• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1733-1741
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• 2020

This study analyzed the effect of meteorological factors on the concentration of PM10 (particulate matter 10) in the atmosphere and the variation of rainwater quality using multivariate statistical analysis. The concentration of PM10 in the atmosphere was continuously measured during eleven precipitation events with a custom-built PM sensor node. A total of 183 rainwater samples were analyzed for pH, EC (electrical conductivity), and water-soluble cations (Na+, Mg2+, K+, Ca2+, NH4+) and anions (Cl-, NO3-, SO42-). The data has been analyzed using two multivariate statistical techniques (principal component analysis, PCA, and Pearson correlation analysis) to identify relationships among PM10 concentrations in the atmosphere, meteorological factors, and rainwater quality factors. When the rainfall intensity was relatively strong (> 5 mm/h, rainfall type 1), the PM10 concentration in the atmosphere showed a negative correlation (r = -0.55, p < 0.05) with cumulative rainfall. The PM10 concentration increased the concentration of water-soluble ions (r = 0.25) and EC (r = 0.4), and decreased the pH (r = -0.7) of rainwater samples. However, for rainfall type 2 (< 5 mm/h), there was no negative correlation between the PM10 concentration in the atmosphere and cumulative rainfall and no statistically significant correlation between the PM10 concentration in the atmosphere and rainwater quality.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.463-473
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• 2021

Recently, United States Geological Survey (USGS) distributed Landsat 8 Collection 2 Level 2 Science Product (L2SP). This paper aims to derive land surface temperature from L2SP and to validate it. Validation is made by comparing the land surface temperature with the one calculated from Landsat 8 Collection 1 Level 1 Terrain Precision (L1TP) and the one from Automated Synoptic Observing System (ASOS). L2SP is calculated from Landsat 8 Collection 2 Level 1 data and it provides land surface temperature to users without processing surface reflectance data. Landsat 8 data from 2018 to 2020 is collected and ground sensor data from eight sites of ASOS are used to evaluate L2SP land surface temperature data. To compare ground sensor data with remotely sensed data, 3×3 grid area data near ASOS station is used. As a result of analysis with ASOS data, L2SP and L1TP land surface temperature shows Pearson correlation coefficient of 0.971 and 0.964, respectively. RMSE (Root Mean Square Error) of two results with ASOS data is 4.029℃, 5.247℃ respectively. This result suggests that L2SP data is more adequate to acquire land surface temperature than L1TP. If seasonal difference and geometric features such as slope are considered, the result would improve.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.27-27
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• 2017

신뢰성 있는 수개월 선행시간의 댐 유입량 예측은 가뭄 상황으로 진입하는 시점에서 효율적인 댐 운영을 위해 필수적이다. 최근 기후변화로 인한 강수량의 경년 및 계절 내 변동성이 증가됨에 따라서 기존의 과거 통계치를 이용한 댐 운영 의사결정은 많은 도전을 받고 있다. 최근 엘리뇨-남방진동(ENSO) 등의 전구기후지수와 지역수문기후와의 원격상관성을 활용하여 수개월 이후에 대한 수문조건을 통계적으로 예측하기 위한 연구가 시도되고 있다. 또한 매월 제공되는 역학적 예측모형으로부터 생산된 월단위 예측정보를 유량예측을 위한 유역모형에 활용하기 위하여 편이보정 및 상세화 기법이 개발되어 활용되고 있다. 본 연구에서는 댐 유입량 예측을 위해 SWAT 모형을 선정하였고 최장 6개월 선행 강수량 및 기온의 예측을 위해서 하이브리드 계절예측 시스템을 활용하였다. 이 시스템은 전지구역학적 예측모형의 자료를 편이보정을 거쳐 직접적으로 사용하는 단순 편이보정(Simple Bias Correction, SBC) 방법에 회귀모형을 이용하여 통계적인 방법으로 예측자료를 생산하는 전구기후지수 기반의 Climate Index Regression (CIR), 실시간 재분석자료 기반의 Observation-based Moving Window Regression (MWR-Obs), 역학적 예측모형의 예측자료 기반의 Moving Window Regression (MWR) 방법을 통합하여 사용하고 있다. 충주댐을 대상으로 우선 관측자료를 이용하여 SWAT 모형을 검 보정한 후, 관측기간에 대하여 하이브리드 시스템에 의한 예측 기상자료를 적용하여 모의된 댐 유입량과 관측 유입량과의 비교를 통해 예측성을 평가하였다. 본 연구는 다양한 기후정보를 활용하여 댐 유입량 예측에 있어서 예측성을 높이고자 시도되었으며, 도출된 결과는 향후 충주댐 운영에 유용한 정보를 제공할 수 있는 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1607-1622
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• 2022

