Quantifying delta channel network changes with Landsat time-series data

Chen, Chunpeng and Tian, Bo and Schwarz, Christian and Zhang, Ce and Guo, Leicheng and Xu, Fan and Zhou, Yunxuan and He, Qing (2021) Quantifying delta channel network changes with Landsat time-series data. Journal of Hydrology, 600: 126688. ISSN 0022-1694

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Abstract

Delta channel networks (DCNs) are highly complex and dynamic systems that are governed by natural and anthropogenic perturbations. Challenges remain in quickly quantifying the length, width, migration, and pattern changes of deltaic channels accurately and with a high frequency. Here, we develop a quantitative framework, which introduces a water occurrence algorithm based on Landsat time-series data and spatial morphological delineation methods, in order to measure DCN structures and associated changes. In examining the Pearl River Delta (PRD) and Irrawaddy River Delta (IRD) as case studies, we analyze their conditions and trends between 1986–2018 at ten-year intervals. Both study areas have undergone various human interventions, including dam construction, sand mining, and land use change driven by urbanization. Our results show the following: (1) the use of a 0.5 water occurrence extraction based on Landsat time-series data, morphological delineation, and spatial change analysis methods can quantify the morphodynamics of DCNs effectively with a root-mean-square error of 15.1 m; (2) there was no evident channel migration in either PRD or IRD with average channel widths of 387.6 and 300.9 m, respectively. Most channels in the PRD underwent remarkable shrinkage, with average rates of 0.4–6.4 m/year, while there were only slight changes in the IRD, which is consistent with observed trends in sediment load variation. The results of this research have the potential to contribute to sustainable river management in terms of flood prevention, riparian tideland reclamation, and water and sediment regulation. Moreover, the proposed framework can be used to develop a new global river width dataset and can be generalized to remotely sensed water discharge and river depth estimation.