Quantification of sedimentation in the Yellow River estuary based on GIS remote sensing technology

Yixuan Duan

doi:10.1117/12.3010750

15 November 2023 Quantification of sedimentation in the Yellow River estuary based on GIS remote sensing technology

Yixuan Duan

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128150A (2023) https://doi.org/10.1117/12.3010750
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

In recent years, the ecological and environmental problems of the Yellow River Basin have gradually become our concern, and sedimentation at the mouth of the Yellow River can directly demonstrate the treating effect of the Yellow River. However, the research and monitoring of the Yellow River environment based on GIS technology is not in-depth enough, and only a few studies have a very effective interpretation of the future changes in the estuary of the Yellow River. Therefore, this study explores the inter-annual variation of sediment sedimentation in the estuary of the Yellow River by collecting Landsat satellite data and using QGIS software for data analysis, feature classification and inter-annual change. The linear fitting model of MATLAB software shows that the total sediment sedimentary area of the mouth of the Yellow River showed a downward trend in the decade from 2013 to 2022, which is closely related to natural factors such as ocean currents and waves and fortifications built by man. Therefore, the environment of the Yellow River has changed well in the past decade, and the governance will continue to strengthen on this basis.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yixuan Duan "Quantification of sedimentation in the Yellow River estuary based on GIS remote sensing technology", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128150A (15 November 2023); https://doi.org/10.1117/12.3010750

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KEYWORDS

Landsat

Remote sensing

Geographic information systems

Earth observing sensors

Data modeling

Environmental monitoring

Image classification

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