• Journal of the Korean earth science society
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• v.26 no.7
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• pp.624-629
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• 2005

Numerical data of the geological time scale in Earth Science I, II textbooks and those of University textbooks of Korea and other countries are briefly reviewed. Numerical data of the geologic time scale shown in Earth Science I, II textbooks are mostly out of date and many of them follow those in the University textbooks of Korea. The same situation is apparent for introductory Earth Science or Geology textbooks of other countries as old data exist in their text books as well. There are many new data in the International Stratigraphic Chart (ISC 2000) and International Geologic Time Scale (IGTS 2003) recently updated by International Commission on Stratigraphy (ICS) and A Geologic Time Scale (GTS 2004). Among the new data, some important things are Paleogene and Neogene Periods of Cenozoic Era, Mississippian and Pensilvanian Epochs of Carborniferous Period, Paleoproterozoic, Mesoproterozoic, and Neoproterozoic Eras of Proterozoic Eon, and Eoarchean, Paleoarchean, Mesoarchean, and Neoarchean Eras of Archean Eon. These new data should be used in the new Earth Science textbooks.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• Journal of Information Management
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• v.39 no.2
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• pp.211-234
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• 2008

Recently, the frequency and scale of natural disasters such as typhoons, flood, earthquakes, and tidal waves from earthquakes has been increasing. Several nations have recognized that earth observation is essential for protecting the Earth's environment. However, as the data format from earth observation varies depending on areas, institutes, and countries, sharing and exchange between data is difficult. Thus, we have a metadata standardization scheme suitable for the domestic situation to allow exchange of data between societal benefit areas with reference to principles of data sharing and exchange that are discussed on GEO (Group on Earth Observation). We have also designed metadata schemes required to identify the metadata situation of earth observation data being used for 9 societal benefit areas of GEOSS(Global Earth Observation System of Systems).

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.153-160
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• 2021

Land Surface Temperature (LST) is the radiological surface temperature which observed by satellite. It is very important factor to estimate condition of the Earth such as Global warming and Heat island. For these reasons, many countries operate their own satellite to observe the Earth condition. South Korea has many landcovers such as forest, crop land, urban. Therefore, if we want to retrieve accurate LST, we would use high-resolution satellite data. In this study, we made LSTs with 4 LST retrieval algorithms which are used widely with Landsat-8 data which has 30 m spatial resolution. We retrieved LST using equations of Price, Becker et al. Prata, Coll et al. and they showed very similar spatial distribution. We validated 4 LSTs with Moderate resolution Imaging Spectroradiometer (MODIS) LST data to find the most suitable algorithm. As a result, every LST shows 2.160 ~ 3.387 K of RMSE. And LST by Prata algorithm show the lowest RMSE than others. With this validation result, we choose LST by Prata algorithm as the most suitable LST to South Korea.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.85-95
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• 2021

Recently, as data mining and artificial neural network techniques are developed, analyzing large amounts of data is proposed to reduce the dimension of the data. In general, empirical orthogonal function (EOF) used to reduce the dimension in the ocean data and recently, Self-organizing maps (SOM) algorithm have been investigated to apply to the ocean field. In this study, both algorithms used the monthly Sea level anomaly (SLA) data from 1993 to 2018 around the East Asia Coasts. There was dominated by the influence of the Kuroshio Extension and eddy kinetic energy. It was able to find the maximum amount of variance of EOF modes. SOM algorithm summarized the characteristic of spatial distributions and periods in EOF mode 1 and 2. It was useful to find the change of SLA variable through the movement of nodes. Node 1 and 5 appeared in the early 2000s and the early 2010s when the sea level was high. On the other hand, node 2 and 6 appeared in the late 1990s and the late 2000s, when the sea level was relatively low. Therefore, it is considered that the application of the SOM algorithm around the East Asia Coasts is well distinguished. In addition, SOM results processed by SLA data, it is able to apply the other climate data to explain more clearly SLA variation mechanisms.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.280.1-285
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• 1998

