Statistical Analysis for Tidal Flat Classification and Topography Using Multitemporal SAR Backscattering Coefficients

Abstract

Coastal zones are very dynamic natural systems that experience short-term and long-term morphological changes. Their highly dynamic behavior requires frequent monitoring. Tidal flat topography for a large spatial coverage has been generated mainly by the waterline extraction method from multitemporal remote sensing observations. Despite the efficiency and robustness of the waterline extraction method, the waterline-based digital elevation model (DEM) is limited to representing small scale topographic features, such as localized tidal tributaries. Tidal flats show a rapid increase in SAR backscattering coefficients when the tide height is lower than the tidal flat topography compared to when the tidal flat is covered by water. This leads to a tidal flat with a distinct statistical behavior on the temporal variability of our multitemporal SAR backscattering coefficients. Therefore, this study aims to suggest a new method that can overcome the constraints of the waterline-based method by using a pixel-based DEM generation algorithm. Jenks Natural Break (JNB) optimization was applied to distinguish the tidal flat from land and ocean using multitemporal Senitnel-1 SAR data for the years 2014–2020. We also implemented a logistic model to characterize the temporal evolution of the SAR backscattering coefficients along with the tide heights and estimated intertidal topography. The Sentinel-1 DEM from the JNB classification and logistic function was evaluated by an airborne Lidar DEM. Our pixel-based DEM outperformed the waterline-based Landsat DEM. This study demonstrates that our statistical approach to intertidal classification and topography serves to monitor the near real-time spatiotemporal distribution changes of tidal flats through continuous and stable SAR data collection on local and regional scales.

Roles of Exposure Time and Geochemical Factors in the Characteristics of the Surface Sediments of the Hwangdo Tidal Flat, Taean, Cheonsu Bay, West Coast of Korea

“…By contrast at the H40, H27, and H28 stations, high tidal-channel-index values indicative of a dendritic pattern of tidal-channel distribution were identified in areas of high elevation and fine-grained sediments, as determined using remotely sensed data such as those from the KOMPSAT-2, Landsat Enhanced Thematic Mapper Plus (ETM+), and aerial photographs [41]. A previous study [42] confirmed our choice of a statistical approach to intertidal classification and topography to monitor the near-real-time spatiotemporal distribution changes in the tidal flats using continuous and stable Synthetic Aperture Radar (SAR) data obtained at local and regional scales. The dataset generated in this study can be used for field verification.…”

Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index

“…Using SAR backscatter coefficient features alone is no longer sufficient to obtain high-precision LCC [56]. In this study, a new radar vegetation index based on the multicomponent DpRVI m is proposed.…”