Tidal and seasonal controls on the stratigraphic architecture of blind tidal channel deposits in the fluvial-tidal transition of the macrotidal Sittaung River estuary, Myanmar

Published: 25 October 2021| Version 1 | DOI: 10.17632/6bcdrpv4r6.1
Contributors:
Kyungsik Choi,
,

Description

This directory contains the following drone-derived datasets collected between 2015 and 2017 in the Sittaung River estuary, Myanmar. The dataset indicates that the blind tidal channels in the distal part of the fluvial-tidal transition zone in the Sittaung River estuary experienced rapid infilling over the time period. Georeferenced orthophotomosaics were collected annually and processed to produce digital elevation models (DEMs) using Pix4D. Sediment volume changes between the specific periods were calculated by subtracting the two DEMs corresponding the periods (e.g., 2017_2015_dod_low.tif is the difference between 2017_12_DEM_low.tif and 2015_12_DEM_low.tif. We uploaded tiff files with reduced resolution (10 cm) due to large file size for original tiff files for DEM and DoD analysis. The resolution of raw tiff files are less than 2 cm. Despite the reduced file resolution, there is no significant increase in root mean square errors for DoD analysis. The original tiff files are available on request. We uploaded orthophotomosaics with full resolution. Drone_derived datasets - Figure 15 - A time series of orthophotomosaics, DEMs, and DoD at KY4 in 2015, 2016, 2017

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Steps to reproduce

Drone-based datasets were obtained by repeated UAV surveys on the macrotidal Sittaung River estuary, south-central Myanmar. Drone-captured images were all georeferenced by implementing the RTK-GPS survey. The images were rectified and processed to produce digital elevation models (DEMs) using Pix4D (version 4.0.25). To quantify morphological changes and sedimentation, DEMs were obtained repeatedly on an annual basis over the three years. By subtracting the DEMs between the two periods, deposition or erosion can be quantified. Also, morphological changes such as infilling can be detected by comparing the time-series of the DEMs.

Institutions

Seoul National University

Categories

Sedimentology, Geomorphology, Drone (Aircraft)

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