Quantifying delta channel network changes with Landsat time-series data

Chen, Chunpeng; Tian, Bo; Schwarz, Christian; Zhang, Ce; Guo, Leicheng; Xu, Fan; Zhou, Yunxuan; He, Qing

doi:10.1016/j.jhydrol.2021.126688

Cited by 8 publications

(2 citation statements)

References 68 publications

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“…The main halophytic vegetation species here are Scirpus mariqueter, Phragmites australis, and Spartina alterniflora (Zhao et al, 2019). The morphometry of local TCNs is analyzed by using 2 satellite images acquired in 2003 (IKONOS) and 2011 (Formosat 2) (Chen et al, 2021). Both IKONOS and Formosat 2 are multi-spectral sensors, and their spatial resolutions are 1 and 2 m, respectively.…”

Section: Study Cases and Geomorphological Settingsmentioning

confidence: 99%

Seaward expansion of salt marshes maintains morphological self-similarity of tidal channel networks

Yang

Finotello²,

Goodwin³

et al. 2022

Journal of Hydrology

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Section: Study Cases and Geomorphological Settingsmentioning

confidence: 99%

Seaward expansion of salt marshes maintains morphological self-similarity of tidal channel networks

Yang

Finotello²,

Goodwin³

et al. 2022

Journal of Hydrology

Self Cite

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“…This study divided the entire study period (1990-2020) into three episodes following previous studies [36,37], namely 1990-2000, 2000-2010, and 2010-2020. This study also focused on the entire period 1990-2020.…”

Section: Quantification Of Accretion Erosion and Unchanged Areas Duri...mentioning

confidence: 99%

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

et al. 2022

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The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.

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Enhanced river regulation following dam reoperation: Impacts on flow regime, sediment transport, and planform adjustment in a plateau river basin in the lower Ganga plains

Ghorai,

Ghosh

2024

CATENA

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Quantifying delta channel network changes with Landsat time-series data

Cited by 8 publications

References 68 publications

Seaward expansion of salt marshes maintains morphological self-similarity of tidal channel networks

Seaward expansion of salt marshes maintains morphological self-similarity of tidal channel networks

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

Enhanced river regulation following dam reoperation: Impacts on flow regime, sediment transport, and planform adjustment in a plateau river basin in the lower Ganga plains

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