The purpose of this study is to determine the actual performance of cool roof in preventing absorbed solar radiation. The spatial correlation between surface temperature and absorbed solar radiation is the method by which the performance of a cool roof can be understood and evaluated. The research area of this study is the vicinity of Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where an actual cool roof is applied. FLIR Vue Pro R thermal infrared sensor, Micasense Red-Edge multi-spectral sensor and DJI H20T visible spectral sensor was used for aerial photography, with attached to the drone DJI Matrice 300 RTK. To perform the spatial correlation analysis, thermal infrared orthomosaics, absorbed solar radiation distribution maps were constructed, and land cover features of roof were extracted based on the drone aerial photographs. The temporal scope of this research ranged over 9 points of time at intervals of about 1 hour and 30 minutes from 7:15 to 19:15 on July 27, 2021. The correlation coefficient values of 0.550 for the normal roof and 0.387 for the cool roof were obtained on a daily average basis. However, at 11:30 and 13:00, when the Solar altitude was high on the date of analysis, the difference in correlation coefficient values between the normal roof and the cool roof was 0.022, 0.024, showing similar correlations. In other time series, the values of the correlation coefficient of the normal roof are about 0.1 higher than that of the cool roof. This study assessed and evaluated the potential of an actual cool roof to prevent solar radiation heating a rooftop through correlation comparison with a normal roof, which serves as a control group, by using high-resolution drone images. The results of this research can be used as reference data when local governments or communities seek to adopt strategies to eliminate the phenomenon of urban heat islands.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.571-585
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• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Economic and Environmental Geology
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• v.36 no.2
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• pp.133-139
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• 2003

The purpose of this study is the analyses of the relationship between geology and terrain/satellite image in the southern part of the Bandung, Indonesia to provide basic information fur geological survey. For this, topography, geology and satellite image were constructed to spatial database. Digital elevation, slope, aspect, curvature, hill shade of topography were calculated from the topographic database and lithology was imported from the geologi-cal database. Lineament, lineament density, and NDVI were extracted the Landsat TM satellite image. The results showed the close relationship between geology and terrain and satellited image. Each sedimentary rocks seldom correspond with geology and analyses of topography but as a whole fur sedimentary rocks coincide with them. Tuff and volcanic breccia in the volcanic rocks correspond with the result of terrain analyses. Talus deposit is well matched with the analyses of topography/satellite image.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.344-344
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• 2020

기후변화 가속화와 국민의 높아진 서비스 요구 수준에 따라 농업용수의 관리방식을 현장인력의 경험적 물관리 방식에서 계측정보 기반의 과락적 물관리 방식으로 전환의 필요성이 대두되어, 2001년부터 농업기반시설 내 무인계측, 원격제어 기능이 탑재된 물관리자동화 시스템을 보급하는 농업용수관리자동화사업을 시행하였다. 농업용수관리자동화사업은 사업시행 초기 연구 결과, 농업기반시설 무인계측 및 원격제어 시스템 보급으로 인력에 의한 관행적 물관리 대비 수리시설의 관리 효율성이 크게 향상되어 유지관리 인력의 절감 및 용수수급의 적정성이 개선될 것으로 분석되었다. 하지만 영농환경의 변화에 따라, 당초 분석결과와 달리 자동화사업 추진과 한국농어촌공사의 유지관리 인력 규모 간 뚜렷한 상관성이 보이지 않는다는 정책기관의 지적이 발생하고 있다. 현재 4차산업기술이 산업 전 분야에 걸쳐 일어나고 있으며 농업분야에도 ICT, LOT, 빅데이터 기술이 도입되어 새로운 가치를 창출하고 있다. 농업용수관리 분야에 있어서는 데이터를 활요한 수요자 중심의 지능형 물관리 사업이 추진되고 있으며, 일정규모 이상 저수지 및 양수장 농업용수 공급량 측정 계측기의 설치가 추진중에 있다. 그러나 현재까지 이러한 설치된 계측장치들의 활용방안에 대해서는 뚜렷한 결과가 도축된 바 없으며, 현재 많은 예산과 인력이 투입되어 설치·운영되고 있는 계측장치들의 활용 방안에 대해서 연구가 필요한 실정이다. 2018년 2,228개 농업기반시설물에 자동화시스템을 설치 완료 하였으나, 각종 장비의 비표준화, 효과대비 고비용, 잦은 통신두절 등의 기술적 문제로 인해 현업부서의 수자원관리 업무에서 자동화시스템의 활용성이 저조한 것으로 관측됐다. 본연구에서는 국내 수자원 계측제어 기술 동향 및 운영환경 조사 결과를 기초로, 기술적 측면의 농업용수관리자동화사업의 개선사항과 4차 산업기술의 농업용수관리자동화사업의 적용방안을 제시 하여 농업용수관리자동화사업 중장기 계획 개정등 향후 정챡수립시 참고 자료로의 활용과 농업용수 효율적 활용과 관리를 위한 TM/TC 미래추진 방안을 제시로 정확하고 신뢰도 높은 농업용수 관리 체계를 구축 하고자 한다.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.49-55
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• 2001

