Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

Abstract

Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, river morphology, flood-wave delineation and flood assessment. Optical-based monitoring and the characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-based unsupervised algorithm for the intra-event monitoring of river dynamics during extreme flow conditions based on the time series of Sentinel-1 SAR data. The method allows the extraction of multi-temporal series of spatially explicit geometric parameters at high temporal and spatial resolutions, linking them to the hydrometric levels acquired by reference gauge stations. The intra-event reconstruction of inundation dynamics has led to (1) the estimation of the relationship between hydrometric level and wet area extension and (2) the assessment of bank erosion phenomena. In the first case, the behavior exhibits a change when the hydrometric level exceeds 1 m. In the second case, the erosion rate and cumulative lateral erosion were evaluated. The maximum erosion velocity was greater than 1 m/h, while the cumulative lateral erosion reached 130 m. Time series of SAR acquisitions, provided by Sentinel-1 satellites, were analyzed to quantify changes in the wet area of a reach of the Tagliamento river under different flow conditions. The algorithm, developed within the Python-API of GEE, can support many types of analyses of river dynamics, including morphological changes, floods monitoring, and bio-physical habitat dynamics. The results encourage future advancements and applications of the algorithm, specifically exploring SAR data from ICEYE and Capella Space constellations, which offer significantly higher spatial and temporal resolutions compared to Sentinel-1 data.

Combining multisensor images and social network data to assess the area flooded by a hurricane event

“…Notwithstanding the flooded areas are now mapped using sophisticated deep learning ( Bai et al., 2021 ; Katiyar, Tamkuan & Nagai, 2021 ) and machine learning algorithms ( Uddin, Matin & Meyer, 2019 ; Soria-Ruiz et al., 2022 ), some researchers acknowledge that the histogram thresholding technique employed in this study is the simplest and most common procedure for floods mapping from SAR data. This technique is a fast, reliable, and computationally less time-consuming method ( Liang & Liu, 2020 ; Levin & Phinn, 2022 ; Rossi et al., 2023 ). However, our SAR-based flood map generated using this technique, achieved an overall accuracy of 23%, as validated through social network downloaded photos.…”

Characterization of Active Riverbed Spatiotemporal Dynamics through the Definition of a Framework for Remote Sensing Procedures

“…For example, intra-seasonal vegetation dynamics can be investigated at weekly or monthly resolution, covering the temporal extent of the vegetative season, with Sentinel-2 or higherresolution imagery. At an even finer temporal resolution, single images could be used to compute water coverage, coupled with water level or discharge measured on the same day and time of image acquisition [57]. It should be noted that, in this work, the water mask is defined with a fixed threshold of the 90th percentile synthetic MNDWI index, for comparison purposes only.…”