• Proceedings of KOSOMES biannual meeting
• /
• 2017.04a
• /
• pp.236-236
• /
• 2017

• Journal of Preventive Medicine and Public Health
• /
• v.42 no.2
• /
• pp.73-81
• /
• 2009

Objectives : This study presents a scientific basis for the establishment of an environmental health contingency plan for dealing with accidental coastal oil spills and suggests some strategies for use in an environmental health emergency. Methods : We reviewed the existing literature, and analyzed the various fundamental factors involved in response strategies for oil spill. Our analysis included data derived from Hebei Spirit oil spill and used air dispersion modeling. Results : Spill amounts of more than 1,000 kl can affect the health of residents along the coast, especially those who belong to vulnerable groups. Almost 30% of South Korean population lives in the vicinity of the coast. The area that is at the highest risk for a spill and that has the greatest number of people at risk is the stretch of coastline from Busan to Tongyeong. The most prevalent types of oil spilt in Korean waters have been crude oil and bunker-C oil, both of which have relatively high specific gravity and contain volatile organic compounds, polycyclic aromatic hydrocarbons, and metals. In the case of a spill of more than 1,000 kl, it may be necessary to evacuate vulnerable and sensitive groups. Conclusions : The government should establish environmental health planning that considers the spill amount, the types of oil, and the distance between the spot of the accident and the coast, and should assemble a response team that includes environmental health specialists to prepare for the future oil spill.

• Journal of Ocean Engineering and Technology
• /
• v.38 no.2
• /
• pp.64-73
• /
• 2024

Oil spills pose significant threats to marine ecosystems, human health, socioeconomic aspects, and coastal communities. Accurate real-time predictions of oil slick transport along coastlines are paramount for quick preparedness and response efforts. This study used an open-source OpenOil numerical model to simulate the fate and trajectories of oil slicks released during the 2007 Hebei Spirit accident along the Korean coasts. Six combinations of input parameters, derived from a five-day met-ocean dataset incorporating various hydrodynamic, meteorological, and wave models, were investigated to determine the input variables that lead to the most reasonable results. The predictive performance of each combination was evaluated quantitatively by comparing the dimensions and matching rates between the simulated and observed oil slicks extracted from synthetic aperture radar (SAR) data on the ocean surface. The results show that the combination incorporating the Hybrid Coordinate Ocean Model (HYCOM) for hydrodynamic parameters exhibited more substantial agreement with the observed spill areas than Copernicus Marine Environment Monitoring Service (CMEMS), yielding up to 88% and 53% similarity, respectively, during a more than four-day oil transportation near Taean coasts. This study underscores the importance of integrating high-resolution met-ocean models into oil spill modeling efforts to enhance the predictive accuracy regarding oil spill dynamics and weathering processes.

• Korean Journal of Remote Sensing
• /
• v.29 no.6
• /
• pp.645-655
• /
• 2013

As one of segmentation techniques for Synthetic Aperture Radar (SAR) image with oil spill, we applied a bimodal histogram method to discriminate oil pixels from non-oil pixels. The threshold of each moving window was objectively determined using the two peaks in the histogram distribution of backscattering coefficients from ENVISAT ASAR image. To reduce the effect of wind speed on oil spill detection, we selected ASAR image which satisfied a limit of wind speeds for successful detection. Overall, a commonly used adaptive threshold method has been applied with a subjectively-determined single threshold. In contrast, the bimodal histogram method utilized herein produces a variety of thresholds objectively for each moving window by considering the characteristics of statistical distribution of backscattering coefficients. Comparison between the two methods revealed that the bimodal histogram method exhibited no significant difference in terms of performance when compared to the adaptive threshold method, except for around the edges of dark oil spots. Thus, we anticipate that the objective method based on the bimodality of oil slicks may also be applicable to the detection of oil spills from other SAR imagery.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_3
• /
• pp.1475-1490
• /
• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.2 no.2
• /
• pp.38-48
• /
• 1999

The oil spill accident near Goeje Island on April 3, 1997 was analyzed using two RADARSAT SAR images. The first scene was acquired 3 days after the accident as an extended low beam mode and the second scene was acquired 12 hours after the first scene as a standard beam mode. The two scenes showed slicks not only by oil spills but also by oil spill look-alikes caused by wind sheltering, low wind, natural film, and etc. These slicks were analyzed and classified, and natural films produced from aquaculture farms around Goeje Island were also suggested as a strong candidate for slicks on SAR images. The study with two SAR imags indicated the oil spill patterns which spreaded to the southwest immediately after the accident and switched the direction to the east. The spreading patterns shown in two SAR images also showed good agreement with in-situ observations.

