<abstract xmlns="http://www.w3.org/1999/xhtml">

Turbidity is an important indicator of water quality in rivers, lakes, and coastal areas. Research on turbidity issues in these areas is significant not only for the development and utilization of water resources for aquaculture, tourism, and other purposes but also for assessing the level of silt (sand) in the river, allowing sediment alluvial to build up a bank of the river, and monitoring the degree of water corrosion in the bridge substructure. This allows for the building of an effective maintenance and conservation program for the bridge in response to climate change.
Traditional methods have defined the turbidity of water in a local area, on a small scale. Interpolation errors of traditional methods for large areas may exceed over 20%. The use of remote sensing technology as Landsat-8 satellite images with a high geometric resolution of 30-meter multispectral channels allows us to estimate and distribute the water turbidity in a 30 × 30 m grid in detail.
Using multi-temporal Landsat-8 data in 2014 and 2015 for modeling water turbidity of Tien and Hau rivers and coastal areas in South Vietnam, the obtained mean absolute error is approximately 20%, the Root Mean Square Error (RMSE) does not exceed 10 NTU. The models have a high coefficient of efficiency ME, approximately 90% (ME = 0.862), and the correlation coefficient R stronger than 90%. This allows an overall assessment of changes in water flow velocity concerning the amount of sediment in the river.
</abstract>

Turbidity is an important indicator of water quality in rivers, lakes, and coastal areas. Research on turbidity issues in these areas is significant not only for the development and utilization of water resources for aquaculture, tourism, and other purposes but also for assessing the level of silt (sand) in the river, allowing sediment alluvial to build up a bank of the river, and monitoring the degree of water corrosion in the bridge substructure. This allows for the building of an effective maintenance and conservation program for the bridge in response to climate change.
Traditional methods have defined the turbidity of water in a local area, on a small scale. Interpolation errors of traditional methods for large areas may exceed over 20%. The use of remote sensing technology as Landsat-8 satellite images with a high geometric resolution of 30-meter multispectral channels allows us to estimate and distribute the water turbidity in a 30 × 30 m grid in detail.
Using multi-temporal Landsat-8 data in 2014 and 2015 for modeling water turbidity of Tien and Hau rivers and coastal areas in South Vietnam, the obtained mean absolute error is approximately 20%, the Root Mean Square Error (RMSE) does not exceed 10 NTU. The models have a high coefficient of efficiency ME, approximately 90% (ME = 0.862), and the correlation coefficient R stronger than 90%. This allows an overall assessment of changes in water flow velocity concerning the amount of sediment in the river.

Using Satellite Images to Retrieve the River Turbidity and Water Flow Velocity for Monitoring their Influences on Bridge

Structure and Environment

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Turbidity is an important indicator of water quality in rivers, lakes, and coastal areas. Research on turbidity issues in these areas is significant not only...

Using Satellite Images to Retrieve the River Turbidity and Water Flow Velocity for Monitoring their Influences on Bridge

Publié en ligne: 26 juil. 2023

Pages: 63 - 72

DOI: https://doi.org/10.30540/sae-2023-006

Mots clés
turbidity monitoring, river banks erosion, bridge erosion, bridge maintenance, remote sensing

© 2023 Luong Minh Chinh, published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Using Satellite Images to Retrieve the River Turbidity and Water Flow Velocity for Monitoring their Influences on Bridge

Publié en ligne: 26 juil. 2023

Pages: 63 - 72

DOI: https://doi.org/10.30540/sae-2023-006

Mots clésturbidity monitoring, river banks erosion, bridge erosion, bridge maintenance, remote sensing

© 2023 Luong Minh Chinh, published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Mots clés
turbidity monitoring, river banks erosion, bridge erosion, bridge maintenance, remote sensing