Surface sediment classification using a deep learning model and unmanned aerial vehicle data of tidal flats.

This study proposes a deep learning model, U-Net, to improve surface sediment classification using high-resolution unmanned aerial vehicle (UAV) images. We constructed training datasets with UAV images and corresponding labeling data acquired from three field surveys on the Hwangdo tidal flat. The labeling data indicated the distribution of surface sediment types.

Depositional environment classification based on environmental variables of South Korea's Nakdong River Estuary barrier-lagoon system.

Anthropogenic activities have altered the geomorphological and ecological conditions of the Nakdong River Estuary (NRE) dramatically over the last century. The objectives of this study were to classify NRE sub-environments and to identify their unique ecological functions. The first step in classification was the establishment of 14 a priori sub-environmental groups based on landscape factors.

Removal of non-aqueous phase liquids (NAPLs) from TPH-saturated sandy aquifer sediments using in situ air sparging combined with soil vapor extraction.

Contamination in coastal aquifer plains is of great concern in many countries given that non-aqueous phase liquids (NAPLs) have polluted numerous sites through accidental oil spills or improper disposal. We have developed a method to remove pollutants such as NAPLs from sandy sediment samples collected from the Mandol area of Gomso Bay in western South Korea. The sediments were collected from around the diffuser in a two-dimensional (2D) acrylic reaction apparatus, and these contained a total petroleum hydrocarbon (TPH) concentration of 89.

Flux and distribution of methane (CH) in the Gunsan Basin of the southeastern Yellow Sea, off the Western Korea.

The flux and distribution of methane (CH) was investigated in the seawater column at 14 stations in the Gunsan Basin, the southeastern part of Yellow Sea from 2013 to 2015. Here CH is concentrated 2.4-4.

The radius of influence of a combined method of in situ air sparging and soil vapor extraction in the intertidal sediments of Gomso Bay on the west coast of South Korea.

Background: In situ air sparging (IAS) was undertaken at sites in the tidal flats of Mandol and Hajeon, on the west coast of South Korea, to estimate variations in the radius of influence (ROI).

Results: The Mandol core sample consisted of sand and muddy sand 1.6-3.

Satellite-based observations of unexpected coastal changes due to the Saemangeum Dyke construction, Korea.

Spatial and temporal changes around an area of conventional coastal engineering can be easily observed from field surveys because of the clear cause-and-effect observable in the before and after stages of the project. However, it is more difficult to determine environmental changes in the vicinity of tidal flats and coastal areas that are a considerable distance from the project. To identify any unexpected environmental impacts of the construction of Saemangeum Dyke in the area, we examined morphological changes identified by satellite-based observations through a field survey on Gomso Bay tidal flats (15km from Saemangeum Dyke), and changes in the suspended sediment distribution identified by satellite-based observations through a hydrodynamic analysis in the Saemangeum and Gomso coastal area.

Decalcification of benthic foraminifera due to "Hebei Spirit" oil spill, Korea.

In order to determine the effects on foraminifera due to spilled crude oil in the "Herbei Spirit" incident, a study of benthic foraminiferal assemblages was carried out on sediment samples collected from the Sogeunri tidal flat, Taean Peninsula, Korea. Breakages of the chambers in the Ammonia beccarii and Elphidium subincertum species of the Sogeunri tidal flat with a low pH (6.98 on average) were marked.

Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model.