imagery are accurate enough to monitor the shoreline dynamics of a project like the Sand Motor. The study proposes and implements a semi-automated workflow to extract shorelines from all 182 available TerraSAR-X images acquired between 2011 and 2014. The shorelines are validated using bi-monthly RTK-GPS topographic surveys and nearby wave and tide measurements. A valid shoreline could be extracted from 54% of the images. The horizontal accuracy of these shorelines is approximately 50 m, which is sufficient to assess the larger scale shoreline dynamics of the Sand Motor. The accuracy is affected strongly by sea state and partly by acquisition geometry. We conclude that using frequent, high-resolution TerraSAR-X imagery is a valid option for assessing coastal dynamics on the order of tens of meters at approximately monthly intervals.

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Section: Comparison To Extracted Shorelines Of Otherssupporting

confidence: 79%

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

et al. 2017

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“…A requirement for the successful derivation of the wavelength is that the subscene contains several visible wave structures. Subscenes on land or mostly on land, which is decided by a static landmask or an automatically generated landmask for the respective scene [26], are excluded from the further analysis.…”

Section: Determination Of the Wavelengthmentioning

confidence: 99%

Automatic bathymetry retrieval from SAR images

Wiehle¹,

Pleskachevsky²,

Gebhardt³

2019

CEAS Space J

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Bathymetry, the topography of the sea floor, is in high demand due to the increase in offshore constructions like wind parks. It is also an important dataset for climate change modelling, when sea level rises and changes in circulation currents are to be simulated. The retrieval of accurate bathymetry data is a cost-intensive task usually requiring a survey vessel charting the respective area. However, bathymetry can also be retrieved remotely using data from Earth observation satellites. The main point of this study is the development of a processor that allows the automatic derivation of gridded bathymetry information from spaceborne Synthetic Aperture Radar (SAR) data. Observations of sea state modifications in SAR images are used to derive the bathymetry in shelf areas using the shoaling effect, which causes wavelengths to become shorter when reaching shallower waters. The water depth is derived using the dispersion relation for surface water waves, which requires wavelength and wave period as input parameters. While the wavelength can be directly retrieved from the SAR image, for the peak period additional information and procedures are required, e.g. local measurements or complex SAR data. A method for automatically deriving the wave period for swell waves in SAR images was developed and tested in this paper. It uses depth data from public databases as initial values which are compared to derived depths iterating through possible peak periods along the calculation grid; the peak period resulting in a minimal root-mean-square deviation is then used for bathymetry calculation. The bathymetry derived from a TerraSAR-X acquisition of the Channel Islands is presented; the resulting peak wave period of 11.3 s fits well to nearby in situ measurement data.

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“…where S(k x ,k y ) results from FFT analysis of a landmasked, normalised and pre-filtered 2.5 km × 2.5 km subscene. The land masking is connected to the algorithm for coastal line estimation from the same scene (Wiehle, and Lehner, 2015). The threshold for switching a subscene between active/inactive (land) is 20% of the land pixels in a subscene (Rikka, et al 2018b.…”

Section: Parameter Used For Tuning the Model Functionmentioning

confidence: 99%

Estimation of sea state from Sentinel-1 Synthetic aperture radar imagery for maritime situation awareness

Pleskachevsky

Jacobsen

Tings

et al. 2019

International Journal of Remote Sensing

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A new empirical algorithm CWAVE_S1-IW for estimation of significant wave height H s including swell and wind sea from C-band satelliteborne Synthetic Aperture Radar (SAR) data has been developed for Sentinel-1 (S1) Interferometric Wide Swatrh Mode (IW) imagery. The algorithm was implemented into the Sea Sate Processor (SSP) for fully automatic processing for near real time (NRT) services and allow the estimation of wave fields of thousands of kilometres in the flight direction and 250 km swath from S1 IW scenes consisting of a sequence of individual images.The priority of CWAVE_S1-IW development was an automatic, fast and robust raster processing independent of wave patterns, applicable even when only clutter is visible in the SAR images. The algorithm is based on the spectral analysis of subscenes in wavenumber space. The empirical function allows direct H s estimation from image spectra without first converting them into wave spectra and uses integrated image spectra parameters as well as estimated local wind information. A texture analysis based on Grey Level Co-occurrence Matrices (GLCM) is also applied. In this way, also the parameters of short waves can be estimated, which are not visible in S1 IW images and are only represented by clutter.The algorithm was tuned worldwide using in-situ collocated measurements of 92 buoys with more than 2500 acquisitions. The validated SSP allows automatic processing of worldwide S1 IW images in VV or HH polarization, including Atlantic storms, cyclones, and huge storms in the Gulf of Alaska with a root-mean-square (RMSE) error of 80 cm for H s . For the closed seas like the North Sea, Baltic Seas and Black Sea the accuracy is higher with an RMSE = 55 cm. The algorithm is integrated into a demonstration service, used for further validation at the DLR ground station in Neustrelitz. The NRT processing has been tested by supporting a research ship cruise in the Antarctic Sea. ARTICLE HISTORY

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Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images

Cited by 20 publications

References 14 publications

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

Automatic bathymetry retrieval from SAR images

Estimation of sea state from Sentinel-1 Synthetic aperture radar imagery for maritime situation awareness

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