• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.211-212
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• 2022

지구온난화에 따른 우리나라 주변 환경의 변화와 최근 중국 불법어선의 연근해 어업자원의 고갈 등으로 인해 우리나라 연근해 어족자원을 보호할 필요성이 증대되고 있으며, 지속 가능한 어업을 위해서는 어획물의 종류와 양을 정확히 파악하고 불법 어업에 대한 철저한 감시 및 관리가 필요하다. 시공간적으로 다양하게 변하는 생태 및 어장 환경 정보와 선박에 대한 정보를 통해 해양관측과 위성 원격탐사를 동시에 이용함으로써 근해와 원양 생물자원 실태를 관측하는 것이 가능하다. 본 연구에서는 야간 불빛 위성 Suomi-NPP (Suomi National Polar-orbiting Partnership) 및 후속위성인 NOAA-20의 VIIRS (Visible Infrared Imaging Radiometer Suite) DNB (Day & Night Band) 영상을 이용하여 야간 불빛을 활용하고자 한다. 이 불빛 위성 자료를 이용하여 야간에 조업하는 어선 선단의 공간 분포를 분석할 수 있다. 또한 이 불빛 위성 자료와 AIS 자료를 상호 비교하여, 불빛 위성 자료를 통해 실제 선박의 위치 정보를 검색하는 것이 가능함을 검증하고자 한다.

• Asian Journal of Business Environment
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• v.13 no.2
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• pp.9-20
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• 2023

Purpose: This study aims to map the spatial distribution of poverty using nighttime light satellite images as a proxy indicator of economic activities and infrastructure distribution in D.I Yogyakarta, Indonesia. Research design, data, and methodology: This study uses official poverty statistics (National Socio-economic Survey (SUSENAS) and Poverty Database 2015) to compare satellite imagery's ability to identify poor urban areas in D.I Yogyakarta. National Socioeconomic Survey (SUSENAS), as poverty statistics at the macro level, uses expenditure to determine the poor in a region. Poverty Database 2015 (BDT 2015), as poverty statistics at the micro-level, uses asset ownership to determine the poor population in an area. Pearson correlation is used to identify the correlation among variables and construct a Support Vector Regression (SVR) model to estimate the poverty level at a granular level of 1 km x 1 km. Results: It is found that macro poverty level and moderate annual nighttime light intensity have a Pearson correlation of 74 percent. It is more significant than micro poverty, with the Pearson correlation being 49 percent in 2015. The SVR prediction model can achieve the root mean squared error (RMSE) of up to 8.48 percent on SUSENAS 2020 poverty data.Conclusion: Nighttime light satellite imagery data has potential benefits as alternative data to support regional poverty mapping, especially in urban areas. Using satellite imagery data is better at predicting regional poverty based on expenditure than asset ownership at the micro-level. Light intensity at night can better describe the use of electricity consumption for economic activities at night, which is captured in spending on electricity financing compared to asset ownership.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.366-368
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• 2022

지구온난화에 따른 우리나라 주변 환경의 변화와 최근 중국 불법 어선의 연근해 어업 자원의 고갈 등으로 인해 우리나라 연근해 어족자원을 보호할 필요성이 증대되고 있으며, 지속 가능한 어업을 위해서는 어획물의 종류와 양을 정확히 파악하고 불법 어업에 대한 철저한 감시 및 관리가 필요하다. 이러한 시공간적으로 다양하게 변하는 생태 및 어장 환경 정보와 선박에 대한 정보를 통해 해양관측과 위성 원격탐사를 동시에 이용함으로써 근해와 원양 생물자원 실태를 관측하는 것이 가능하다. 본 연구에서는 NOAA-20 위성의 VIIRS (Visible Infrared Imaging Radiometer Suite) DNB (Day & Night Band) 영상을 기반으로 추정한 야간 불빛 자료를 활용하고자 한다. DNB 불빛 영상은 낮은 조도의 불빛을 감지하여 그 정보를 보여 준다. 야간 불빛 자료에 포함된 구름 부분을 마스킹하기 위해 NASA의 신규알고리즘이 적용된JPSS-JRR-CloudMask 기술을 이용하였다. 이번 연구에서는 구름의 영향이 없는 날짜를 선별한 후 AIS 정보에서 어선의 정보를 추출하여 검증 자료로 사용하였다. 실제 선박의 정보를 이용한 위성 불빛 자료의 검증을 통해 위성자료의 신뢰성을 확보하고 향후 불빛과 선단 규모의 상관관계 분석 및 어선의 분포 경향 분석을 통하여 우리나라의 어장환경 분석에 활용 가능할 것으로 기대한다.

