• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1259-1268
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• 2021

The 2nd Geostationary Ocean Color Imager (GOCI-II) is the successor to the Geostationary Ocean Color Imager (GOCI), which employs one near-ultraviolet wavelength (380 nm) and eight visible wavelengths(412, 443, 490, 510, 555, 620, 660, 680 nm) and three near-infrared wavelengths(709, 745, 865 nm) to observe the marine environment in Northeast Asia, including the Korean Peninsula. However, the multispectral radiance image observed at satellite altitude includes both the water-leaving radiance and the atmospheric path radiance. Therefore, the atmospheric correction process to estimate the water-leaving radiance without the path radiance is essential for analyzing the ocean environment. This manuscript describes the GOCI-II standard atmospheric correction algorithm and its initial phase validation. The GOCI-II atmospheric correction method is theoretically based on the previous GOCI atmospheric correction, then partially improved for turbid water with the GOCI-II's two additional bands, i.e., 620 and 709 nm. The match-up showed an acceptable result, with the mean absolute percentage errors are fall within 5% in blue bands. It is supposed that part of the deviation over case-II waters arose from a lack of near-infrared vicarious calibration. We expect the GOCI-II atmospheric correction algorithm to be improved and updated regularly to the GOCI-II data processing system through continuous calibration and validation activities.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.307-325
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• 2023

Due to the prolonged COVID-19 pandemic, the frequency of people who are tired of living indoors visiting nearby mountains and national parks to relieve depression and lethargy has exploded. There is a place where thousands of people who came out of nature stop walking and breathe and rest, that is the mineral spring. Even in mountains or national parks, there are about 600 mineral springs that can be found occasionally in neighboring parks or trails in the metropolitan area. However, due to irregular and manual water quality tests, people drink mineral water without knowing the test results in real time. Therefore, in this study, we intend to develop a model that can predict the quality of the spring water in real time by exploring the factors affecting the quality of the spring water and collecting data scattered in various places. After limiting the regions to Seoul and Gyeonggi-do due to the limitations of data collection, we obtained data on water quality tests from 2015 to 2020 for about 300 mineral springs in 18 cities where data management is well performed. A total of 10 factors were finally selected after two rounds of review among various factors that are considered to affect the suitability of the mineral spring water quality. Using AutoML, an automated machine learning technology that has recently been attracting attention, we derived the top 5 models based on prediction performance among about 20 machine learning methods. Among them, the catboost model has the highest performance with a prediction classification accuracy of 75.26%. In addition, as a result of examining the absolute influence of the variables used in the analysis through the SHAP method on the prediction, the most important factor was whether or not a water quality test was judged nonconforming in the previous water quality test. It was confirmed that the temperature on the day of the inspection and the altitude of the mineral spring had an influence on whether the water quality was unsuitable.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.52-64
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• 2023

As cities continue to develop rapidly, overcrowding, pollution, and urban sanitation problems arise, and the need to separate conflicting uses is emerging. From this perspective, there is no disagreement that urban land use should be planned. Therefore, all activities in land space must be predicted in advance and planned so that land use can be rationally established. This study used the constructed data to compare and analyze the use distribution characteristics of residential, commercial, and industrial areas in Busan Metropolitan City to identify the building area status, total floor area, and floor area ratio by use zone in districts and counties in Busan Metropolitan City. As a result, it was found that the residential area accounted for the largest proportion of the area by use zone at 51%, and that the residential area accounted for the largest proportion at 63% of the total floor area by use zone. And the analysis was conducted using a specialization coefficient that can identify regional characteristics based on land use composition ratio. Because it is difficult to determine the trend of the entire region just by counting the absolute value of the area, the area composition ratio was calculated and compared. Looking at the residential facilities among the specialization coefficients by use area, it is above 1.0 except for Gijang-gun, Sasang-gu, Saha-gu, and Jung-gu. Commercial facilities are over 1.0 except for Gijang-gun, Gangseo-gu, Nam-gu, Sasang-gu, and Saha-gu. Looking at industrial facilities, you can see that the industrial complex distribution area is Gangseo-gu (2.5), Gijang-gun (1.22), Sasang-gu (2.06), and Saha-gu (1.64). In addition, it was found that business facilities and educational welfare facilities were evenly distributed. Land use analysis was conducted through simulation of the current status of building heights according to each elevation in each use area and the height of buildings in each use area. In general, areas over 80m account for more than 43% of Busan City, showing that the distribution of use areas is designated in areas with high altitude due to the influence of topographical conditions.