본 연구에서는 GIS를 이용하여 산불관련 데이터베이스를 구축하고 효과적인 산불 현황정보 검색시스템을 개발하녀 산불 관리자에게 효율적인 공간분석 도구를 제공함으로서 산불에 관한 종합적인 공간정보를 빠른 시간 내에 분석하여 속성을 갱신·추출 할 수 있도록 하는 효과적인 GUI(Graphic User Interface)를 개발하였다. 이를 위하여 최근 10년간의 산불현황 통계자료를 이용한 전국 시·군단위의 공간분포도를 작성하여 전국 산불 발생현황을 시·공간적으로 분석하고 산불 발생에 미치는 다양한 요인들과의 상관성을 분석 가능하였다. 특히 최근 산불발생이 빈번한 삼척시의 지형도 및 임상도, 위성영상, 현지사진을 이용하여 GIS 데이터베이스를 구축함으로서 산불 발생위험지역에 대한 보다 상세한 정보를 얻을 수 있다. 본 시스템은 응용프로그램 개발을 위한 플랫폼은 IBM호환 PC에서 Windows 98을 운영체 제로 하여 DBMS는 Access 2000을 이용하였고 프로그래밍 언어로는 객체지향언어인 Visual Basic 6.0과 GIS 기능을 구현하기 위해서 Component GIS인 MapObjects 2.0을 사용 하였다. 그 결과 산불관리자는 진화에 필요한 관리구역내의 정보를 신속하게 제공받을 뿐만 아니라 산불방제사업에 대한 효과적인 의사결정지원과 함께 실무자 중심의 산불관리행정을 도모하 고 산림자원관리비가 효율적으로 이용될 것이다.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.133-139
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• 2016

Particle depolarization ratios (DPRs) at 440, 675, 870 and 1020 nm are retrieved from AERONET sun/sky radiometer observations at Gosan and Kongju in South Korea. The retrieved results show good agreement with DPRs measured by lidar at 532 nm. High DPRs are found when Asian dust passes through at the upper atmosphere over 2 km above the Earth's surface. In case of lower atmosphere less than 2 km from the ground, DPRs are relatively low due to the small amount of dust particles and mixing of dust with air pollutants.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.169-181
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• 2023

Recently, in order to reduce carbon dioxide (CO₂) emissions, which is the main cause of global warming, Korea has declared carbon emission reduction targets and carbon neutral. Accurate assessment of regional emissions and atmospheric CO₂ concentrations is becoming important as a result. In this study, we identified the spatiotemporal differences between satellite-based atmospheric CO₂ concentration and CO₂ emissions for the Korean Peninsula region using column-averaged CO₂ dry-air mole fraction from the Orbiting Carbon Observatory-2 and emission inventory. And we explained these differences using solar-induced fluorescence (SIF), a photosynthetic reaction index according to vegetation growth. The Greenhouse Gas Inventory and Research Center (GIR) and Emissions Database for Global Atmospheric Research (EDGAR) emissions continued to increase in Korea from 2014 to 2018, but the satellite-based atmospheric CO₂ concentration decreased in 2018, respectively. Regionally, GIR and EDGAR emissions increased in 2018 in Gyeonggi-do and Chungcheongbuk-do, but satellite-based CO₂ concentrations decreased for the corresponding years. In addition, the correlation analysis between emissions and satellite-based CO₂ concentration showed a low correlation of 0.22 (GIR) and 0.16 (EDGAR) in Seoul and Gangwon-do. Atmospheric CO₂ concentrations showed a different correlation with SIF by region. In the CO₂-SIF correlation analysis for the growing season (May to September), Seoul and Gyeonggi-do showed a negative correlation coefficient of -0.26, Chungcheongbuk-do and Gangwon-do showed a positive correlation coefficient of 0.46. Therefore, it can be suggested that consideration of the CO₂ absorption process is necessary for analyzing the relationship between the atmospheric CO₂ concentration and emission inventory.