The earth's environmental problems have attracted serious attention worldwide. Various kinds of environmental data, such as remote sensing data, have become available for examining. Although this data is crucial to understanding such problems, there has become an over-abundance in variety of size, format, and filetype which makes it difficult for researchers to handle. We feel that earth environmental researchers should not be burdened by such cumbersome tasks. Therefore, we are developing a digital library for earth environmental information and a VRML based data visualization system for it. Even now, content-based image retrieval systems have many problems attributed to the degree of difficulty in implementing them. Thus, we are trying to visualize this data so that researchers can utilize it more efficiently, effectively, and easily. A great advantage for VRML users is that people can see environmental data from any perspective above the earth and with any resolution easily. Also by using MPEG-movie, users can observe the changes of data drawn from time series files.

• Journal of the Korean earth science society
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• v.23 no.2
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• pp.188-193
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• 2002

Various research methods have been used in science depending on the various contexts. This implies that certain methods or inferences may be more frequently used in earth science. The purpose of the study are to explore the contexts of earth science, and the inferences frequently used in earth science. The context earth science research is quite different from that of other areas of natural science in terms of its time scale, space scale, accessibility, complexity, and controllability. The purpose of earth science research is twofold: historical and causal. The inferences frequently used in earth science are abduction and prediction. Abductive inferences go from the resulting state to controlling state. Predictive inferences go from hypothesis to expected data.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.487-501
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• 2019

In this study, steric height variability in the East Asian Seas (EAS) has been analyzed by using ocean reanalysis intercomparison project (ORA-IP) data. Results show that there are significant correlations between ocean reanalysis and satellite data except the phase of annual cycle and interannual signals of the Yellow Sea. Reanalysis ensemble derived from 15-different assimilation systems depicts higher correlation (0.706) than objective analysis ensemble (0.296) in the EAS. This correlation coefficient is also much higher than that of the global ocean (0.441). For the long-term variability of the thermosteric sea level during 1993-2010, a significant warming trend is found in the East/Japan Sea, while cooling trend is shown around the Kuroshio extension area. For the halosteric sea level, a dominant freshening trend is found in the EAS. However, below 300 m depth around this area, the signal-to-noise ratio of the linear trend is generally less than one, which is related to the low density of observation data.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.518-537
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• 2019

This study examined the implications of a place-based earth science program integrated with Mathematics. 11 pre-service earth science teachers and 22 middle school students participated in the service education activities of earth science for 30 hours focusing on the measurement of the earth's size through earth science experiments as part of the middle school curriculum. In order to minimize errors that may occur during the earth's size measurement experiments using Eratosthenes's shadows length method of the ancient Greek era, the actual data were collected after triangulation ratios were conducted in the locations of two middle schools: one in remote metropolitan and the other in rural area. The two schools' students shared the final estimate result. Through this process, they learned the mathematical method to express the actual data effectively. Participants, experienced the importance and difficulty of the repetitive and accurate data acquisition process, and also discussed the causes of errors included in the final results. It implies that a Place-Based Earth Science Program activity can contribute to students' increased-understanding of the characteristics of earth science inquiry and to developing their problem solving skills, thinking ability, and communication skills as well, which are commonly emphasized in science and mathematics in the 2015 reunion curriculum. It is expected that a place-based science program can provide a foundation for developing an integrated curriculum of mathematics and science.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.22-25
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• 2005

The 10 Year Implementation Plan for a Global Earth Observation System of Systems (GEOSS), which was endorsed at the Third Earth Observation Summit in Brussels in February, 2005, emphasizes the importance of data management facilities for diverse and large-volume Earth Observation data from inhomogeneous information sources. A three year research plan for addressing this key target of GEOSS has just approved as the first step by the Japanese government. The goals of this research are, (1) to develop a data management core system consisting of data integration and information fusion functions and interoperability and information service functions; (2) to establish data and information flows between data providers and users; (3) to promote application studies of data integration and information fusion, especially in the fields of weather forecasting, flood forecasting, agricultural management, and climate variability and changes. The research group involves leading scientists on information science and technology, who have been developing giant data archive servers, storage area networks, metadata models, ontology for the earth observations. They are closely cooperating with scientists on earth sciences, water resources management, and agriculture, and establishing an effective collaborative research framework.