Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery

Abstract The ability to repeatedly observe and quantify the changing position of the shoreline is key to present-day coastal management and future coastal planning. This study evaluates the capability of satellite remote sensing to resolve at differing temporal scales the variability and trends in shoreline position along sandy coastlines. Shorelines are extracted from 30 + years of publicly available satellite imagery and compared to long-term in-situ measurements at 5 diverse test sites in Europe, Australia, the USA and New Zealand. These sites span a range of different beach characteristics including wave energy and tide range as well as timescales of observed shoreline variability, from strongly seasonal (e.g., Truc Vert, France), to storm-dominated (e.g., Narrabeen-Collaroy, Australia), to only minor annual to multi-annual signals (e.g., Duck, USA). For the 5 sites, the observed typical horizontal errors varied between a root-mean-squared error (RMSE) of 7.3 m and 12.7 m. An analysis of the typical magnitudes of shoreline variability at temporal scales ranging from a single month up to 10 years indicates that, by the implementation of targeted image pre-processing then the application of a robust sub-pixel shoreline extraction technique, the resulting satellite-derived shorelines are generally able to resolve (signal-to-noise ratio > 1) the observed shoreline variance at timescales of 6 months and longer. The only exception to this is along coastlines where minimal annual to multi-annual shoreline variability occurs (e.g. Duck, USA); at these sites decadal-scale variations are successfully captured. The results of this analysis demonstrate that satellite-derived shorelines spanning the past 30 years as well as into the future can be used to explore and quantify intra- and inter-annual shoreline behaviour at a wide range of beaches around the world. Moreover, it is demonstrated that present-day satellite observations are also capable of capturing event-scale shoreline changes (e.g. individual storms) that occur at timescales shorter than 6 months, where this rapid response exceeds the typical magnitude of shoreline variability. Finally, several practical coastal engineering applications are presented, demonstrating the use of freely-available satellite imagery to monitor inter-annual embayed beach rotation, rapid storm-induced shoreline retreat and a major sand nourishment.

[1]  Jean-Francois Pekel,et al.  Global long-term observations of coastal erosion and accretion , 2018, Scientific Reports.

[2]  Tim Scott,et al.  Extreme wave activity during 2013/2014 winter and morphological impacts along the Atlantic coast of Europe , 2016 .

[3]  M. Crowell,et al.  Long-term Shoreline Position Prediction and Error Propagation , 2000 .

[4]  Luis Ángel Ruiz Fernández,et al.  Evaluating Fourier Cross-Correlation Sub-Pixel Registration in Landsat Images , 2017, Remote. Sens..

[5]  Ad Reniers,et al.  On the accuracy of automated shoreline detection derived from satellite imagery: A case study of the sand motor mega-scale nourishment , 2018 .

[6]  Charitha Pattiaratchi,et al.  Seasonal changes in beach morphology along the sheltered coastline of Perth, Western Australia , 2001 .

[7]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[8]  Vincent Marieu,et al.  Spatial and temporal patterns of shoreline change of a 280-km high-energy disrupted sandy coast from 1950 to 2014: SW France , 2018 .

[9]  Mitchell D. Harley,et al.  Assessment and integration of conventional, RTK-GPS and image-derived beach survey methods for daily , 2011 .

[10]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[11]  Jorge Guillén,et al.  Shoreline dynamics and beach rotation of artificial embayed beaches , 2008 .

[12]  Nan Xu Detecting Coastline Change with All Available Landsat Data over 1986–2015: A Case Study for the State of Texas, USA , 2018 .

[13]  Ian L Turner,et al.  Beach oscillation and rotation: local and regional response at three beaches in southeast Australia , 2014 .

[14]  Nicholas C. Kraus,et al.  Temporal and spatial scales of beach profile change, Duck, North Carolina , 1994 .

[15]  T. Aagaard,et al.  Intertidal beach change during storm conditions; Egmond, The Netherlands , 2005 .

[16]  R. Holman,et al.  The history and technical capabilities of Argus , 2007 .

[17]  Roshanka Ranasinghe,et al.  The Southern Oscillation Index, wave climate, and beach rotation , 2004 .

[18]  Te-Ming Tu,et al.  A new look at IHS-like image fusion methods , 2001, Inf. Fusion.

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

[20]  Mitchell D. Harley,et al.  New insights into embayed beach rotation: The importance of wave exposure and cross‐shore processes , 2015 .

