• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.5-5
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• 2023

제3차 우주개발 진흥 기본계획의 일환으로써 개발되는 차세대 중형위성 5호인 수자원위성은 수자원/수재해 감시 전용 위성으로 2025년 발사 예정이다. 수자원위성의 메인 센서인 C-band 영상레이더(Synthetic Aperture Radar, SAR)는 기상조건 및 주야 상관없이 지표면 관측이 가능한 센서로 급변하는 수재해 양상에 효과적으로 대응하기 위해 탑재된 센서이다. 본 연구사업은 차세대 수자원위성의 효과적 활용 방안 및 SAR 자료기반의 활용산출물 및 주제도 서비스를 위한 알고리즘 구조설계 및 표출시스템 시범개발을 목표로 하고 있으며, 홍수/가뭄/안전/환경모니터링을 주제로 수자원 및 원격탐사 분야의 다학제적 전문가들로 구성된 컨소시엄을 구성하여 추진하고 있다. 본 연구의 내용은 가뭄 모니터링을 위해 개발 중인 SAR 기반 토양수분과 농업적 가뭄지수 산정 알고리즘 개발 및 공간적 표출을 포함한다. 토양수분은 SAR 영상에서 지표피복별로 추출된 후방산란계수와 수문학적 개념의 융합을 통해 논/밭/산림에 대해 산정한다. 물리적 특성에 기반한 변화탐지모델을 활용해 토양수분량을 추출 후, 기계학습기법과 S C S - C N 방법에서 파생된 수문학적 개념 5일 선행강우량과 결합한 토양수분 산정 알고리즘을 개발하였다. 산정된 토양수분을 기반으로, 논 지역은 벼 재배에 따른 담수 시기를 고려한 토양의 포화/불포화상태, 밭 지역은 토양 종류에 따른 토양의 물리적 특성, 산림 지역은 수문학적 개념 및 식생지수를 활용하여 가뭄 판단 기준을 구축하고, 가뭄의 해갈 여부와 해갈되는 시점의 강우량을 산정 가능한 알고리즘을 개발하였다. 개발된 가뭄 모니터링 기법은 향후 고도화, 최적화 및 안정화를 통해 수자원위성의 핵심 활용기술로써 구현할 계획이다.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.555-567
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• 2018

In order to identify the characteristics of sea surface temperature (SST) differences between microwave SST from GCOM-W1/AMSR2 and in-situ measurements in the western coast of Korea, a total of 6,457 collocated matchup data were produced using the in-situ temperature measurements from marine buoy stations (Deokjeokdo, Chilbaldo, and Oeyeondo) from July 2012 to December 2017. The accuracy of satellite microwave SSTs was presented by comparing the ocean buoy data of Deokjeokdo, Chilbaldo, and Oeyeondo stations with the AMSR2 SST data more than five years. The SST differences between the microwave SST and the in-situ temperature measurements showed some dependence on environmental factors, such as wind speed and water temperature. The AMSR2 SSTs were tended to be higher than the in-situ temperature measurements during the daytime when the wind speed was low ($<6ms^{-1}$). On the other hand, they showed positive deviation increasingly as the wind speed increased for nighttime. In addition, increasing tendency of SST differences was related to decreasing sensitivity of microwave sensors at low temperatures and data contamination by land. A monthly analysis of the SST difference showed that unlike the previous trend, which was known to be the largest in winter when strong winds were blowing, the SST difference was largest in summer in Deokjeokdo and Chilbaldo buoy stations. This seemed to be induced by differential tidal mixing at the collocated matchup points. This study presented problems and limitations of the use of microwave SSTs with high contribution to the SST composites in the western coastal region off the Korean peninsula.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.953-966
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• 2022

Since aerosols adversely affect human health, such as deteriorating air quality, quantitative observation of the distribution and characteristics of aerosols is essential. Recently, satellite-based Aerosol Optical Depth (AOD) data is used in various studies as periodic and quantitative information acquisition means on the global scale, but optical sensor-based satellite AOD images are missing in some areas with cloud conditions. In this study, we produced gap-free GeoKompsat 2A (GK-2A) Advanced Meteorological Imager (AMI) AOD hourly images after generating a Random Forest based gap-filling model using grid meteorological and geographic elements as input variables. The accuracy of the model is Mean Bias Error (MBE) of -0.002 and Root Mean Square Error (RMSE) of 0.145, which is higher than the target accuracy of the original data and considering that the target object is an atmospheric variable with Correlation Coefficient (CC) of 0.714, it is a model with sufficient explanatory power. The high temporal resolution of geostationary satellites is suitable for diurnal variation observation and is an important model for other research such as input for atmospheric correction, estimation of ground PM, analysis of small fires or pollutants.

