Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images

Abstract

The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring.

Monitoring Sand Spit Variability Using Sentinel-2 and Google Earth Engine in a Mediterranean Estuary

“…Estuaries and coastal lagoons are vulnerable environments affected by atrophic impacts and climate change; therefore, monitoring efforts are crucial for their conservation. By applying remote sensing techniques, consistent and robust methodologies for sea-land mapping and coastline detection were recently obtained on a global scale [22,25,29,48,60,61]. However, heterogeneity of sea-land mapping and coastline delineation methods and the difficult access to these tools by coastal managers usually restricts their use [24,35,36,40,62,63].…”

“…However, heterogeneity of sea-land mapping and coastline delineation methods and the difficult access to these tools by coastal managers usually restricts their use [24,35,36,40,62,63]. The potential of GEE has already been demonstrated in previous studies [37,38,60,64,65], and we used S2 images within GEE in order to ensure accuracy and robust performance, as well as a user-friendly and accessible tool for coastal managers.…”

A GIS Procedure to Assess Shoreline Changes over Time Using Multi-temporal Maps: An Analysis of a Sandy Shoreline in Southern Italy over the Last 100 Years

“…In this environment, it is possible to calculate the distance between the furthest shoreline from the baseline and the nearest shoreline for each transect as well as to make an assessment of the annual rate of change or determine other statistical parametres useful for its analysis. Several tools and algorithms, such as the Simple Change Analysis of Retreating and Prograding Systems (SCARPS) [21], BeachTools [22], Digital Shoreline Analysis System (DSAS) [23,24] and Analyzing Moving Boundaries Using R (AMBUR) [25] were developed in GIS environment.…”

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

“…It provides a variety of packages for image collection, analysis, processing, classification and export to ensure that users are no longer solely dependent on expensive commercial software [55][56][57]. Several researches use GEE to take advantage of its massive data catalogue to examine dynamic processes over long timeseries data and to generate large-scale thematic classifications for a variety of applications, including LU/LC mapping [58], cropland classifications [59], forest habitats mapping [60], surface water detection [61], urban and rural settlement [62], mine mapping [63], natural hazard mapping and snow and shoreline detection [64,65]. However, its application in geological mapping remains very limited [54].…”