The Japan Organization for Metals and Security (JOGMEC) is committed to develop a technology to quantitatively evaluate the environmental impacts associated with the development of seafloor hydrothermal deposits (e.g., the effects of turbidity plumes and resedimentation). As part of this project, a seafloor disturbance experiment and monitoring survey was conducted in the Okinawa Sea area in January 2017 to determine the impact of turbidity generated by the SUR mining activity. A numerical model (particle tracking model) developed by JOGMEC was used to quantitatively predict the extent and magnitude of these impact during the experiment. In this study, we first set the condition for generation of turbidity based on the monitoring data to the numerical model, and then we examined the ability to simulate a resedimentation thickness and the spatial distribution pattern during the experiment period.

The simulation results using a time-varying flow field estimated from the observation data such as ADCP showed good agreement with the spatial distribution pattern and resedimentation thickness of the monitoring results, suggesting that the model can be used as a prediction tool for environmental impact assessment during the development of seafloor hydrothermal deposits. In addition, the flow field prediction method using the hydrodynamic model is effective and accurate for the preliminary impact prediction especially under the situation where observation data such as current velocimeters are insufficient. The results of this preliminary impact prediction are expected to be utilized as useful information that contributes to the planning of monitoring surveys.

The deep sea is the largest habitat on Earth for a diverse range of benthic organisms. Deep-sea organisms play important roles in the formation of sedimentary structures, the rearrangement of organic matter, pore water chemistry, and biogeochemical cycles. However, visualization and quantification of benthic organisms inhabiting the deep-sea floor has been difficult due to the sparse distribution of benthic organisms and the inability of deep-sea cameras to visualize benthic organisms living below the sediment surface. Here, a newly developed acoustic coring system (A-core-2000) was applied to visualize and detect burrowing bivalves in deep-sea sediments. In situ acoustic observations focused on patches of juvenile bivalves with a completely invertebrate lifestyle in a dense bivalve colony in a hydrocarbon upwelling area in Sagami Bay; strong backscatter from the top and bottom edges of the animals was clearly observed in the 3D acoustic data. At least 17 reflection points were identified in the study area, which were interpreted to correspond to live clams. The estimated depth of the lower margin of the clams ranged from 41–98 mm. The acoustic system presented here is useful for detecting and monitoring endogenous organisms with calcified exoskeletons. This new tool will help to better assess and understand the distribution and biogeochemical cycles of deep-sea endotherms, especially groups with hard exoskeletons.

Currently, in terms of the utilization of blue carbon, it is practically expected to be a powerful approach for achieving carbon neutrality. However, there is a challenge for crediting such seaweed beds when it comes to the development of a method to efficiently determine the area and existing amount of seaweed beds which are a source of CO₂ absorption. Moreover, within the shallow-water areas on which the seaweed beds are growing, there are plenty of dotted reefs and the currents in this area are often fast due to breaking waves. In this regard, an efficient method for monitoring the status of seaweed beds has not yet been fully established at present. On the premise of such circumstance, in recent years, the green laser bathymetry equipment to be carried on small aircraft has made it possible to obtain detailed topographic data in a short time in shallow-water areas that had been difficult to survey in the past. Additionally, there are specifically water surface, seaweed bed and seafloor for ALB in classification based on the difference in the reflection intensity of the irradiating laser. After multiplying the leaf length of seaweed beds by the distribution of seaweed beds in the aerial photograph, it is feasible to accurately calculate the abundance of seaweed beds. In the study, the information on the distribution of seaweed beds on a trial basis is acquired in trial with the help of point clouds acquired by ALB. Furthermore, the seafloor topography which is the foundation for the growth of seaweed beds is also obtained accordingly.

Consideration of blue carbon is rapidly progressing, and mapping of seagrass/macroalgal beds on a broad scale is essential as a basis for these studies. Here, I introduce quantitative evaluation methods using remote sensing, and review recent applications of deep learning to seagrass beds. In addition, I would like to address the case studies how such mapped information can be addressed to predict future distribution of macrophyte beds in different socio economic and/or climate scenarios that includes carbon sequestration. Not only for the initiatives by UNFCC, these aspects will contribute on the international achievements of biodiversity targets and disclosures such as Kunming-Montreal Global biodiversity framework (KMGBF) and The Taskforce on Nature-related Financial Disclosures (TNFD). If it comes with the monitoring of the biodiversity and ecosystem services.

In this paper, we report the results of measuring pCO₂ (partial pressure of carbon dioxide) in seawater around Tomari wharf, Naha Port using the sightseeing boats “Marine Star” and “Harmony” owned by Marine Tourism Development Co., Ltd.. The measuring device used was a portable NDIR-based carbon dioxide measuring device, which used non-dispersive infrared absorption (NDIR) to measure pCO₂ in seawater and the atmosphere. During observations conducted on February 15, 2022, confirmed that seawater pCO₂ was approximately 400 ppm at the Tomari wharf, but it dropped to 328 ppm directly above the soft coral colony area in the Naha port. This suggests that soft corals absorb CO₂ from seawater. However, spatiotemporal fluctuations in seawater pCO₂ are thought to be influenced by many environmental factors, including day and night photosynthesis and respiration processes, solar radiation, freshwater inflow, tidal advection and diffusion, and the stratified structure of water masses. Future research will require continuous observation and data analysis to clarify the relationship between these factors and seawater pCO₂, and to more accurately calculate the amount of CO₂ absorbed by soft corals. In order to accurately assess the carbon reduction potential of blue carbon ecosystems, it is important to conduct highly accurate pCO₂ measurements on a broad temporal and spatial scale, which forms the basis for estimating CO₂ absorption between the atmosphere and the ocean.

Observation of marine soundscapes, which comprises biological, geological, and anthropological sounds, is a cost-effective tool to monitor the biodiversity and human activity in the coastal seas. Real-time and automated classification of complex underwater sound sources is challenging but essential step to elicit meaningful information from recorded sound stream. Although numerous classification algorithms have been proposed, many of them were designed for a specific sound source, since time-frequency characteristics largely vary among soundscape component. In a bid to establish a real-time monitoring system, this study aimed at developing a single classification algorithm which is capable for diverse marine soundscape components. Ground-truth sound data were collected in the coral reef area around Ishigaki Island, Japan, and in captivity. After the filtering and normalization procedure for collected sounds, two-second log-spectrograms were generated. An image classifier with a convolutional neural network was then trained using these spectrograms. The classifier was trained with 6511 samples from 52 sound classes. Average F-measures from Level 1 (biophony, geophony, and anthrophony), 2 (fish, marine mammals, or vessels), 3 (species) classifications were 90.9%, 94.3%, 93.0%, respectively. Given the quality and quantity of training data play crucial role for the development of reliable classifier using AI technique, building a database to accumulate underwater sound is expected.

The United Nations Environment Programme (UNEP) first coined the term “blue carbon” in 2009. At that time, of course, almost no one in Japan or the world had heard of the term. Today, however, it appears daily in newspapers and magazines in Japan and has even appeared as a question on a TV quiz show. Here, I explain that the use of blue carbon is one of the essential carbon dioxide removal (CDR) technologies and nature-based solutions (NbS) to achieve the domestic and international goal of carbon neutrality by 2050. Also, I show (1) the latest examples of research and technology development and national policy trends on blue carbon in Japan, and (2) the latest information on social implementation, such as carbon credit trading through voluntary participation of various stakeholders, including fishermen, citizens, local governments, and private companies.