Monitoring 23-year of shoreline changes of the Zengwun Estuary in Southern Taiwan using time-series Landsat data and edge detection techniques

Abstract

More than 80% of sandy beaches in Taiwan have been experiencing a severe recession, although the sediment discharge of rivers in Taiwan is significantly higher than the world average producing almost 2% of global fluvial sediment discharge. This contradiction is primarily due to the widespread constructions of reservoirs and intensive anthropogenic activities in coastal regions. In addition, coasts are particularly vulnerable to hazards due to climate change, such as sea-level rise, as they are located at the transition zone of terrestrial and marine environments. Along with the fact that Taiwan is an island and is one of the most climate-vulnerable regions globally, coastal management and sustainability are nationally critical topics, especially considering the ongoing reformation and legislation of Taiwan's coastal conservation laws. As stated in the Sustainable Development Goals (SDGs) goal 14, accurate and continuous shoreline positions information is essential for coastal conservation. However, by reviewing previous global studies and projects commissioned by the Taiwanese government aiming at monitoring shoreline changes, they usually exhibit several limitations, such as limited band selections or conservative band ratio-derived water indices, relying on either manual digitization or simple thresholding methods, focusing on either artificial or smoothly shaped coasts, and using images acquired at considerably different tidal height levels. Therefore, in the present study, a subpixel shoreline extraction approach based on a sustainable cross-generation dataset and a robust edge detection algorithm is proposed. This approach is exemplified by the Zengwun River Estuary located in southwestern Taiwan-Taiwan's most critical coastal preservation region. By quantitatively analyzing the resultant time-series shoreline positions from 1999 to 2021, several hotspots of shoreline recession have been identified: an extreme erosional rate up to -69.4 m year^-1 is revealed in the northern sand bank; while the offshore sand bar demonstrates an overall landward retreat rate of -35.4 ± 1.24 m year^-1.

Keywords: Coastal management and governance; Coastline mapping; Integrated coastal zone management (ICZM); Machine learning; Sustainability; Tidal height.