• Korean Journal of Soil Science and Fertilizer
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• v.42 no.1
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• pp.21-28
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• 2009

Detailed information on soil characteristics is of great importance for the use and conservation of soil resources that are essential for human welfare and ecosystem sustainability. This paper introduces soil inventory of Korea focusing on national soil database establishment, information systems, use, and future direction for natural resources management. Different scales of soil maps surveyed and soil test data collected by RDA (Rural Development Administration) were computerized to construct digital soil maps and database. Soil chemical properties and heavy metal concentrations in agricultural soils including vulnerable agricultural soils were investigated regularly at fixed sampling points. Internet-based information systems for soil and agro-environmental resources were developed based on 'National Soil Survey Projects' for managing soil resources and for providing soil information to the public, and 'Agroenvironmental Change Monitoring Project' to monitor spatial and temporal changes of agricultural environment will be opened soon. Soils data has a great potential of further application in estimation of soil carbon storage, water capacity, and soil loss. Digital mapping of soil and environment using state-of-the-art and emerging technologies with a pedometrics concept will lead to future direction.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.3
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• pp.483-494
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• 2019

From the cosmopolitan superiority of the as the first world map completed in 1402 with surprisingly detailed images and contents on the Africa Continent it is reasonable to think that the Koreans in early fifteen century were already with highly up-to-date perspectives on the universe and world history and cultures. However, some 490 year later the first phytohydrographic plankton investigation in the neritic seas of Korea was performed by a Japanese company with sampling points covering from Tokyo Bay through Jeju neritic waters to Shanghai estuary, which was in turn preceded by the first oceanographic investigation other than the simple mapping Koreans seas by using two French sailboats. The first phytohydrographic plankton investigation in Korean seas were behind the world first oceanic plankton exploration, the German Plankton Expedition, by 25 years. Starting from the oceanographic investigation including phytohydrographic samplings in the whole Yellow Sea in 1915 the full-scale phytohydrographic plankton studies were tried in Korean seas which is well represented by the 1921 oceanographic investigation on the whole East Sea with 80 sampling stations. In 1932 two separate oceanographic investigations followed, one in the East Sea where 78 stations from Busan to southern Sakhalin Island were simultaneously visited by 50 research vessels for the physical, chemical, and biological oceanographic studies, and the other one in southern coast and western East Sea of Korea where ocean current observation as well as plankton sampling were made in 120 stations to understand the relationship between the ocean current and plankton distribution in the region. In 1933-1934 more intensified investigations on phytohydrography were carried out particularly in the East Sea as an integral part of the basic marine ecosystem studies for the Myeong-Tae (Alaska Pollock) resources estimation. Scientists' attitude for the marine investigation and research activities seemed to be almost unchanging even to the year 1943, which could be reflected by the fact that publication of the results from the investigations performed in 1945 were finally done in 1967 at Tokyo. Some 70 years later from the mid-twenty century we might be standing on the turning-point of "need to be prepared" for the new era of changing paradigm by reviewing, archiving, and analyzing the prior information big data from the previous ocean observation and biohydrographic investigations. At the same time each professional societies for the above mentioned sciences might trigger a continuous project to reorganize and update the records on related bibliography and its history every 30 years.

• The Journal of the Petrological Society of Korea
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• v.28 no.3
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• pp.171-193
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• 2019

This study aims to identify the geometry and internal structures of the Yeongdeok Fault, a branch fault of the Yangsan Fault, by detailed mapping and to characterize its kinematics by analyzing the attitudes of sedimentary rocks adjacent to the fault, slip data on the fault surfaces, and anisotropy of magnetic susceptibility (AMS) of the fault gouges. The Yeongdeok Fault, which shows a total extension of 40 km on the digital elevation map, cuts the Triassic Yeongdeok Granite and the Cretaceous sedimentary and volcanic rocks with about 8.1 km of dextral strike-slip offset. The NNW- or N-S-striking Yeongdeok Fault runs as a single fault north of Hwacheon-ri, Yeongdeok-eup, but south of Hwacheon-ri it branches into two faults. The western one of these two faults shows a zigzag-shaped extension consisting of a series of NNE- to NE- and NNW-striking segments, while the eastern one is extended south-southeastward and then merged with the Yangsan Fault in Gangu-myeon, Yeongdeok-gun. The Yeongdeok Fault dips eastward with an angle of > $65^{\circ}$ at most outcrops and shows its fault cores and damage zones of 2~15 m and of up to 180 m wide, respectively. The fault cores derived from several different wall rocks, such as granites and sedimentary and volcanic rocks, show different deformation patterns. The fault cores derived from granites consist mainly of fault breccias with gouge zones less than 10 cm thick, in which shear deformation is concentrated. While the fault cores derived from sedimentary rocks consist of gouges and breccia zones, which anastomose and link up each other with greater widths than those derived from granites. The attitudes of sedimentary rocks adjacent to the fault become tilted at a high angle similar to that of the fault. The fault slip data and AMS of the fault gouges indicate two main events of the Yeongdeok Fault, (1) sinistral strike-slip under NW-SE compression and then (2) dextral strike-slip under NE-SW compression, and shows the overwhelming deformation feature recorded by the later dextral strike-slip. Comparing the deformation history and features of the Yeongdeok Fault in the study area with those of the Yangsan Fault of previous studies, it is interpreted that the two faults experienced the same sinistral and dextral strike-slip movements under the late Cretaceous NW-SE compression and the Paleogene NE-SW compression, respectively, despite the slight difference in strike of the two faults.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.21-36
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• 2019

