4.7 Article

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

Journal

REMOTE SENSING
Volume 13, Issue 19, Pages -

Publisher

MDPI
DOI: 10.3390/rs13193842

Keywords

Google Earth Engine; dense time-series images; edge probability feature; spatial-temporal dynamic coastline monitoring; Malay Islands; reclamation

Funding

  1. CAS Earth Big Data Science Project of China [XDA19060303]
  2. National Science Foundation of China [41671436, 41901354, 41890854]
  3. Innovation Project of Laboratory of Resources and Environmental Information System [O88RAA01YA]

Ask authors/readers for more resources

The research on extracting coastline information using remote sensing big data platforms and dense time-series methods to achieve a unified tide level standard is of great significance for coastal dynamic change monitoring and island reef change monitoring. The results show that this method has achieved good results in large-scale monitoring of coastline changes and analyzing the reasons for the Malay Islands coastline changes.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available