• Geophysics and Geophysical Exploration
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• v.12 no.3
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• pp.225-232
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• 2009

As part of seismic experiments investigating crustal velocity structures of the Korean peninsula, permanent (fixed) seismographs of the Korea Meteorological Administration (KMA) network recorded seismic signals from four and eight large explosions in Korean Crustal Research Team (KCRT) profiles shot in 2004 and 2008, respectively. Among the seismograms recorded by 43 velocity sensors and 103 accelerometers at KMA stations distributed throughout the southern Korean Peninsula, 156 records with epicentral distances less than 120 km and high signal-to-noise ratios were analyzed to determine velocity anisotropy of the Pg phase. Relative elevation corrections of -101.6 to 105.3 ms were made using velocity information derived from the 2004 KCRT profile data and differences in elevation between the permanent KMA stations and the temporary stations in the KCRT profiles at the same source-receiver offsets. To remove site effects, receiver-station corrections of -89.6 to 192.2 ms were additionally made to the KMA station data by subtracting the average differences in traveltimes between KMA stations and portable stations at the same offsets for all available shots with different azimuths. With the exception of anomalously fast velocities along trends of the Chugaryeong fault zone and the Okchon fold belt and anomalously slow velocities in the regions of high terrestrial heat near Yeongduk and Ulsan, the analysis of crustal velocity anisotropy using the Pg phase indicates overall isotropy in the southern half of the Korean peninsula.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.1-6
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• 2020

UAV (Unmanned Aerial Vehicles) are used widely for military purposes because they are more economical than general manned aircraft and satellites, and have easy access to the object. Recently, owing to the development of IT technology, UAV equipped with various sensors have been released, and their use is increasing in a wide range of fields, such as surveying, agriculture, meteorological observation, communication, broadcasting, and sports. An increasing number of studies and attempts have made use of it. On the other hand, existing research was related mostly to photogrammetry, but there has been a lack of analytical research on LiDAR (Light Detection And Ranging). Therefore, this study examined the characteristics of a UAV LiDAR sensor for the application of a geospatial information field. In this study, the performance of commercialized LiDAR sensors, such as the acquisition speed and the number of echoes, was investigated, and data acquisition and analysis were conducted by selecting Surveyor Ultra and VX15 models with similar accuracy and data acquisition distances. As a result, a DSM of each study site was generated for each sensor, and the characteristics of data density, precision, and acquisition of ground data from vegetation areas were presented through comparison. In addition, the UAV LiDAR sensor showed an accuracy of 0.03m ~ 0.05m. Hence, it is necessary to select equipment considering the characteristics of data for effective use. In the future, the use of UAV LiDAR may be suggested if additional data can be obtained and analyzed for various areas, such as urban areas and forest areas.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.311-326
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• 2022

The Compact Advanced Satellite 500-4 (CAS500-4) is under development to efficiently manage and monitor forests in Korea and is scheduled to launch in 2025. The National Institute of Forest Science is developing 36 types of forestry applications to utilize the CAS500-4 efficiently. The products derived using the remote sensing method require validation with ground reference data, and the quality monitoring results for the products must be continuously reported. Due to it being the first time developing the national forestry satellite, there is no official calibration and validation site for forestry products in Korea. Accordingly, the author designed a calibration and validation site for the forestry products following international standards. In addition, to install calibration and validation sites nationwide, the authors selected appropriate sensors and evaluated the applicability of the sensors. As a result, the difference between the ground observation data and the Sentinel-2 image was observed to be within ±5%, confirming that the sensor could be used for nationwide expansion.

• Atmosphere
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• v.24 no.3
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• pp.445-456
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• 2014

A real-time quality control system for meteorological data (air temperature, air pressure, relative humidity, wind speed, wind direction, and precipitation) measured by an integrated meteorological sensor has been developed based on comparison of quality control procedures for meteorological data that were developed by the World Meteorological Organization and the Korea Meteorological Administration (KMA), using time series and statistical analysis of a 12-year meteorological data set observed from 2000 to 2011 at the Incheon site in Korea. The quality control system includes missing value, physical limit, step, internal consistency, persistence, and climate range tests. Flags indicating good, doubtful, erroneous, not checked, or missing values were added to the raw data after the quality control procedure. The climate range test was applied to the monthly data for air temperature and pressure, and its threshold values were modified from ${\pm}2{\sigma}$ and ${\pm}3{\sigma}$ to ${\pm}3{\sigma}$ and ${\pm}6{\sigma}$, respectively, in order to consider extreme phenomena such as heat waves and typhoons. In addition, the threshold values of the step test for air temperature, air pressure, relative humidity, and wind speed were modified to $0.7^{\circ}C$, 0.4 hPa, 5.9%, and $4.6m\;s^{-1}$, respectively, through standard deviation analysis of step difference according to their averaging period. The modified quality control system was applied to the meteorological data observed by the Weather Information Service Engine in March 2014 and exhibited improved performance compared to the KMA procedures.