Study on the Erosion and Deposition Changes of Tidal Flat in Jiangsu Province Using ICESat-2 and Sentinel-2 Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 14; p. 3598
• Main Authors: Wang, Kaizheng, Li, Huan, Zhang, Nan, Zhang, Jiabao, Zhang, Xiaoyan, Gong, Zheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Accuracy; Algorithms; Artificial neural networks; China; Coasts; Comparative analysis; convolutional neural network; Data acquisition; Deep learning; Environmental aspects; Erosion; Forecasts and trends; Geomorphology; ICESat-2 data; Image acquisition; Lasers; Machine learning; Measurement; Multispectral photography; Neural networks; Observations; Remote sensing; Satellite imagery; Satellite imaging; Satellites; Sedimentation and deposition; Sediments; Sentinel-2; Shoreline protection; Soil erosion; Tidal flats; tidal flats flushing and siltation evolution; tidal flats monitoring in Jiangsu Province; Tidal marshes; Tidal waves; Topography; Trends; China; Jiangsu China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15143598

The ecological dynamics of tidal flats are subject to continuous transformations attributed to the interplay of the tidal erosion and sedimentation processes. Accordingly, the realization of a dynamic monitoring system for tidal flats holds significant importance in facilitating the judicious utilization and safeguarding of tidal flats resources. In this study, an integrated methodology was implemented to monitor and assess the tidal flats in Jiangsu province. Specifically, a collection of five multispectral images from the Sentinel-2 satellite was acquired, coupled with data derived from the ICESat-2 satellite. Through the application of advanced techniques, including convolutional neural networks and machine learning algorithms, a comprehensive combined model for inverting tidal flats topography was developed. The accuracy disparity between various model combinations was meticulously evaluated, thereby enabling the acquisition of detailed topography information encompassing the entirety of Jiangsu’s tidal flats for the year 2021. Comparative analysis against measured topographic data demonstrated that the overall accuracy of the combined model inversion surpassed 80%. Additionally, this study conducted an extensive investigation into the temporal evolution of tidal flats in Jiangsu by integrating data from 2008 to 2021, focusing particularly on the measured topography from 2008. Consequently, the study successfully delineated the various types of tidal flats present in Jiangsu and elucidated their corresponding evolutionary trends.