• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.123-135
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• 2013

The reflectance observed in the visible channels of a geostationary meteorological satellite can be used to calculate the amount of cloud by comparing the reflectance with the observed solar radiation data at the ground. Using this, the solar radiation arriving at the surface can be estimated. This study used the Meteorological Imager (MI) reflectance observed at a wavelength of 675 nm and the Geostationary Ocean Color Imager (GOCI) reflectance observed at similar wavelengths of 660 and 680 nm. Cloudy days during a typhoon and sunny days with little cloud cover were compared using observation data from the geostationary satellite. Pixels that had more than 40% reflectance in the satellite images showed less than 0.3 of the cloud index and blocked more than 70% of the solar energy. Pixels that showed less than 15% reflectance showed more than 0.9 of the cloud index and let through more than 90% of the solar energy to the surface. The calculated daily accumulated solar radiation was compared with the observed daily accumulated solar radiation in 22 observatories of the Korean Meteorological Administration. The values calculated for the COMS and MTSAT MI sensors were smaller than the observation and showed low correlations of 0.94 and 0.93, respectively, which were smaller than the 0.96 correlation coefficient calculated for the GOCI sensor. The RMSEs of MTSAT, COMS MI and GOCI calculation results showed 2.21, 2.09, 2.02 MJ/$m^2$ in order. Comparison of the calculated daily accumulated results from the GOCI sensor with the observed data on the ground gave correlations and RMSEs for cloudy and sunny days of 0.96 and 0.86, and 1.82 MJ/$m^2$ and 2.27 MJ/$m^2$, respectively, indicating a slightly higher correlation for cloudy days. Compared to the meteorological imager, the geostationary ocean color imager in the COMS satellite has limited observation time and observation is not continuous. However, it has the advantage of providing high resolution so that it too can be useful for solar energy analysis.

• Atmosphere
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• v.12 no.4
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• pp.20-42
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• 2002

Based on the "Mid to Long Term Plan for Space Development", a project to launch COMeS (Communication, Oceanography, and Meteorological Satellite) into the geostationary orbit is undergoing. Accordingly, KMA (Korea Meteorological Administration) has defined the meteorological missions and prepared the user requirements to fulfill the missions. To make a realistic user requirements, we prepared a first draft based on the ideal meteorological products derivable from a geostationary platform and sent the RFI (request for information) to the sensor manufacturers. Based on the responses to the RFI and other considerations, we revised the user requirement to be a realistic plan for the 2008 launch of the satellite. This manuscript introduces the revised user requirements briefly. The major mission defined in the revised user requirement is the augmentation of the detection and prediction ability of the severe weather phenomena, especially around the Korean Peninsula. The required payload is an enhanced Imager, which includes the major observation channels of the current geostationary sounder. To derive the required meteorological products from the Imager, at least 12 channels are required with the optimum of 16 channels. The minimum 12 channels are 6 wavelength bands used for current geostationary satellite, and additional channels in two visible bands, a near infrared band, two water vapor bands and one ozone absorption band. From these enhanced channel observation, we are going to derive and utilize the information of water vapor, stability index, wind field, and analysis of special weather phenomena such as the yellow sand event in addition to the standard derived products from the current geostationary Imager data. For a better temporal coverage, the Imager is required to acquire the full disk data within 15 minutes and to have the rapid scan mode for the limited area coverage. The required thresholds of spatial resolutions are 1 km and 2 km for visible and infrared channels, respectively, while the target resolutions are 0.5 km and 1 km.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.947-958
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• 2021

This study was conducted to provide basic data for crop monitoring by comparing and analyzing changes in reflectance and vegetation index by sensor of multi-spectral sensors mounted on unmanned aerial vehicles. For four types of unmanned aerial vehicle-mounted multispectral sensors, such as RedEdge-MX, S110 NIR, Sequioa, and P4M, on September 14 and September 15, 2020, aerial images were taken, once in the morning and in the afternoon, a total of 4 times, and reflectance and vegetation index were calculated and compared. In the case of reflectance, the time-series coefficient of variation of all sensors showed an average value of about 10% or more, indicating that there is a limit to its use. The coefficient of variation of the vegetation index by sensor for the crop test group showed an average value of 1.2 to 3.6% in the crop experimental sites with high vitality due to thick vegetation, showing variability within 5%. However, this was a higher value than the coefficient of variation on a clear day, and it is estimated that the weather conditions such as clouds were different in the morning and afternoon during the experiment period. It is thought that it is necessary to establish and implement a UAV flight plan. As a result of comparing the NDVI between the multi-spectral sensors of the unmanned aerial vehicle, in this experiment, it is thought that the RedEdeg-MX sensor can be used together without special correction of the NDVI value even if several sensors of the same type are used in a stable light environment. RedEdge-MX, P4M, and Sequioa sensors showed a linear relationship with each other, but supplementary experiments are needed to evaluate joint utilization through off-set correction between vegetation indices.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.437-444
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• 2017

