• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1283-1297
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• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.91-107
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• 2018

It is required to monitor and assess the desertification in Tunisia, where the Sahara Desert, which is located in the southern part of Tunisia, is recently expanding northward. In this study, by using remote sensed data, land cover changes were examined, and the Normalized Difference Vegetation Index (NDVI), Topsoil Grain Size Index (TGSI) and Albedo are used to monitor and assess desertification in Tunisia. Decision Tree was constructed, and the frequencies and trends of each assessment indicator, desertification degree and land cover were identified. In addition, we analyzed the correlation between assessment indicators and precipitation. As a result, desertification is generally intensifying northward, especially in areas with high levels of desertification. Also, bivariate correlation analysis showed that Albedo, NDVI and TGSI were all highly correlated with precipitation. It indicates that changes in precipitation have also been shown to affect Tunisian desertification. In conclusion, this study has improved the usability of various methodologies considering the assessment indicators based on satellite imagery, Decision Tree, which is a method of evaluating them complexly, and trends of land cover change.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.105-115
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• 2007

Data mining extracts interesting knowledge from a large database. Among numerous data mining techniques, research work is primarily concentrated on clustering and association rules. The clustering technique of the active research topics mainly deals with analyzing spatial and attribute data. And, the technique of association rules deals with identifying frequent patterns. There was an advanced apriori algorithm using an existing bit-clustering algorithm. In an effort to identify an alternative algorithm to improve apriori, we investigated FP-Growth and discussed the possibility of adopting bit-clustering as the alternative method to solve the problems with FP-Growth. FP-Growth using bit-clustering demonstrated better performance than the existing method. We used chess data in our experiments. Chess data were used in the pattern mining evaluation. We made a creation of FP-Tree with different minimum support values. In the case of high minimum support values, similar results that the existing techniques demonstrated were obtained. In other cases, however, the performance of the technique proposed in this paper showed better results in comparison with the existing technique. As a result, the technique proposed in this paper was considered to lead to higher performance. In addition, the method to apply bit-clustering to GML data was proposed.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.379-394
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• 2021

Air pollution data in South Korea is provided on a real-time basis by Air Korea stations since 2005. Previous studies have shown the feasibility of gridding air pollution data, but they were confined to a few cities. This paper examines the creation of nationwide gridded maps for PM10 concentration using 333 Air Korea stations with variogram optimization and ordinary kriging. The accuracy of the spatial interpolation was evaluated by various sampling schemes to avoid a too dense or too sparse distribution of the validation points. Using the 114,745 matchups, a four-round blind test was conducted by extracting random validation points for every 365 days in 2019. The overall accuracy was stably high with the MAE of 5.697 ㎍/m³ and the CC of 0.947. Approximately 1,500 cases for high PM10 concentration also showed a result with the MAE of about 12 ㎍/m³ and the CC over 0.87, which means that the proposed method was effective and applicable to various situations. The gridded maps for daily PM10 concentration at the resolution of 0.05° also showed a reasonable spatial distribution, which can be used as an input variable for a gridded prediction of tomorrow's PM10 concentration.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.167-176
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• 2022

We designed a borehole deviation survey tool applicable for steel-cased holes, K-DEV, and developed a prototype for a depth of 500 m aiming to development of own equipment required to secure deep subsurface characterization technologies. K-DEV is equipped with sensors that provide digital output with verified high performance; moreover, it is also compatible with logging winch systems used in Korea. The K-DEV prototype has a nonmagnetic stainless steel housing with an outer diameter of 48.3 mm, which has been tested in the laboratory for water resistance up to 20 MPa and for durability by running into a 1-km deep borehole. We confirmed the operational stability and data repeatability of the prototype by constantly logging up and down to the depth of 600 m. A high-precision micro-electro-mechanical system (MEMS) gyroscope was used for the K-DEV prototype as the gyro sensor, which is crucial for azimuth determination in cased holes. Additionally, we devised an accurate trajectory survey algorithm by employing Unscented Kalman filtering and data fusion for optimization. The borehole test with K-DEV and a commercial logging tool produced sufficiently similar results. Furthermore, the issue of error accumulation due to drift over time of the MEMS gyro was successfully overcome by compensating with stationary measurements for the same attitude at the wellhead before and after logging, as demonstrated by the nearly identical result to the open hole. We believe that the methodology of K-DEV development and operational stability, as well as the data reliability of the prototype, were confirmed through these test applications.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.121-137
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• 2020

