Acoustic Doppler current profilers (ADCPs) were developed to acquire water current velocities, as well as depth-dependent echo intensities. As the backscattering strength of an underwater object can be estimated from the measured echo intensity, the ADCP can be used to estimate plankton populations and distributions. In this study, the backscattering strength of bubble clusters in a water tank was estimated using the commercial ADCP as a proof-of-concept. Specifically, the temporal variations in the backscattering strength and the duration of bubble existence were quantitatively evaluated. Additionally, the PDSL (population density spectrum level) and VF (void fraction) of the artificial bubbles were characterized based on the obtained distribution characteristics using a PDPA (phase Doppler particle analyzer).

Recently, neural network-based deep learning techniques have been actively applied to detect underwater objects in sonar (sound navigation and ranging) images. However, unlike optical images, acquiring sonar images is extremely time- and cost-intensive, and therefore securing sonar data and conducting related research can be rather challenging. Here, a side-scan sonar was used to obtain sonar images to detect underwater objects off the coast of the Korean Peninsula. For the detection experiments, we used an underwater mock-up model with a similar size, shape, material, and acoustic characteristics to the target object that we wished to detect. We acquired various side-scan sonar images of the mock-up object against the background of mud, sand, and rock to account for the different characteristics of the coastal and seafloor environments of the Korean Peninsula. To construct a detection network suitable for the obtained sonar images from the experiment, the performance of five types of feature extraction networks and two types of optimizers was analyzed. From the analysis results, it was confirmed that performance was achieved when DarkNet-19 was used as the feature extraction network, and ADAM was applied as the optimizer. However, it is possible that there are feature extraction network and optimizer that are more suitable for our sonar images. Therefore, further research is needed. In addition, it is expected that the performance of the modified detection network can be more improved if additional images are obtained.

Bubble clusters present in seawater can cause acoustic interference and acoustic signal distortion during marine exploration. However, this interference can also be used as an acoustic masking technique, which has significant implications for military purposes. Therefore, characterizing the distribution of bubble clusters in water would allow for the development of anti-detection technologies. In this study, a sea experiment was performed using a multi-sonar array system and a bubble-generating material developed by our research group to obtain acoustic signals from an artificial bubble cluster and characterize its distribution. The acquired acoustic data were preprocessed, and reverse-time migration (RTM) was applied to the dataset. For effective RTM, an envelope waveform was used to decrease computation time and memory requirements. The envelope RTM results could be used to effectively image the distribution characteristics of the artificial bubble clusters. Compared with acoustic Doppler current profiler data, the backscattering strength of the boundary of the imaged artificial bubble cluster was estimated to range between −30 and −20 dB. Therefore, the three-dimensional distribution characteristics of bubble clusters in the open sea can be effectively determined through envelope RTM. Furthermore, the data obtained from this study can be used as a reference for future studies.

In this study, we analyzed the effects of snow cover changes caused by snow fences (SFs) installed in 2017 in the Alaskan tundra to examine ground subsidence. Digital surface model data obtained through LiDAR-based remote sensing in 2019 and 2022, combined with a field survey in 2021, revealed approximately 0.2 m of ground subsidence around the SF. To investigate the relationship between SF-induced snow cover changes and ground subsidence, geophysical methods, electrical resistivity tomography (ERT) and ground-penetrating radar (GPR), were applied in 2023 to analyze subsurface characteristics. The increased snow cover due to the SF-enhanced insulation, delaying the penetration of winter cold into the subsurface. This delay caused subsurface temperatures to decrease more slowly, melting the upper permafrost and increasing the thickness of the active layer. ERT and GPR surveys well delineated the boundary between the active layer and permafrost, confirming that the increased snow cover thickened the active layer. This thickening led to the melting of pore ice, causing water runoff and ground compaction, which resulted in subsidence. The runoff also formed channels flowing eastward over the SF. This study highlights how changes in snow cover can influence active layer properties, leading to localized environmental changes and ground subsidence.

The impact of buildings around the King Sejong Station (KSS), South Korea’s first scientific station in Antarctica, has locally altered snowfall accumulation and vegetation distribution. Areas with high snowfall accumulation exhibited sparse vegetation, whereas areas with low snowfall showed distinct vegetation. This study conducted a comprehensive analysis using various data sources to understand the causes of changes in vegetation distribution. Meteorological data, including air temperature, soil temperature, soil moisture, and wind, were analyzed to determine the impact of station buildings on snow cover changes. The changes in vegetation distribution were more clearly visible through results of measured Normalized Difference Vegetation Index. Additionally, time-lapse electrical resistivity data were collected throughout 2020 to analyze variations in the subsurface electrical resistivity distribution. Electrical resistivity surveys utilized both dipole–dipole and Wenner arrays to gather data, with subsurface electrical resistivity information obtained through inversion process. The active layer, which is characterized by low electrical resistivity and is conducive to vegetation growth, is distributed in the upper layers and changes over time, only in vegetated area. In contrast, the development of the active layer was not observed in nonvegetated area. In conclusion, the time-lapse electrical resistivity data effectively reveal the temporal changes in the distribution of the active layer in the study area. When the electrical resistivity data were interpreted in conjunction with meteorological data, it provided a good understanding of the causes of changes in the distribution of vegetation around the KSS.