• Journal of the Korean Society for Marine Environment & Energy
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• v.7 no.1
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• pp.1-12
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• 2004

The summer distribution of $Cha-{alpha}$ and physical processes for simulating outbreak region of red tide were estimated by the Eco-Hydrodynamic model in Chinhae Bay. As a result of simulation of surface residual currents, the southward flow come in contact with the northward flow at the inlet and western part of bay in case of windlessness and below wind velocity 2 m/sec. As wind velocity increases, the velocity and direction of currents were fairly shifted. The predicted concentration of $Cha-{alpha}$ exceeded 20 mg/㎥ in Masan and Haengam Bays, and most regions were over 10 mg/㎥, which meant the possibility of red tide outbreak. From the results of the contributed physical processes to $Cha-{alpha}$, accumulation sites were distributed at the northern part of Kadok channel, around the Chilcheon island, the western part of Kajo island and some area of Chindong Bay. On the other hand, inner parts of the study area such as Masan Bay were estimated as the sites of strong algal activities. Masan and Haengam Bay are considered as the initial outbreak region of red tide by the modeling and observed data, and then red tide expanded to other areas such as physical accumulation region and western inner bay, as depending on environmental variation. The increase of wind velocity led to decrease of $Cha-{alpha}$ and enlargement of accumulation region. The variation of intensity of radiation and sunshine duration caused to rapidly fluctuation of $Cha-{alpha}$: however, it was not largely affected by the variation of pollutant loads from the land only.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1635-1650
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• 2023

Under global warming, the steadily increasing sea surface temperature (SST) severely impacts marine ecosystems,such as the productivity decrease and change in marine species distribution. Recently, the catch of Todarodes Pacificus, one of South Korea's primary marine resources, has dramatically decreased. In this study, we analyze the marine environment that affects the formation of fishing grounds of Todarodes Pacificus and develop seasonal habitat suitability index (HSI) models based on various satellite data including Geostationary Ocean Color Imager (GOCI) data to continuously manage fisheries resources over Korean exclusive economic zone. About 83% of catches are found within the range of SST of 14.11-26.16℃,sea level height of 0.56-0.82 m, chlorophyll-a concentration of 0.31-1.52 mg m^-3, and primary production of 580.96-1574.13 mg C m^-2 day^-1. The seasonal HSI models are developed using the Arithmetic Mean Model, which showed the best performance. Comparing the developed HSI value with the 2019 catch data, it is confirmed that the HSI model is valid because the fishing grounds are formed in different sea regions by season (East Sea in winter and Yellow Sea in summer) and the high HSI (> 0.6) concurrences to areas with the high catch. In addition, we identified the significant increasing trend in SST over study regions, which is highly related to the formation of fishing grounds of Todarodes Pacificus. We can expect the fishing grounds will be changed by accelerating ocean warming in the future. Continuous HSI monitoring is necessary to manage fisheries' spatial and temporal distribution.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.