• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1773-1784
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• 2021

In order to minimize damage to oil spill accidents in the ocean, it is essential to collect a spilled area as soon as possible. Thus satellite-based remote sensing is a powerful source to detect oil spills in the ocean. With the recent rapid increase in the number of available satellites, it has become possible to generate a status report of marine oil spills soon after the accident. In this study, the oil spill area was calculated using various satellite images for the Symphony oil spill accident that occurred off the coast of Qingdao Port, China, on April 27, 2021. In particular, improving the accuracy of oil spill area determination was applied using high-resolution commercial satellite images with a spatial resolution of 2m. Sentinel-1, Sentinel-2, LANDSAT-8, GEO-KOMPSAT-2B (GOCI-II) and Skysat satellite images were collected from April 27 to May 13, but five images were available considering the weather conditions. The spilled oil had spread northeastward, bound for coastal region of China. This trend was confirmed in the Skysat image and also similar to the movement prediction of oil particles from the accident location. From this result, the look-alike patch observed in the north area from the Sentinel-1A (2021.05.01) image was discriminated as a false alarm. Through the survey period, the spilled oil area tends to increase linearly after the accident. This study showed that high-resolution optical satellites can be used to calculate more accurately the distribution area of spilled oil and contribute to establishing efficient response strategies for oil spill accidents.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• 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.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.36-58
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• 2022

In this study, the weather conditions corresponding to the increase in the environmental concentration of fine dust (PM₁₀) and ultrafine dust (PM_2.5) from 2001 to 2019 in Jeju and Seogwipo cities were analyzed. The increase in the levels of PM₁₀ and PM_2.5 was observed in the order: spring > winter > autumn > summer. In both cities, PM₁₀ and PM_2.5 levels increased more frequently during the day in spring and summer and at night in autumn and winter, with PM_2.5 showing a greater increase in concentration than PM₁₀. The air temperature and wind speed corresponding with increased levels of PM₁₀ were higher than their respective seasonal averages in spring and winter, but lower in summer and autumn. Relative humidity was lower than the seasonal average during all seasons. The air temperature variation corresponding with increased levels of PM_2.5 showed the same seasonal trend as that observed for PM₁₀. The relative humidity was higher than the respective seasonal averages in spring and summer, and lower in winter. The wind speed was lower than the seasonal average in both the cities. When the PM₁₀ and PM_2.5 levels increased, the wind direction was from the north and the west during the day and varied according to the season at night. The rate of the increase in the PM₁₀ concentration was the highest in both cities at the wind speed of 1.6 - 3.4 ms^-1 during the day and night except during night in the summer. The highest concentration of PM_2.5 was observed with the wind speed range of 1.6 - 3.4 ms^-1 in Jeju, and 0.3 - 1.6 ms^-1 in Seogwipo. The results of this study applied to urban and landscape planning will aid in the formulation of strategies to reduce the adverse effects of fine particular matter.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of Practical Agriculture & Fisheries Research
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• v.10 no.1
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• pp.43-58
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• 2008

This research was conducted to determine the alternative tool of chemical spray for rice cultivation using the unmanned helicopter(Yamaha, R-Max Type 2G-remote controlled system) at farmer's field in Korea. The unmanned helicopter tested was introduced form Japan. In Korea the application of chemicals by machine sprayer for pest management in rice cultivation has been ordinarily used at the farmer's level. However, it involved a relatively high cost and laborious for the small scale of cultivation per farm household. Farm population has been highly decreased to 7.5% in 2002 and the population is expected to rapidly reduce by 3.5% in 2012. In Japan, pest control depending on unmanned helicopter has been increased by leaps and bounds. This was due in part to the materialization of the low-cost production technology under agricultural policy and demand environmentally friendly farm products. The practicability of the unmanned helicopter in terms of super efficiency and effectiveness has been proven, and the farmers have understood that the unmanned helicopter is indispensable in the future farming system that they visualized. Also, the unmanned helicopter has been applied to rice, wheat, soybean, vegetables, fruit trees, pine trees for spraying chemicals and/or fertilizers in Japan Effect of disease control by unmanned helicopter was partially approved against rice blast and sheath blight. However, the result was not satisfactory due to the weather conditions and cultural practices. The spray density was also determined in this experiment at 0, 15, 30, and 60cm height from the paddy soil surface and there was 968 spots at 0cm, 1,560 spots at 15cm, 1,923 spots at 30cm, and 2,999 spots at 60cm height. However, no significant difference was found among the treatments. At the same time, there was no phytotoxicity observed under the chemical stray using this unmanned helicopter, nor the rice plant itself was damaged by the wind during the operation.

• Research in Plant Disease
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• v.28 no.4
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• pp.221-230
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• 2022

The fire blight caused by Erwinia amylovora (Ea) was first reported in 2015 in Korea, and the disease has rapidly spread to 22 regions until 2021. In Korea, all host plants in the apple and pear orchards where fire blight occurred should be eliminated and buried by the Plant Protection Act. To prevent the spread of the disease, all burial sites were prohibited from planting the new host plants for the next three years. To confirm the eradication efficiency of Ea and the reoccurrence of fire blight, the surveillance facilities were established on three burial sites from 2019 to 2020 in Anseong-si, Gyeonggi-do, and Chungju-si, Chungcheongbuk-do. As host plants, five apple trees of fire blight-susceptible cultivar 'Fuji', were planted in each facility. All facilities were enclosed with fences and nets and equipped with two CCTVs, motion sensors, and several other sensors for recording weather conditions to monitor the environment of the sentinel plants in real-time. The sentinel plants were checked for the reoccurrence of fire blight routinely. Suspicious plant parts were collected and analyzed for Ea detection by loop-mediated isothermal amplification polymerase chain reaction and conventional polymerase chain reaction. Until November 2022, Ea has not been detected in all sentinel plants. These results might support that the burial control of infected plants in soil works efficiently to remove Ea and support the possibility to shorten the prohibition period of host plant establishment in the burial sites.

• 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.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.497-508
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• 2023

Agricultural reservoirs are crucial structures for water resources monitoring especially in Korea where the resources are seasonally unevenly distributed. Optical and Synthetic Aperture Radar (SAR) satellites, being utilized as tools for monitoring the reservoirs, have unique limitations in that optical sensors are sensitive to weather conditions and SAR sensors are sensitive to noises and multiple scattering over dense vegetations. In this study, we tried to improve water body detection accuracy through optical-SAR data fusion, and quantitatively analyze the complementary effects. We first detected water bodies at Edong, Cheontae reservoir using the Compact Advanced Satellite 500(CAS500), Kompsat-3/3A, and Sentinel-2 derived Normalized Difference Water Index (NDWI), and SAR backscattering coefficient from Sentinel-1 by K-means clustering technique. After that, the improvements in accuracies were analyzed by applying K-means clustering to the 2-D grid space consists of NDWI and SAR. Kompsat-3/3A was found to have the best accuracy (0.98 at both reservoirs), followed by Sentinel-2(0.83 at Edong, 0.97 at Cheontae), Sentinel-1(both 0.93), and CAS500(0.69, 0.78). By applying K-means clustering to the 2-D space at Cheontae reservoir, accuracy of CAS500 was improved around 22%(resulting accuracy: 0.95) with improve in precision (85%) and degradation in recall (14%). Precision of Kompsat-3A (Sentinel-2) was improved 3%(5%), and recall was degraded 4%(7%). More precise water resources monitoring is expected to be possible with developments of high-resolution SAR satellites including CAS500-5, developments of image fusion and water body detection techniques.