An Analysis on Relative Importance and Priority of Hydrographic Survey for Major Ports in South Korea

Sang-Goo, Kim; Lee, Won-IL; Woo, Yang-Ho

doi:10.7837/kosomes.2015.21.2.154

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enhance the accuracy of water reflectance results, sun-glint correction was applied 37 . This correction typically involves establishing a linear relationship between the NIR band and other bands, followed by adjustments for outlier pixels 38 , 39 . This procedure was conducted using the Sentinel Application Platform offered by the European Space Agency (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…37 This correction typically involves establishing a linear relationship between the NIR band and other bands, followed by adjustments for outlier pixels. 38,39 Table 4 The amplitude of observed values from the three regions was compared with the amplitude of values from the NAO99.Jb model. This procedure was conducted using the Sentinel Application Platform offered by the European Space Agency (Fig.…”

Section: Sun-glint Correctionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

Kwon,

Shin,

Kim

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Bathymetry, the measurement of sea depth, has conventionally been conducted using echo-sounders on vessels. However, various factors limit conventional shipborne surveys in coastal regions, including data continuity, geographic obstacles, diplomatic concerns, and marine infrastructures. Remote sensing technology can address these limitations, particularly with the advancement of satellite imaging technology. Indeed, many studies are underway to develop machine learning-based water depth estimation technologies. However, previous studies have focused on clear waters with low turbidity or uniform seabed sediment. Therefore, in this study, we developed a satellite-derived bathymetry (SDB) model using the random forest machine learning algorithm, which was applied to three coastal areas around the Korean Peninsula with distinct characteristics: clear waters (Samcheok), high turbidity (Cheonsuman), and varied seabed sediments (Hallim). We then compared the accuracy of the bathymetric mapping data derived in these three areas. The estimated depth values exhibited the highest accuracy in Samcheok, followed by Hallim and Cheonsuman. Based on Worldview-3 images and on-site surveys, we confirmed the presence of basalt on the seabed. However, the remote reflectance was attenuated due to the effect of the black rock, leading to an overestimation of the depth. In the future, additional satellite images will be applied as training data for the machine learning model to advance the SDB technology using turbidity and seabed sediment distribution data for each area. Ultimately, the SDB results will be applied as depth monitoring data to facilitate safe ship passage in coastal areas, including ports that require periodic and consistent coastal bathymetry. In addition, they can be applied as input data for numerical ocean models, contributing to various fields.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Sun-glint Correctionmentioning

confidence: 99%

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

Kwon,

Shin,

Kim

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

Research on the hydrographic survey cycle for updating navigational charts

2021

View full text Add to dashboard Cite

Due to the vast ocean area and limited human and material resources, hydrographic survey must be carried out in a selective and well-planned way. Therefore, scientific planning of hydrographic surveys to ensure the effectiveness of navigational charts has become an urgent issue to be addressed by the hydrographic office of each coastal state. In this study, a reasonable calculation model of hydrographic survey cycle is established, which can be used to make the plan of navigational chart updating. The paper takes 493 navigational charts of Chinese coastal ports and fairways as the research object, analyses the fundamental factors affecting the hydrographic survey cycle and gives them weights, proposes to use the BP neural network to construct the relationship between the cycle and the impact factors, and finally establishes a calculation model of the hydrographic survey cycle. It has been verified that the calculation cycle of the model is effective, and it can provide reference for hydrographic survey planning and chart updating, as well as suggestions for navigation safety.

show abstract

An Analysis on Relative Importance and Priority of Hydrographic Survey for Major Ports in South Korea

Cited by 2 publications

References 3 publications

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

Research on the hydrographic survey cycle for updating navigational charts

Contact Info

Product

Resources

About