Remote sensing retrieval of surface suspended sediment concentration in the Yellow River Estuary

2017 ◽  
Vol 27 (6) ◽  
pp. 934-947 ◽  
Author(s):  
Chao Zhan ◽  
Junbao Yu ◽  
Qing Wang ◽  
Yunzhao Li ◽  
Di Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Yellow River ◽  
River Estuary ◽  
Sediment Concentration ◽  
Yellow River Estuary ◽  
The Yellow River

Flow and Sediment Conditions for the Yellow River Estuary Balance

2013 ◽  
Vol 405-408 ◽  
pp. 2226-2230
Author(s):  
Shou Bing Yu ◽  
Kai Rong Wang ◽  
Wan Zhan Wang
Keyword(s):  
Yellow River ◽  
River Estuary ◽  
Sediment Concentration ◽  
Yellow River Estuary ◽  
Water Volume ◽  
The Yellow River ◽  
Sediment Volume ◽  
Elevation Data ◽  
Annual Water ◽  
Sediment Data

Multi-object application and water and sediment regulation of the Xiaolangdi Reservoir since 2001 have changed the flow and sediment conditions entering the Lower Reaches of Yellow River and the Estuary. Field flow and sediment data at Lijin Hydrological Station and river cross section elevation data downstream from Lijin Section during 2001~2010 have shown that the Estuary have been in a state of little scouring. The 2D mathematical model has been used to study the flow and sediment conditions for the Yellow River Estuary balance. The conclusions have arrived at that total annual water volume is 196 × 108 m3, total annual sediment volume is 1.40~1.70×108 t, coarse sediment concentration is 3kg/m3.

scholarly journals GF-1 Satellite Observations of Suspended Sediment Injection of Yellow River Estuary, China

2020 ◽  
Vol 12 (19) ◽  
pp. 3126
Author(s):  
Ru Yao ◽  
LiNa Cai ◽  
JianQiang Liu ◽  
MinRui Zhou
Keyword(s):  
Suspended Sediment ◽  
Yellow River ◽  
River Estuary ◽  
Yellow River Estuary ◽  
Field Of View ◽  
The Yellow River ◽  
Wide Field ◽  
Current Direction ◽  
Wide Field Of View ◽  
Local Current

We analyzed the distribution of suspended sediments concentration (SSC) in the Yellow River Estuary based on data from GaoFen-1 (GF-1), which is a high-resolution satellite carrying a wide field-of-view (WFV) sensor and panchromatic and a multispectral (PMS) sensor on it. A new SSC retrieval model for the wide field-of-view sensor (M-WFV) was established based on the relationship between in-situ SSC and the reflectance in blue and near infrared bands. SSC obtained from 16 WFV1 images were analyzed in the Yellow River Estuary. The results show that (1) SSC in the study area is mainly 100–3500 mg/L, with the highest value being around 4500 mg/L. (2) The details of suspended sediment injection phenomenon were found in the Yellow River Estuary. The SSC distribution in the coastal water has two forms. One is that the high SSC water evenly distributes near the coast and the gradient of the SSC is similar. The other is that the high SSC water concentrates at the right side of the estuary (Laizhou Bay) with a significantly large area. Usually, there is a clear-water notch at the left side of the estuary. (3) Currents clearly influenced the SSC distribution in the Yellow River Estuary. The SSC gradient in the estuary was high against the local current direction. On the contrary, the SSC gradient in the estuary was small towards the local current direction. Eroding the coast and resuspension of the bottom sediments, together with currents, are the major factors influencing the SSC distribution in nearshore water in the Yellow River Estuary.

Study on Dynamic Equilibrium of Sediment Transport by Qingshuigou Course of the Yellow River

2012 ◽  
Vol 212-213 ◽  
pp. 351-357 ◽  
Author(s):  
Shou Bing Yu ◽  
Kai Rong Wang ◽  
Wan Zhan Wang
Keyword(s):  
Sediment Transport ◽  
Dynamic Equilibrium ◽  
Yellow River ◽  
River Estuary ◽  
Longitudinal Section ◽  
Sediment Concentration ◽  
Yellow River Estuary ◽  
Water Volume ◽  
The Yellow River ◽  
Annual Total

The multi-object application of the Xiaolangdi Reservoir and water and sediment regulation have greatly changed flow and sediment conditions emptying into the Yellow River Estuary. By use of flow and sediment field data at the Lijin Hydrological Station and river cross-section elevation data during 2001~2010, the paper has analyzed characteristics of the Yellow River Estuary in terms of incoming flow and sediment conditions, main flume area, average river longitudinal section and river length. The results show that annual total water volume emptying into the Estuary since 2003 has maintained about 200×108 m3, annual total sediment 1.77×108 t, whole annual average sediment concentration 9.3kg/m3, which are stable and beneficial for dynamic equilibrium of sediment transport. Interannual stability of main flume area, average river bed elevation and river length since the Lijin Section during 2007-2010 indicate that current Qingshuigou Course of the Yellow River has achieved dynamic equilibrium of sediment transport.

scholarly journals Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wang Ping ◽  
Jie Fu ◽  
Wenyu Qiao ◽  
Muhammad Yasir ◽  
Sheng Hui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coupling Coefficient ◽  
Yellow River ◽  
River Estuary ◽  
Hyperspectral Data ◽  
Yellow River Estuary ◽  
The Yellow River ◽  
Wetland Classification ◽  
Classification Methods ◽  
Machine Method

There are many wetland resources in the area where the Yellow River enters the sea. This area has good ecological and economic value. Therefore, wetlands are precious resources. The accuracy of traditional wetland classification methods is low (for example, based on the support machine method). In order to explore ways to improve the accuracy of wetland classification, this paper selected the wetland at the mouth of the Yellow River as the study area. And, we used the hyperspectral data of “Zhuhai No. 1” as the research data. Then, we used the logarithmic transformation method to enhance the spectral characteristics of remote-sensing images. Finally, we used Markov random field method (MRF) and support vector machine method (SVM) to finely classify the wetlands in the Yellow River estuary area. We used these experiments to explore wetland classification methods for hyperspectral data. The results showed that the settings of the coupling coefficient and the initial value in the Markov model had a greater impact on the classification results. We found that the best result was when the initial classification number is 50 and the coupling coefficient is 0.5. Compared with the SVM classification method, the overall classification accuracy of our proposed method was improved by 3.9672%, and the Kappa coefficient was improved by 0.042.

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