Statistical analysis for tidal flat classification and topography using multitemporal sar backscattering coefficients

Keunyong Kim, Hahn Chul Jung, Jong Kuk Choi, Joo Hyung Ryu

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1 Citation (Scopus)


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.

Original languageEnglish
Article number5169
JournalRemote Sensing
Issue number24
Publication statusPublished - 2021 Dec 1

Bibliographical note

Funding Information:
Funding: This research was supported by the National Research Foundation of Korea (NRFK) and funded by the Korean Government (2021R1A2C100578011). This research was also funded by “Development of technology for constructing biological and environmental spatial information system of tidal flats through machine learning of remotely sensed visual data” from the Korea Institute of Ocean Science and Technology (PE99915).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)


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