Monitoring shallow coastal environment using Landsat/altimetry data under rapid sea-level change

Abstract

The Caspian Sea (CS) is the largest enclosed inland body of water and eminent for its rapid sea-level change. From 1929 to 1955, rapid cyclic changes with amplitudes of 3 m led to the formation of large submerged and emerged areas. The present study seeks to incorporate multi-source sensor data such as the Landsat time-series data (i.e., MSS, TM, ETM+ and OLI) alongside radar altimetry products (i.e., TOPEX, Jason-1, OSTM, Jason-3) as a means for extracting morphological features (Gorgan Bay and Gomishan lagoon) of the southeastern shorelines of the Caspian Sea, as well as to investigate changes in the shoreline for a given period of 42 years (1975-2016). We also employ Particle Swarm Optimization (PSO) algorithm as an automated method to extract shoreline change in shallow marine environments. Over the past century, the CS has experienced a lowstand in 1977 and a highstand in 1995. Despite an approximate 1.5 m drop in sea-level from 1995 to 2015, Gorgan Bay and Gomishan lagoon, with depths of 4.5 and 2.5 m, appear to have outlasted and emerged, respectively. PSO is a highly efficient method capable of defining shorelines and extracting water bodies. The surface area estimations using the PSO method are consistent with corresponding reference values, with an average error of 1.73% and a high coefficient of determination (R2 = 0.99). Differences between calculated and reference areas were mainly observed in muddy and swamp sectors of the study area. This study highlights the key role of satellite time-series in shoreline monitoring and management under rapid sea-level change conditions. Moreover, the study demonstrates the capabilities of the PSO algorithm as an automated and accurate method for shoreline detection.