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

[22]  D. Basco,et al.  Managing coastal erosion , 1992 .

[23]  Nathaniel G. Plant,et al.  Practical use of video imagery in nearshore oceanographic field studies , 1997 .

[24]  A. S. Belward,et al.  Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites , 2015 .

[25]  Claudio Delrieux,et al.  Superresolution border segmentation and measurement in remote sensing images , 2012, Comput. Geosci..

[26]  L. Wright,et al.  Morphodynamic variability of surf zones and beaches: A synthesis , 1984 .

[27]  G. Różyński Long-term shoreline response of a nontidal, barred coast , 2005 .

[28]  T.J. Wright,et al.  The role of space-based observation in understanding and responding to active tectonics and earthquakes , 2016, Nature Communications.

[29]  Mitchell D. Harley,et al.  UAVs for coastal surveying , 2016 .

[30]  C. Daly,et al.  Nearshore sandbar rotation at single-barred embayed beaches , 2016 .

[31]  Nathaniel G. Plant,et al.  Intertidal beach slope predictions compared to field data , 2001 .

[32]  R. Ranasinghe,et al.  The State of the World’s Beaches , 2018, Scientific Reports.

[33]  Fevzi Karsli,et al.  Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey , 2011 .

[34]  R. Holman,et al.  Estimation of Shoreline Position and Change using Airborne Topographic Lidar Data , 2002 .

[35]  Peter Reinartz,et al.  Evaluation of Skybox Video and Still Image products , 2014 .

[36]  D. Roelvink,et al.  Video-Based Detection of Shorelines at Complex Meso–Macro Tidal Beaches , 2012 .

[37]  K. O. Emery,et al.  A SIMPLE METHOD OF MEASURING BEACH PROFILES , 1961 .

[38]  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..

[39]  Willis J. Abbot Watching the world go by , 1933 .

[40]  J. Peck,et al.  Long-Term Beach Profile Variations Along the South Shore of Rhode Island, U.S.A. , 1998 .

[41]  Yoshiaki Kuriyama,et al.  Medium-term bar behavior and associated sediment transport at Hasaki, Japan , 2002 .

[42]  Shoreline rotation and response to nourishment of a gravel embayed beach using a low-cost video monitoring technique: San Michele-Sassi Neri, Central Italy , 2014, Journal of Coastal Conservation.

[43]  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..

[44]  Ian L Turner,et al.  The Performance of Shoreline Detection Models Applied to Video Imagery , 2007 .

[45]  Arthur C. Trembanis,et al.  Decadal Scale Patterns in Beach Oscillation and Rotation Narrabeen Beach, Australia—Time Series, PCA and Wavelet Analysis , 2004 .

[46]  Kristen D. Splinter,et al.  Extreme coastal erosion enhanced by anomalous extratropical storm wave direction , 2017, Scientific Reports.

[47]  Sylvain Capo,et al.  Equilibrium shoreline modelling of a high-energy meso-macrotidal multiple-barred beach , 2014 .

[48]  Kristen D. Splinter,et al.  Coastal vulnerability across the Pacific dominated by El Niño-Southern Oscillation , 2015 .

[49]  I. Kanellopoulos,et al.  Strategies and best practice for neural network image classification , 1997 .

[50]  Jesús Palomar-Vázquez,et al.  Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator , 2016 .

[51]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[52]  Kristen D. Splinter,et al.  A multi-decade dataset of monthly beach profile surveys and inshore wave forcing at Narrabeen, Australia , 2016, Scientific Data.

[53]  John Trinder,et al.  Automatic super-resolution shoreline change monitoring using Landsat archival data: a case study at Narrabeen–Collaroy Beach, Australia , 2017 .

[54]  Giovanni Coco,et al.  Observations of shoreline-sandbar coupling on an embayed beach , 2013 .

[55]  Hilary F. Stockdon,et al.  Empirical parameterization of setup, swash, and runup , 2006 .

[56]  R. Holman,et al.  Shoreline variability from days to decades: Results of long‐term video imaging , 2015 .

[57]  B. Castelle,et al.  Morphodynamic response of a meso- to macro-tidal intermediate beach based on a long-term data set , 2009 .

[58]  Kathelijne Mariken Wijnberg,et al.  Extracting decadal morphological behaviour from high-resolution, long-term bathymetric surveys along the Holland coast using eigenfunction analysis , 1995 .

[59]  Peter Ruggiero,et al.  Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño , 2017, Nature Communications.