Dead fuel moisture content is a key variable in fire danger rating as it affects fire ignition and behavior. This study evaluates simple regression models estimating the moisture content of standardized 10-h fuel stick (10-h FMC) at three sites with different characteristics(urban and outside/inside the forest). Equilibrium moisture content (EMC) was used as an independent variable, and in-situ measured 10-h FMC was used as a dependent variable and validation data. 10-h FMC spatial distribution maps were created for dates with the most frequent fire occurrence during 2013-2018. Also, 10-h FMC values of the dates were analyzed to investigate under which 10-h FMC condition forest fire is likely to occur. As the results, fitted equations could explain considerable part of the variance in 10-h FMC (62~78%). Compared to the validation data, the models performed well with R2 ranged from 0.53 to 0.68, root mean squared error (RMSE) ranged from 2.52% to 3.43%, and bias ranged from -0.41% to 1.10%. When the 10-h FMC model fitted for one site was applied to the other sites, $R^2$ was maintained as the same while RMSE and bias increased up to 5.13% and 3.68%, respectively. The major deficiency of the 10-h FMC model was that it poorly caught the difference in the drying process after rainfall between 10-h FMC and EMC. From the analysis of 10-h FMC during the dates fire occurred, more than 70% of the fires occurred under a 10-h FMC condition of less than 10.5%. Overall, the present study suggested a simple model estimating 10-h FMC with acceptable performance. Applying the 10-h FMC model to the automatic mountain weather observation system was successfully tested to produce a national-scale 10-h FMC spatial distribution map. This data will be fundamental information for forest fire research, and will support the policy maker.

• Korean Journal of Breeding Science
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• v.50 no.4
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• pp.387-395
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• 2018

This study was conducted to identify DNA markers related to resistance to herbicide containing mesotrione in Tongil type rice. Two Tongil type elite lines; Milyang154 and Suweon382, showed resistance to mesotrione, whereas the others were susceptible at 20 days after mesotrione application, and severe growth inhibition was observed in the remaining 13 lines. As a result of analysis of mesotrione resistance using 190 $F_2$ populations derived from a cross of Hanareum2 (susceptible) and Milyang154 (resistant), the mesotrione resistance locus was shown to be a single dominant gene with a 3:1 segregation ratio ($X^2=1.19$, P=0.31). To identify a DNA marker closely linked to the mesotrione resistance gene, bulked segregant analysis (BSA) was adopted. The DNA marker RM3501 was identified on chromosome 2 with a recombinant value of 0.53 to the mesotrione resistance gene. Mst1(t) was located between SSR (simple sequence repeat) markers RM3501 and RM324 with a physical map distance of 10.2 Mb-11.4 Mb on chromosome 2. The band pattern of agarose gel electrophoresis of the SSR marker RM3501 showed the same segregation pattern with respect to mesotrione treatment in 20 Tongil type varieties and a $BC_2F_2$ segregation population derived from a cross between Unkwang (resistant) and Hanareum2 (susceptible). Thus, the RM3501 DNA marker could be used in breeding programs for Marker Assisted Selection in mesotrione resistant rice breeding.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.64-73
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• 2021

The purpose of this study is to present a method to assess the landscape aesthetic value of Bukhansan National Park using geotagged images that have been shared on social media sites. The method presented in this study consisted mainly of collecting geotagged image data, identifying landscape images, and analyzing the cumulative visibility by applying a target probability index. Ramblr is an application that supports outdoor activities with many users in Korea, from which a total of 110,954 geotagged images for Bukhansan National Park were collected and used to assess the landscape aesthetics. The collected geotagged images were interpreted using the Google Vision API, and were subsequently were divided into 11 landscape image types and 9 non-landscape image types through cluster analysis. As a result of analyzing the landscape types of Bukhansan National Park based on the extracted landscape images, landscape types related to topographical characteristics, such as peaks and mountain ranges, accounted for the largest portion, and forest landscapes, foliage landscapes, and waterscapes were also commonly found as major landscape types. In the derived landscape aesthetic value map, the higher the elevation and slope, the higher the overall landscape aesthetic value, according to the proportion and characteristics of these major landscape types. However, high landscape aesthetic values were also confirmed in some areas of lowlands with gentle slopes. In addition, the Bukhansan area was evaluated to have higher landscape aesthetics than the Dobongsan area. Despite the high elevation and slope, the Dobongsan area had a relatively low landscape aesthetic value. This shows that the aesthetic value of the landscape is strongly related not only to the physical environment but also to the recreational activities of visitors who are viewing the scenery. In this way, the landscape aesthetics assessment using the cumulative visibility of geotagged images is expected to be useful for planning and managing the landscape of Bukhansan National Park in the future, through allowing the geographical understanding of the landscape values based on people's perceptions and the identification of the regional deviations.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.