Monitoring shallow coastal environment using Landsat/altimetry data under rapid sea-level change. (31st August 2019)

• Record Type: Journal Article
• Authors: Jeihouni, Mehrdad; Kakroodi, A.A.; Hamzeh, Saeid
• Is Part Of: Estuarine, coastal and shelf science. Volume 224(2019)
• Page Start: 260
• Page End: 271
• Publication Date: 2019-08-31
• Subjects: Caspian sea -- Lake altimetry -- Shoreline detection -- Sea-level fluctuations -- PSO; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2019.05.005
• Languages: English
• ISSNs: 0272-7714

Abstract

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 (R 2 = 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. Graphical abstract: Image 1 Highlights: PSO could be used for automatic shoreline detection. Rapid coastal evolution observed under rapid sea-level change. River input is crucial to lagoon survival. Gomishan Lagoon emerged entirely (2.5 m depth, ∼1.5 m sea level fall). Combined altimetry and satellite data enabled prediction of lagoon drying point.