Donguk Lee

This study investigated the long-term changes in sediment grain size characteristics of tidal flats along the southwestern coast of Korea using datasets collected through the National Marine Ecosystem Survey from 2015 to 2025. The study area consisted of 129 survey lines and 387 monitoring stations distributed across the western and southwestern coastal regions of Korea, including the Gyeonggi-do, Incheon, Chungcheong, Jeonbuk-do, and Jeollanam-do regions. Spatial distributions and temporal variations of mean grain size were analyzed to identify regional sedimentary characteristics and long-term trends. The results showed that relatively coarse sediments were dominant in the Gyeonggi-do, Incheon, and Chungcheong regions, whereas finer sediments were generally distributed in the Jeonbuk-do and Jeollanam-do regions. These spatial differences were interpreted as the combined effects of tidal and wave energy, exposure to the open sea, terrestrial sediment supply, and geomorphological enclosure. Long-term time-series analysis revealed that although sediment grain size variations were observed in some regions and years, no clear nationwide trend of overall coarsening or fining was identified during the study period. In addition, grain-size class analysis indicated that coarse sediments showed relatively large interannual variability, whereas fine sediments exhibited comparatively stable distribution patterns. These findings suggest that tidal flats along the southwestern coast of Korea are characterized not by a simple one-way erosion system, but rather by a dynamic equilibrium environment where resuspension and redeposition continuously occur under tidal and wave influences. This study is meaningful in that it quantitatively evaluated long-term sedimentary environmental changes in Korean tidal flats based on a national-scale monitoring dataset, and the results are expected to provide fundamental information for future tidal flat conservation and coastal management policies.

Understanding suspended sediment transport in macrotidal embayments is crucial for assessing water quality, ecosystem function, and long-term morphological stability. This study provides a high-resolution, localized estimate of suspended sediment flux and examines the empirical relationship between turbidity (NTU, nephelometric turbidity unit) and total suspended matter (TSM, mg·L⁻¹) in the main tidal channel of Gomso Bay, a UNESCO-designated tidal flat on the west coast of Korea. A 13 h high-resolution fixed-point observation was conducted during a semi-diurnal tidal cycle using a multi-instrument platform, including an RCM, CTD profiler, tide gauge, and water sampling for gravimetric TSM analysis. Vertical measurements at the surface, mid, and bottom layers, taken every 15–30 min, revealed a strong linear correlation (R² = 0.94) between turbidity and TSM, empirically validating the use of optical sensors for real-time sediment monitoring under the highly dynamic conditions of Korean west-coast tidal channels. The net suspended sediment transport load was estimated at approximately 5503 kg·m⁻¹, with ebb-dominant residual currents indicating a net seaward sediment flux at the observation site. Residual flows over macrotidal channels are known to vary laterally, with landward fluxes often occurring over shoals. Importantly, the results from this single-station, short-duration observation indicate a predominantly seaward suspended sediment transport during the study period, which should be interpreted as a localized and time-specific estimate rather than a bay-wide characteristic. Nevertheless, these findings provide a baseline for assessing sediment flux and contribute to future applications in digital twin modeling and coastal management. Gomso Bay is part of the UNESCO-designated ‘Getbol, Korean Tidal Flats’, underscoring the global significance of preserving and monitoring this dynamic coastal system.

Optical satellite imagery is severely constrained by cloud contamination, particularly in dynamic coastal environments such as tidal flats, where tidal variations are significant. In this study, we propose a diffusion model-based technique for cloud removal that operates solely on single-point-of-view satellite images without relying on multi-temporal imagery or external sensor data. Specifically, a guided sampling strategy inspired by classifier guidance is employed to leverage gradient information from cloud-free reference regions, enabling image restoration that accurately reflects the underlying topography and temporal characteristics. The proposed method is evaluated against existing inpainting and Repaint approaches under varying cloud cover conditions (10% to 50%). In terms of quantitative metrics (SSIM, PSNR, CC, RMSE), the proposed method demonstrates excellent stability and superior reconstruction quality compared to conventional techniques. Even in cases of extensive cloud coverage, the reconstructed topography structures closely resemble those of the original imagery. Furthermore, the land-water boundary is extracted using NDWI by integrating UAV-derived DEM and tidal data. A comparison between the extracted boundaries and the images reconstructed using the proposed method confirms a high degree of agreement with the actual topography.

The importance of blue carbon is significant in terms of climate change mitigation and marine ecosystem conservation, and halophyte acts as a crucial reservoir for this blue carbon. Accordingly, this study utilized unmanned aerial vehicle (UAV) optical sensors to create a distribution map of vegetation in the natural salt marsh of the Jujin estuary. The optical images captured from a UAV at an altitude of 50 m provide ultra-high-resolution optical information with a ground sampling distance of 0.6 cm. Based on these images, a U-Net model was trained to classify Phragmites communis and Suaeda maritima, generating a classification map of the mixed habitats of salt marsh plants. The areas of Phragmites communis and Suaeda maritima in the Jujin- Cheon region were found to be 6,653.23 m2 and 1,409.08 m2, respectively. The classification results were validated using field control point data, confirming an approximate classification accuracy of 92%.

Joo-Hyung Ryu, Donguk Lee, Minju Kim. Korean J. Remote Sens. -0001;0:. https://doi.org/10.7780/kjrs.2024.40.5.2.4

Recent advances in deep learning (DL) and unmanned aerial vehicle (UAV) technologies have made it possible to monitor salt marshes more efficiently and precisely. However, studies have rarely compared the classification performance of DL with the pixel-based method for coastal wetland monitoring using UAV data. In particular, many studies have been conducted at the landscape level; however, little is known about the performance of species discrimination in very small patches and in mixed vegetation. We constructed a dataset based on UAV-RGB data and compared the performance of pixel-based and DL methods for five scenarios (combinations of annotation type and patch size) in the classification of salt marsh vegetation. Maximum likelihood, a pixel-based classification method, showed the lowest overall accuracy of 73%, whereas the U-Net classification method achieved over 90% accuracy in all classification scenarios. As expected, in a comparison of pixel-based and DL methods, the DL approach achieved the most accurate classification results. Unexpectedly, there was no significant difference in overall accuracy between the two annotation types and labeling data sizes in this study. However, when comparing the classification results in detail, we confirmed that polygon-type annotation was more effective for mixed-vegetation classification than the bounding-box type. Moreover, the smaller size of labeling data was more effective for detecting small vegetation patches. Our results suggest that a combination of UAV-RGB data and DL can facilitate the accurate mapping of coastal salt marsh vegetation at the local scale.