New Methodology for Shoreline Extraction Using Optical and Radar (SAR) Satellite Imagery

Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due to anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial to preserve these areas for safeguarding not only coastal ecosystems and cultural heritage, but also the population living there. In this context, monitoring coastal areas is essential and geomatics techniques, especially satellite remote sensing imagery, might prove very advantageous. In this paper, a semi-automatic methodology to extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 and optical Sentinel-2 satellite images was developed. An experimental algorithm, called J-Net Dynamic, was tested in two pilot sites. The semi-automatic methodology was validated with GNSS (Global Navigation Satellite System) reference shorelines and demonstrated to be a powerful tool for a robust extraction of the shoreline both from optical and SAR images. The experimental algorithm was able to extract the shoreline closer to the reference with SAR images on the natural beach of Castelldefels and it was demonstrated to be less sensitive to speckle effects than the commonly used Canny Edge Detector. Using the SAR images of the urban beach of Somorrostro, the Canny detector was not able to extract the shoreline, while the new algorithm could do it but with low accuracy because of the noise induced by man-made structures. For further investigation, the Sentinel-2-extracted shorelines were also compared to the ones extracted by a state-of-the-art tool, CoastSat, in the two beaches using both automatic and manual thresholds. The mean errors obtained with J-Net Dynamic were generally higher than the ones from CoastSat using the manual threshold but lower if using the automatic one. The proposed methodology including the J-Net Dynamic algorithm proves to extract the shorelines closer to the reference in most of the cases and offers the great advantage of being able to work with both optical and SAR images. This feature could allow to reduce the time lag between satellite derived shorelines paving the way to an enhanced monitoring and management of coastal areas.

[1]  D. Ierodiaconou,et al.  Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys , 2022, Remote Sensing of Environment.

[2]  D. Buscombe,et al.  The future of coastal monitoring through satellite remote sensing , 2022, Cambridge Prisms: Coastal Futures.

[3]  A. Monsiváis-Huertero,et al.  Sentinel-1 observation for shoreline delineation applied to Mexico’s Coast , 2022, Geocarto International.

[4]  G. Simarro,et al.  Observations of megacusp dynamics and their coupling with crescentic bars at an open, fetch‐limited beach , 2022, Earth Surface Processes and Landforms.

[5]  Suhendra Suhendra,et al.  COASTLINE CHANGE ANALYSIS ON BALI ISLAND USING SENTINEL-1 SATELLITE IMAGERY , 2021, International Journal of Remote Sensing and Earth Sciences (IJReSES).

[6]  P. Hesp,et al.  Assessing Shoreline Change using Historical Aerial and RapidEye Satellite Imagery (Cape Jaffa, South Australia) , 2021, Journal of Coastal Research.

[7]  B. Castelle,et al.  Satellite-derived shoreline detection at a high-energy meso-macrotidal beach , 2021, Geomorphology.

[8]  Luís Pedro Almeida,et al.  Coastal Analyst System from Space Imagery Engine (CASSIE): Shoreline management module , 2021, Environ. Model. Softw..

[9]  Xueyun Wei,et al.  Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model , 2021, Remote. Sens..

[10]  Jaime Almonacid-Caballer,et al.  An efficient protocol for accurate and massive shoreline definition from mid-resolution satellite imagery , 2020 .

[11]  Sudhir Kumar Chaturvedi,et al.  An assessment of oil spill detection using Sentinel 1 SAR-C images , 2020, Journal of Ocean Engineering and Science.

[12]  F. Landerer,et al.  The causes of sea-level rise since 1900 , 2020, Nature.

[13]  Paolo Massimo Buscema,et al.  Shoreline Extraction Based on an Active Connection Matrix (ACM) Image Enhancement Strategy , 2019, Journal of Marine Science and Engineering.

[14]  Kristen D. Splinter,et al.  CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery , 2019, Environ. Model. Softw..

[15]  Kristen D. Splinter,et al.  Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery , 2019, Coastal Engineering.