Data from the Ozone Monitoring Instrument (OMI) satellite and national emission inventories were used in this study to analyze air quality in East Asia and estimate the impact of domestic and foreign emissions on South Korea's air quality, based on which future emissions were predicted. The concentration trends of nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) in East Asia from 2005 to 2015 showed that both substances were highest in North East China (NEC), followed by South East China (SEC) and Seoul Metropolitan Area (SMA). The average SO₂ concentration was 1.63 times higher in NEC than in SMA. Analysis on the ratios of NO₂/SO₂ and NO_x/SO_x provides an indirect picture of the effect of transboundary air pollutants on atmospheric composition in Korea. The concentration ratio of NO₂/SO₂ in all study areas peaked in 2013 and SMA's emission ratio of NO_x/SO_x increased in 2015 by over 22% from 2013. Despite the reduction in domestic emissions, the concentration-to-emission ratios (NO₂/NO_x, SO₂/SO_x) rose gradually, which implies that other factors besides domestic emissions (e.g., foreign sources, lifetime, etc.) influence air quality in SMA. We estimated future emissions of NO_x and SO_x in SMA to be 296.2 and 39.0 ktons in 2025 and 284.4 and 33.8 ktons in 2035, respectively. Application of the inter-comparison techniques of this study to the data from the Geostationary Environment Monitoring Instrument (GEMS) is expected to provide concrete information which can be used to improve national emission inventories and figure out factors and sources that affect domestic air quality.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.3
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• pp.220-237
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• 2021

Since the functional importance of marine phytoplankton was firstly advocated from early 1880s massive data on the species composition and abundance were produced by classical microscopic observation and the advanced auto-imaging technologies. Recently, pigment composition resulted from direct chemical analysis of phytoplankton samples or indirect remote sensing could be used for the group-specific quantification, which leads us to more diversified data production methods and for more improved spatiotemporal accessibilities to the target data-gathering points. In quite a few cases of many long-term marine ecosystem monitoring programs the phytoplankton species composition and abundance was included as a basic monitoring item. The phytoplankton data could be utilized as a crucial evidence for the long-term change in phytoplankton community structure and ecological functioning at the monitoring stations. Usability of the phytoplankton data sometimes is restricted by the differences in data producers throughout the whole monitoring period. Methods for sample treatments, analyses, and species identification of the phytoplankton species could be inconsistent among the different data producers and the monitoring years. In-depth study to determine the precise quantitative values of the phytoplankton species composition and abundance might be begun by Victor Hensen in late 1880s. International discussion on the quality assurance of the marine phytoplankton data began in 1969 by the SCOR Working Group 33 of ICSU. Final report of the Working group in 1974 (UNESCO Technical Papers in Marine Science 18) was later revised and published as the UNESCO Monographs on oceanographic methodology 6. The BEQUALM project, the former body of IPI (International Phytoplankton Intercomparison) for marine phytoplankton data QA/QC under ISO standard, was initiated in late 1990. The IPI is promoting international collaboration for all the participating countries to apply the QA/QC standard established from the 20 years long experience and practices. In Korea, however, such a QA/QC standard for marine phytoplankton species composition and abundance data is not well established by law, whereas that for marine chemical data from measurements and analysis has been already set up and managed. The first priority might be to establish a QA/QC standard system for species composition and abundance data of marine phytoplankton, then to be extended to other functional groups at the higher consumer level of marine food webs.