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Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery
by
Erwin W. J. Bergsma
Erwin W. J. Bergsma 1,*
,
Adrien N. Klotz
Adrien N. Klotz 1,2
,
Stéphanie Artigues
Stéphanie Artigues 1,
Marcan Graffin
Marcan Graffin 1,2,
Anna Prenowitz
Anna Prenowitz 1,
Jean-Marc Delvit
Jean-Marc Delvit 1 and
Rafael Almar
Rafael Almar
Dr. Rafael Almar is a physical oceanographer specializing in coastal bathymetry and the development [...]
Dr. Rafael Almar is a physical oceanographer specializing in coastal bathymetry and the development of remote-sensing applications. He is also an international leader in the coastal oceanography community on topics related to remote sensing, multiscale coastal dynamics, and tropical bands. He received his Ph.D. from the University of Bordeaux in 2009 in the field of nearshore high-frequency hydromorphodynamics, for which he was awarded the best national Ph.D. prize by the Ministry of Higher Education. His research to date has advanced the field of coastal oceanography, including the morphological impact of high-frequency storms. He was recruited by the French Research Institute for Development (IRD) in 2011. He conducts his research activities at the Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS) in Toulouse, France. His research strategy is to develop and apply innovative observational and predictive modeling tools for multi-scale coastal dynamics, from fine process-based studies to regional climate. Beyond academic studies and scientific publications, his research has important societal applications to coastal erosion and flooding, the impact of weather extremes, and climate change.
2
1
CNES (French Space Agency), Earth Observation Lab, 18 Av. Edouard Belin, 31400 Toulouse, France
2
IRD-LEGOS (Research Institute Pour Le Developpement—Laboratoire d’Etudes en Geophysique et Oceanographie Spatiales), UMR-5566, 14 Av. Edouard Belin, 31400 Toulouse, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(15), 2795; https://doi.org/10.3390/rs16152795 (registering DOI)
Submission received: 21 May 2024
/
Revised: 24 July 2024
/
Accepted: 29 July 2024
/
Published: 30 July 2024
Abstract
Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.
Share and Cite
MDPI and ACS Style
Bergsma, E.W.J.; Klotz, A.N.; Artigues, S.; Graffin, M.; Prenowitz, A.; Delvit, J.-M.; Almar, R.
Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery. Remote Sens. 2024, 16, 2795.
https://doi.org/10.3390/rs16152795
AMA Style
Bergsma EWJ, Klotz AN, Artigues S, Graffin M, Prenowitz A, Delvit J-M, Almar R.
Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery. Remote Sensing. 2024; 16(15):2795.
https://doi.org/10.3390/rs16152795
Chicago/Turabian Style
Bergsma, Erwin W. J., Adrien N. Klotz, Stéphanie Artigues, Marcan Graffin, Anna Prenowitz, Jean-Marc Delvit, and Rafael Almar.
2024. "Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery" Remote Sensing 16, no. 15: 2795.
https://doi.org/10.3390/rs16152795
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