[16]  G. Pugliano,et al.  Statistical Deviations in Shoreline Detection Obtained with Direct and Remote Observations , 2019, Journal of Marine Science and Engineering.

[17]  Paolo Massimo Buscema,et al.  High Resolution Satellite Images for Instantaneous Shoreline Extraction Using New Enhancement Algorithms , 2019, Geosciences.

[18]  R. Sivaranjani,et al.  Speckle noise removal in SAR images using Multi-Objective PSO (MOPSO) algorithm , 2019, Appl. Soft Comput..

[19]  Franz J. Meyer,et al.  Mapping pan-Arctic landfast sea ice stability using Sentinel-1 interferometry , 2019, The Cryosphere.

[20]  Nicola Casagli,et al.  A Sentinel-1-based clustering analysis for geo-hazards mitigation at regional scale: a case study in Central Italy , 2019, Geomatics, Natural Hazards and Risk.

[21]  Paul D. Wagner,et al.  Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[22]  M. Vousdoukas,et al.  Global long-term observations of coastal erosion and accretion , 2018, Scientific Reports.

[23]  Chao Wang,et al.  Combining a single shot multibox detector with transfer learning for ship detection using sentinel-1 SAR images , 2018, Remote Sensing Letters.

[24]  Joshua T. Kelly,et al.  Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[25]  Alfonso Fernández-Sarría,et al.  Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery , 2018, Remote. Sens..

[26]  Martin Kappas,et al.  Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery , 2017, Sensors.

[27]  Mehrez Zribi,et al.  Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas , 2017, Remote. Sens..

[28]  Alexandre Bouvet,et al.  Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications , 2017 .

[29]  Vishal M. Patel,et al.  SAR Image Despeckling Using a Convolutional Neural Network , 2017, IEEE Signal Processing Letters.

[30]  N. Demir,et al.  EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE , 2016 .

[31]  A. Fisher,et al.  Comparing Landsat water index methods for automated water classification in eastern Australia , 2016 .

[32]  Claudio Parente,et al.  High Resolution Satellite Images to Reconstruct Recent Evolution of Domitian Coastline , 2015 .

[33]  E. Pranzini,et al.  Aspects of coastal erosion and protection in Europe , 2015, Journal of Coastal Conservation.

[34]  Giovanni Coco,et al.  On the use of variance images for runup and shoreline detection , 2015 .

[35]  I. Figueiredo,et al.  AUTOMATIC EXTRACTION OF TIDE-COORDINATED SHORELINE USING OPEN SOURCE SOFTWARE AND LANDSAT IMAGERY , 2015 .

[36]  R. Nicholls,et al.  Future Coastal Population Growth and Exposure to Sea-Level Rise and Coastal Flooding - A Global Assessment , 2015, PloS one.

[37]  Claudio Prati,et al.  Shoreline detection: capability of COSMO-SkyMed and high-resolution multispectral images , 2013 .

[38]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[39]  J. Jimenez,et al.  A Coastal Storms Intensity Scale for the Catalan Sea , 2009 .

[40]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[41]  Ian L Turner,et al.  Shoreline Definition and Detection: A Review , 2005 .

[42]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[43]  E. Nezry,et al.  Structure detection and statistical adaptive speckle filtering in SAR images , 1993 .

[44]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Ferdinando Nunziata,et al.  On the Use of Multipolarization Satellite SAR Data for Coastline Extraction in Harsh Coastal Environments: The Case of Solway Firth , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[46]  Pau Luque,et al.  Automatic Shoreline Detection from Video Images by Combining Information from Different Methods , 2020, Remote. Sens..

[47]  Gerald Forkuor,et al.  Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study , 2020 .

[48]  Rasmus Fensholt,et al.  Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery , 2014 .

[49]  Massimo Buscema,et al.  Images as Active Connection Matrixes: the J-Net System , 2008 .

[50]  R. Nicholls,et al.  A global analysis of human settlement in coastal zones , 2003 .

[51]  Patrick Wambacq,et al.  Speckle filtering of synthetic aperture radar images : a review , 1994 .