Automatic Shoreline Detection from Video Images by Combining Information from Different Methods

Gonzalo Simarro,Jaime Arriaga,Pau Luque,Francesca Ribas

doi:10.3390/rs12223717

Abstract

Properly registering the time evolution of the shoreline—the coastal land-water interface—is a crucial issue in coastal management, among other disciplines. Video stations have shown to be powerful low-cost tools for continuous monitoring of the coast in the last 30 years. Despite the efforts of the scientific community to get algorithms able to properly track the shoreline position from video images without human supervision, there is not yet an algorithm that can be recognized as fully satisfactory. The present work introduces a methodology to combine the results from different shoreline detection algorithms so as to obtain a smooth and very much improved result when compared to the actual shoreline. The output of the introduced methodology, which is fully automatic, includes not only the shorelines at all available times but also a measure of the quality of the obtained shoreline at each point (called self-computed error). The results from the studied beaches—located in the region of Barcelona city (Spanish Mediterranean coast)—show that such self-computed errors are in general good proxies of the actual errors. Using a certain threshold for the self-computed errors, the final computed shorelines have RMSE (Root Mean Squared Errors) that are in general smaller than 2.5 m in the great majority of analysed images, when compared to the manually digitized shorelines by three expert users. The global RMSE for all dates and beaches is of 1.8 m, with a mean bias <1 m and percentage of retrieval success >95% of the points.

Highlights

A considerable proportion of the world’s sedimentary coasts are facing a general erosional trend due to, e.g., a decrease of fluvial sand supply, and this tendency will be strongly accentuated by sea level rise due to global warming [1]
The present study focuses on images form video monitoring stations, but the methodology could be applicable to satellite images as well
The aim of the present study is to introduce a fully-automated methodology that combines a few simple shoreline detection methods in order to obtain an accurate and robust shoreline position, giving more weight to those that are working better in a given circumstance

Summary

Introduction

A considerable proportion of the world’s sedimentary coasts are facing a general erosional trend due to, e.g., a decrease of fluvial sand supply, and this tendency will be strongly accentuated by sea level rise due to global warming [1] In this context, monitoring the position of the shoreline—the interface between water and land—of sandy beaches is crucial to manage these dynamic environments of high socio-economic value, as well as to ensure the safety of the many cities, infrastructures and ecosystems located near them. Despite the critical importance of quantifying the shoreline position, there is not yet a satisfactory solution that provides high-resolution accurate data

Objectives

Methods

Results

Discussion

Conclusion