Electric Pole is a supporting beam used for power transmission/distribution which is sensitive to external force change of environmental factors. Therefore, power facilities have many difficulties in terms of maintenance/conservation from external environmental changes and natural disasters that cause a great economic impact. The aerial wire cause elasticity due to the influence of temperature, or factors such as wind speed and wind direction, that weakens the electric pole. The situation may lead to many safety risk in day-to-day life. But, the safety assessment of the pole is carried out at the design stage, and aftermath is not considered. For the safety and maintenance purposes, it is very important to analyze the influence of weather factors on external forces periodically. In this paper, we analyze the acceleration data of the sensor nodes installed in electric pole for maintenance/safety purpose and use Kalman filter as noise compensation method. Fast Fourier Transform (FFT) is performed to analyze the influence of each meteorological factor, along with the meteorological factors on frequency components. The result of the analysis shows that the temperature, humidity, solar radiation, hour of daylight, air pressure, wind direction and wind speed were influential factors. In this paper, the influences of meteorological factors on frequency components are different, and it is thought that it can be an important factor in achieving the purpose of safety and maintenance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.347-350
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• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.63-70
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• 2011

A bicycle is different from vehicles in the structure that a rider is fully exposed to the surrounding environment. Therefore, it needs to make use of prior information about local weather, air quality, trail road condition. Moreover, since it depends on human power for moving, it should acquire route property such as hill slope, winding, and road surface to improve its efficiency in everyday use. Recent mobile applications which are to be used during bicycle riding let us aware of the necessity of development of intelligent bicycles. This study aims to develop a riding state (up-hill, down-hill, accelerating, braking) recognition algorithm using a low-power wrist watch type embedded system which has 3-axis accelerometer and wireless communication capability. The developed algorithm was applied to 19 experimental riding data and showed more than 95% of correct recognition over 83.3% of the total dataset. The altitude and temperature sensor also in the embedded system mounted on the bicycle is being used to improve the accuracy of the algorithm. The developed riding state recognition algorithm is expected to be a platform technology for intelligent bicycle interface system.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.483-489
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• 2019

True color images display colors similar to natural colors. This has the advantage that it is possible to monitor rapidly the complex earth atmosphere phenomenon and the change of the surface type. Currently, various organizations are producing true color images. In Korea, it is necessary to produce true color images by replacing generations with next generation weather satellites. Therefore, in this study, visual enhancement for true color image production was performed using Top of Atmosphere (TOA) data of Advanced Himawari Imager (AHI) sensor mounted on Himawari-8 satellite. In order to improve the visualization, we performed two methods of Nonlinear enhancement and Histogram equalization. As a result, Histogram equalization showed a strong bluish image in the region over $70^{\circ}$ Solar Zenith Angle (SZA) compared to the Nonlinear enhancement and nonlinear enhancement technique showed a reddish vegetation area.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.22 no.3
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• pp.118-134
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• 2017

The radiation observation data will be used importantly in research field such as climatology, weather, architecture, agro-livestock and marine science. The Ieodo Ocean Research Station (IORS) is regarded as an ideal observatory because its location can minimize the solar radiation reflection from the surrounding background and also the data produced here can serve as a reference data for radiation observation. This station has the potential to emerge as a significant observatory and join a global radiation observation group such as the Baseline Surface Radiation Network (BSRN), if the surrounding of observatory is improved and be equipped with the essential radiation measuring instruments (pyaranometer and pyrheliometer). IORS has observed the solar radiation using a pyranometer since November 2004 and the data from January 1, 2005 to December 31, 2015 were analyzed in this study. During the period of this study, the daily mean solar radiation observed from IORS decreased to $-3.80W/m^2/year$ due to the variation of the sensor response in addition to the natural environment. Since the yellow sand and fine dust from China are of great interest to scientists around the world, it is necessary to establish a basis of global joint response through the radiation data obtained at the Ieodo as well as at Sinan Gageocho and Ongjin Socheongcho Ocean Research Station. So it is an urgent need to improve the observatory surrounding and the accuracy of the observed data.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1859-1867
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• 2021

This study investigated seasonal characteristics of the particulate matter (PM) in the atmosphere and rainwater quality in Busan, South Korea, and evaluated the seasonal effect of PM₁₀ concentration in the atmosphere on the rainwater quality using multivariate statistical analysis. The concentration of PM in the atmosphere and meteorological observations(daily precipitation amount and rainfall intensity) are obtained from automatic weather systems (AWS) by the Korea Meteorological Administration (KMA) from March 2020 to August 2020. Rainwater samples (n = 216, 13 rain events) were continuously collected from the beginning of the precipitation using the rainwater collecting device at Pukyong National University. The samples were analyzed for pH, EC (electrical conductivity), water-soluble cations(Na⁺, Mg²⁺, K⁺, Ca²⁺, and NH₄⁺), and anions(Cl^-, NO₃^-, and SO₄^2-). The concentration of PM₁₀ in the atmosphere was steadily measured before and after the precipitation with a custom-built PM sensor node. The measured data were analyzed using principal component analysis (PCA) and Pearson correlation analysis to identify relationships between the concentration of PM₁₀ in the atmosphere and rainwater quality. In spring, the daily average concentration of PM₁₀ (34.11 ㎍/m³) and PM_2.5 (19.23 ㎍/m³) in the atmosphere were relatively high, while the value of daily precipitation amount and rainfall intensity were relatively low. In addition, the concentration of PM₁₀ in the atmosphere showed a significant positive correlation with the concentration of water-soluble ions (r = 0.99) and EC (r = 0.95) and a negative correlation with the pH (r = -0.84) of rainwater samples. In summer, the daily average concentration of PM₁₀ (27.79 ㎍/m³) and PM_2.5 (17.41 ㎍/m³) in the atmosphere were relatively low, and the maximum rainfall intensity was 81.6 mm/h, recording a large amount of rain for a long time. The results indicated that there was no statistically significant correlation between the concentration of PM₁₀ in the atmosphere and rainwater quality in summer.