• Ocean and Polar Research
• /
• v.41 no.1
• /
• pp.1-10
• /
• 2019

Right after the 2007 Hebei Spirit Oil Spill phytoplankton ecosystems were investigated for 11 years based on the seasonal monitoring of the composition and abundance of phytoplankton species. Comparable time-series data from the 1989 Exxon Valdez or the 2010 Deepwater Horizon Oil Spill sites were not available. It was suggested that the ecological healthiness of phytoplankton ecosystems at EVOS sites had recovered after 10 years following the oil spill based on chlorophyll concentrations even though these concentrations only represented phytoplankton communities in most cases. Chlorophyll concentrations can only reflect limited aspects of highly complex phytoplankton ecosystems. During the last 11 years following the 2017 HSOS, extreme variabilities were met in the seasonally averaged ratios of diatoms to phototrophic flagellates including dinoflagellates based on the microscopic cell countings. Summer phytoplankton communities exhibited some cyclic interannual changes in dominant groups every 2-4 years. During the early years (2008-2010) cryptophytes or raphidophytes (Chattonella spp.) dominated alternately each year, which was repeated again in 2014, 2015 and 2017. Two thecate dinoflagellates, Tripos fusus and Tripos furca, together accounted for 52.5% and 50.0% of all organisms in the summers of 2011 and 2012, respectively, which was repeated again in 2018. Summer occurrence and dominance by the phototrophic flagellates including HABs (Harmful Algal Blooms) species as well as their interannual variabilities in the oil spill sites could be utilized as markers for the stable and long-term management of healthy ecosystems. For this type of scientific ecosystem management monitoring of chlorophyll concentrations may sometimes be insufficient to gain a proper and comprehensive understanding of phytoplankton communities located in areas where oil spills have occurred and harmed the ecosystem.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1773-1784
• /
• 2021

In order to minimize damage to oil spill accidents in the ocean, it is essential to collect a spilled area as soon as possible. Thus satellite-based remote sensing is a powerful source to detect oil spills in the ocean. With the recent rapid increase in the number of available satellites, it has become possible to generate a status report of marine oil spills soon after the accident. In this study, the oil spill area was calculated using various satellite images for the Symphony oil spill accident that occurred off the coast of Qingdao Port, China, on April 27, 2021. In particular, improving the accuracy of oil spill area determination was applied using high-resolution commercial satellite images with a spatial resolution of 2m. Sentinel-1, Sentinel-2, LANDSAT-8, GEO-KOMPSAT-2B (GOCI-II) and Skysat satellite images were collected from April 27 to May 13, but five images were available considering the weather conditions. The spilled oil had spread northeastward, bound for coastal region of China. This trend was confirmed in the Skysat image and also similar to the movement prediction of oil particles from the accident location. From this result, the look-alike patch observed in the north area from the Sentinel-1A (2021.05.01) image was discriminated as a false alarm. Through the survey period, the spilled oil area tends to increase linearly after the accident. This study showed that high-resolution optical satellites can be used to calculate more accurately the distribution area of spilled oil and contribute to establishing efficient response strategies for oil spill accidents.

• Journal of the Korea Convergence Society
• /
• v.8 no.10
• /
• pp.1-8
• /
• 2017

Accurate forecasting enables to effectively prepare for future phenomenon. Especially, meteorological phenomenon is closely related with human life, and it can prevent from damage such as human life and property through forecasting of weather and disaster that can occur. To respond quickly and effectively to oil spill accidents, it is important to accurately predict the movement of oil spills and the weather in the surrounding waters. In this paper, we selected four representative machine learning techniques: support vector machine, Gaussian process, multilayer perceptron, and radial basis function network that have shown good performance and predictability in the previous studies related to oil spill detection and prediction in meteorology such as wind, rainfall and ozone. we suggest the applicability of oil spill prediction model based on machine learning.

• Korean Journal of Remote Sensing
• /
• v.39 no.3
• /
• pp.311-323
• /
• 2023

Oil spill accidents can cause various environmental issues, so it is important to quickly assess the extent and changes in the area and location of the spilled oil. In the case of oil spill detection using satellite imagery, it is possible to detect a wide range of oil spill areas by utilizing the information collected from various sensors equipped on the satellite. Previous studies have analyzed the reflectance of oil at specific wavelengths and have developed an oil spill index using bands within the specific wavelength ranges. When analyzing multiple images before and after an oil spill for monitoring purposes, a significant amount of time and computing resources are consumed due to the large volume of data. By utilizing Google Earth Engine, which allows for the analysis of large volumes of satellite imagery through a web browser, it is possible to efficiently detect oil spills. In this study, we evaluated the applicability of four types of oil spill indices in the area of various land cover using Sentinel-2 MultiSpectral Instrument data and the cloud-based Google Earth Engine platform. We assessed the separability of oil spill areas by comparing the index values for different land covers. The results of this study demonstrated the efficient utilization of Google Earth Engine in oil spill detection research and indicated that the use of oil spill index B ((B3+B4)/B2) and oil spill index C (R: B3/B2, G: (B3+B4)/B2, B: (B6+B7)/B5) can contribute to effective oil spill monitoring in other regions with complex land covers.