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Dr. Li Dan
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, PR China

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Research Keywords & Expertise

0 Remote Sensing Applications
0 hydraulic engineering
0 Sediment Dynamics
0 Hydraulic analysis and modeling
0 river hydraulics

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Journal article
Published: 06 July 2021 in Remote Sensing
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River discharge and width, as essential hydraulic variables and hydrological data, play a vital role in influencing the water cycle, driving the resulting river topography and supporting ecological functioning. Insights into bankfull river discharge and bankfull width at fine spatial resolutions are essential. In this study, 10-m Sentinel-2 multispectral instrument (MSI) imagery and digital elevation model (DEM) data, as well as in situ discharge and sediment data, are fused to extract bankfull river widths on the upper Yellow River. Using in situ cross-section morphology data and flood frequency estimations to calculate the bankfull discharge of 22 hydrological stations, the one-to-one correspondence relationship between the bankfull discharge data and the image cover data was determined. The machine learning (ML) method is used to extract water bodies from the Sentinel-2 images in the Google Earth Engine (GEE). The mean overall accuracy was above 0.87, and the mean kappa value was above 0.75. The research results show that (1) for rivers with high suspended sediment concentrations, the water quality index (SRMIR-Red) constitutes a higher contribution; the infrared band performs better in areas with greater amounts of vegetation coverage; and for rivers in general, the water indices perform best. (2) The effective river width of the extracted connected rivers is 30 m, which is 3 times the image resolution. The R2, root mean square error (RMSE), and mean bias error (MBE) of the estimated river width values are 0.991, 7.455 m, and −0.232 m, respectively. (3) The average river widths of the single-thread sections show linear increases along the main stream, and the R2 value is 0.801. The river width has a power function relationship with bankfull discharge and the contributing area, i.e., the downstream hydraulic geometry, with R2 values of 0.782 and 0.630, respectively. More importantly, the extracted river widths provide basic data to analyze the spatial distribution of bankfull widths along river networks and other applications in hydrology, fluvial geomorphology, and stream ecology.

ACS Style

Dan Li; Ge Wang; Chao Qin; Baosheng Wu. River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data. Remote Sensing 2021, 13, 2650 .

AMA Style

Dan Li, Ge Wang, Chao Qin, Baosheng Wu. River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data. Remote Sensing. 2021; 13 (14):2650.

Chicago/Turabian Style

Dan Li; Ge Wang; Chao Qin; Baosheng Wu. 2021. "River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data." Remote Sensing 13, no. 14: 2650.

Journal article
Published: 24 August 2020 in Remote Sensing
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Water is essential for the survival of plants, animals, and human beings. It is imperative to effectively manage and protect aquatic resources to sustain life on Earth. Small tributaries are an important water resource originating in mountain areas, they play an important role in river network evolution and water transmission and distribution. Snow and cloud cover cast shadows leading to misclassification in optical remote sensing images, especially in high-mountain regions. In this study, we effectively extract small and open-surface river information in the Upper Yellow River by fusing Sentinel-2 with 10 m resolution optical imagery corresponding to average discharge of the summer flood season and the 90 m digital elevation model (DEM) data. To effectively minimize the impact of the underlying surface, the study area was divided into five sub-regions according to underlying surface, terrain, and altitude features. We minimize the effects of cloud, snow, and shadow cover on the extracted river surface via a modified normalized difference water index (MNDWI), revised normalized difference water index (RNDWI), automated water extraction index (AWEI), and Otsu threshold method. Water index calculations and water element extractions are operated on the Google Earth Engine (GEE) platform. The river network vectors derived from the DEM data are used as constraints to minimize background noise in the extraction results. The accuracy of extracted river widths is assessed using different statistical indicators such as the R-square (R2) value, root mean square error (RMSE), mean bias error (MBE). The results show the integrity of the extracted small river surface by the RNDWI index is optimal. Overall, the statistical evaluation indicates the accuracy of the extracted river widths is satisfactory. The effective river width that can be accurately extracted based on satellite images is three times the image resolution. Sentinel-2 MSI images with a spatial resolution of 10 m are used to find that the rivers over 30 m wide can be connectedly, accurately extracted with the proposed method. Results of this work can enrich the river width database in the northeast Tibetan Plateau and its boundary region. The river width information may provide a foundation for studying the spatiotemporal changes in channel geometry of river systems in high-mountain regions. They can also supplement the necessary characteristic river widths information for the river network in unmanned mountain areas, which is of great significance for the accurate simulation of the runoff process in the hydrological model.

ACS Style

Dan Li; Baosheng Wu; Bowei Chen; Chao Qin; Yanjun Wang; Yi Zhang; Yuan Xue. Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River. Remote Sensing 2020, 12, 2737 .

AMA Style

Dan Li, Baosheng Wu, Bowei Chen, Chao Qin, Yanjun Wang, Yi Zhang, Yuan Xue. Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River. Remote Sensing. 2020; 12 (17):2737.

Chicago/Turabian Style

Dan Li; Baosheng Wu; Bowei Chen; Chao Qin; Yanjun Wang; Yi Zhang; Yuan Xue. 2020. "Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River." Remote Sensing 12, no. 17: 2737.