• Korean Journal of Environment and Ecology
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• v.34 no.1
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• pp.63-71
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• 2020

This study characterized the spatial and seasonal patterns of light pollution in the Deogyusan National Park and examined the potential effects of light pollution on ecosystems in the park using light intensities derived from VIIRS (Visible Infrared Imaging Radiometer Suite) DNB (Day and Night Band) nightlight images collected in January and August 2018. Results showed that the Muju Deogyusan resort had the greatest light intensity than other sources of light pollution in the park, and light intensity of the resort was much higher in January than in August, suggesting that artificial lights in ski slopes and facilities were the major source of light pollution in the park. An analysis of an urban-natural light pollution gradient along a neighboring urban area through the inside of the park indicated that light radiated from a light pollution source permeated for up to 1km into the adjacent area and contaminated the edge area of the park. Of the legally protected species whose distributions were reported in literature, four mammals (Martes flavigula, Mustela nivalis, Prionailurus bengalensis, Pteromys volans aluco), two birds (Falco subbuteo, Falco tinnunculus), and nine amphibians and reptiles (Onychodactylus koreanus, Hynobius leechii, Karsenia koreana, Rana dybowskii, Rana huanrenensis, Elaphe dione, Rhabdophis tigrinus, Gloydius ussuriensis, Gloydius saxatilis) inhabited light-polluted areas. Of those species inhabiting light-polluted areas, nocturnal species, such as Prionailurus bengalensis and Pteromys volans aluco, in particular, were vulnerable to light pollution. These results implied that protecting ecosystems from light pollution in national parks requires managing nighttime light in the parks and surrounding areas and making a plan to manage nighttime light pollution by taking into account ecological characteristics of wild animals in the parks.

• Korean Journal of Environment and Ecology
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• v.36 no.4
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• pp.433-441
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• 2022

Light pollution is one of the factors that disturb coastal and island ecosystems. This study examined the factors causing light pollution in the islands in Daedohaehaesang National Park using nighttime satellite images. This study selected 101 islands with an area of 100,000 m² or more in Daedohaehaesang National Park, and measured the levels of light pollution of the selected islands by calculating mean nighttime radiance recorded in VIIRS DNB monthly images for January, April, August, and October 2019. Of seven districts of the park, The highest mean nighttime radiance was recorded in Geumodo district (17,666nW/m²/sr), followed by Geonumdo·Baekdo, Narodo, Soando·Cheongsando districts. By season, mean nighttime radiance in October was the highest at 9,509nW/m²/sr, followed by August, January, and April. Regression analyses show that the total floor area and the number of lighthouses in a 5 km buffer area had a statistically significant effect on mean nighttime radiance at all times, but those within the island did not, indicating that light pollution in islands in a national park where land development is strictly restricted is influenced by artificial lights in nearby areas. However, the total floor area of an island significantly affected mean nighttime radiance only in August, which appears to be attributed to the impact of intensive use of artificial light by visitors during summer vacation. The size of an island had a negative (-) effect on nighttime radiance. This negative effect suggests that light pollution is a type of ecological edge effect, i.e., the smaller island is more likely to have a relatively larger proportion of edge area that is affected by light emitted from the neighboring areas. The results of this study indicate that managing artificial lights in nearby areas is necessary to mitigate light pollution in islands in marine and coastal national parks.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.247-253
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• 2023

As climate change problem has recently become serious, studies are being conducted to identify carbon dioxide (CO₂) emission dynamics based on satellite data to reduce emissions. It is also very important to analyze spatial patterns by estimating and mapping CO₂ emissions dynamic. Therefore, in this study, CO₂ emissions in the Korean Peninsula from 2013 to 2020 were estimated and mapped. To spatially estimate and map emissions, we use the enhanced vegetation index adjusted nighttime light index, an index that combines nighttime light (NTL) and vegetation index, to map both areas where NTL is observed and areas where NTL is not observed. In order to spatially estimate and map CO₂ emissions, the total annual emissions of the Korean Peninsula were calculated, resulting in an increase of 11% from 2013 to 2017 and a decrease of 13% from 2017 to 2020. As a result of the mapping, it was confirmed that the spatial pattern of CO₂ emissions in the Korean Peninsula were concentrated in urban areas. After being divided into 17 regions, which included the downtown area, the metropolitan area accounted for roughly 40% of CO₂ emissions in the Korean Peninsula. The region that exhibited the most significant change from 2013 to 2020 was Sejong City, showing a 96% increase.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.81-91
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• 2024

Electrical energy is a key source of energy for modern civilization, and changes in electricity generation and consumption are closely related to industry and life in general. In this study, we identified the correlation between electricity generation and nighttime light values in South Korea and used it to predict monthly electricity generation trends in North Korea. The results of the study showed a low Pearson correlation coefficient of 0.34 between nighttime light and electricity generation in Seoul, but a high Pearson correlation coefficient of 0.79 between weighting for Seoul case nighttime light values and electricity generation using monthly average temperature. Using nighttime light values weighting for Seoul case by the average monthly temperature in Pyongyang to predict the monthly power generation trend in North Korea, we found that the month-on-month power generation increase in December 2022 was about 60% higher than the month-on-month power generation increase in December 2020 and 2021. The results of this study are expected to help predict monthly electricity generation trends in regions where monthly electricity generation data does not exist, making it difficult to identify timely industry trends.