Quantifying delta channel network changes with Landsat time-series data

三角洲 频道(广播) 水文学(农业) 三角洲 海滩形态动力学 环境科学 河岸带 大洪水 土地复垦 沉积物 泥沙输移 地质学 自然地理学 地貌学 地理 生态学 计算机科学 生物 工程类 计算机网络 航空航天工程 考古 栖息地 岩土工程
作者
Chunpeng Chen,Bo Tian,Christian Schwarz,Ce Zhang,Leicheng Guo,Fan Xu,Yunxuan Zhou,Qing He
出处
期刊:Journal of Hydrology [Elsevier BV]
卷期号:600: 126688-126688 被引量:10
标识
DOI:10.1016/j.jhydrol.2021.126688
摘要

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 and 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 delta channel network dataset and can be generalized to remotely sensed water discharge and river depth estimation.
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