• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.1-13
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• 2005

In the process of a hydrology circulation, evapotranspiration is considered a very important factor to build a plan for the development of water resources and to operate water resources system. This study purposes to estimate daily potential evapotranspiration quantity in consideration of energy factors of the surface by using spatial information such as Landsat TM (ETM+) data, DEM and Landcover. Kyounan-cheon, Han River is selected as a target area, and landcover is divided by vegetation and non-vegetation covered area. Penman-Monteith equation which considers leaf-area index is used to estimate potential evapotranspiration quantity of vegetation covered area. The combination method (energy burget and aerodynamic method) is used in non-vegetation covered area. Among the input data for estimating potential evapotranspiration, NDVI, SR and Albedo is formed by Landsat, TM and ETM+ from 1986 through 2002. ground heat flux is estimated by using NDVI distribution map, LAI distribution map is drawn by using SR distribution map. The result of estimation shows that the average potential evapotranspiration in the whole basin is about 1.8-3.2mm/day per each cell. THe results of estimating potential evapotranspiration quantity by each landcover are as follows; water surface 3.6-4.9mm/day, city 1.4-3.1mm/day, bareland 1.4-3.5mm/day, grassland 1.7-3.7mm/day, forest 1.7-3.0mm/day and farmland 1.8-3.6mm/day. The potential evapotranspiration quantity is underestimated in comparison with observed evaporation data by evaporation pan, but it is considered that it has physical propriety.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.259-267
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• 2013

GIS-based web map service is all the more accessible to the public. Among others, location query services are most frequently utilized, which are currently restricted to only one keyword search. Although there increases the demand for the service for querying multiple keywords corresponding to sequential activities(banking, having lunch, watching movie, and other activities) in various locations POI, such service is yet to be provided. The objective of the paper is to develop the k-IPS algorithm for quickly and accurately querying multiple POIs that internet users input and locating the search outcomes on a web map. The algorithm is developed by utilizing hierarchical tree structure of $R^*$-tree indexing technique to produce overlapped geometric regions. By using recursive $R^*$-tree index based spatial join process, the performance of the current spatial join operation was improved. The performance of the algorithm is tested by applying 2, 3, and 4 multiple POIs for spatial query selected from 159 keyword set. About 90% of the test outcomes are produced within 0.1 second. The algorithm proposed in this paper is expected to be utilized for providing a variety of location-based query services, of which demand increases to conveniently support for citizens' daily activities.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1389-1403
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• 2021

Disaster management using VHR (very high resolution) satellite images supports rapid damage assessment and also offers detailed information of the damages. However, the acquisition of pre-event VHR satellite images is usually limited due to the long revisit time of VHR satellites. The absence of the pre-event data can reduce the accuracy of damage assessment since it is difficult to distinguish the changed region from the unchanged region with only post-event data. To address this limitation, in this study, we conducted the wildfire-induced change detection on national wildfire cases using post-fire VHR satellite images and GIS (Geographic Information System) data. For GIS data, a national land cover map was selected to simulate the pre-fire NIR (near-infrared) images using the spatial information of the pre-fire land cover. Then, the simulated pre-fire NIR images were used to analyze bi-temporal NDVI (Normalized Difference Vegetation Index) correlation for unsupervised change detection. The whole process of change detection was performed on a superpixel basis considering the advantages of superpixels being able to reduce the complexity of the image processing while preserving the details of the VHR images. The proposed method was validated on the 2019 Gangwon wildfire cases and showed a high overall accuracy over 98% and a high F1-score over 0.97 for both study sites.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.389-395
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• 2014

This technical note describes about the base stages of technology implementation for establishing "Early Warning System for Weather Hazard Management in Climate-smart Agriculture" to national onsite service. First of all, a special weather report service at catchment was represented sequential risk of 810 units of catchment by spatial statistical methods to existing 150 counties units special weather report released in KMA. The second, chronic hazard alarm service based on daily data of 76 Synoptic stations was monitor about 810 Catchment of mid-long term lapse weather and represented as a relative risk index chronic hazard risk of this time in preparation for the climatological normal conditions in the same period. Finally, we establish the foundation for delivering individually calculated field specific in hazard risk about volunteer farmer of early warning service demonstration area in seomjin downstream watershed. These three types of information were built a near real-time map service on the VWORLD background map of Ministry of Land as superposed layers nationwide catchment and demonstration areas within the farm unit weather hazard.

• Journal of the Korean GEO-environmental Society
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• v.19 no.10
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• pp.13-19
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• 2018

Following the 2016 Gyeongju earthquake, the Pohang Earthquake occurred in 2017, and the south-east region in Korea is under the threat of an earthquake. Especially, in the Pohang Earthquake, the liquefaction phenomenon occurred in the sedimentation area of the coast, and preparation of countermeasures is very important. The soil liquefaction can affect the underground facilities directly as well as various structures on the ground. Therefore, it is necessary to identify the liquefaction risk of facilities and the structures against the possible earthquakes and to prepare countermeasures to minimize them. In this study, we investigated the seismic liquefaction risk about the electric power utility tunnels in the southeast area where the earthquake occurred in Korea recently. In the analysis of seismic liquefaction risk, the earthquake with return period 1000 years and liquefaction potential index are used. The liquefaction risk analysis was conducted in two stages. In the first stage, the liquefaction risk was analyzed by calculating the liquefaction potential index using the ground survey data of the location of electric power utility tunnels in the southeast region. At that time, the seismic amplification in soil layer was considered by soil amplification factor according to the soil classification. In the second stage, the liquefaction risk analysis based on the site response analyses inputted 3 earthquake records were performed for the locations determined to be dangerous from the first step analysis, and the final liquefaction potential index was recalculated. In the analysis, the site investigation data were used from the National Geotechnical Information DB Center. Finally, it can be found that the proposed two stage assessments for liquefaction risk that the macro assessment of liquefaction risk for the underground facilities including the electric power utility tunnel in Korea is carried out at the first stage, and the second risk assessment is performed again with site response analysis for the dangerous regions of the first stage assessment is reasonable and effective.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.4
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• pp.513-525
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• 2019

In Seoul, the subway system has been in use since 1974 and is the most frequently used travel mode accounting for approximately 40% of passenger journeys in 2015. As such the subway system is widely adopted by people and therefore, can have great impacts on their everyday life. However, it is easily noted that transit resources are not distributed spatially uniform, in other words, not all parts of the city gain the same benefits from their networks. This study aims to examine the inequity of spatial distribution of subway networks based on accessibility. Accessibility of subway networks are calculated based on the time-distance between stations and then equity is measured using the Gini index. Resulted map of subway accessibility shows that the benefits are not evenly distributed in Seoul with a pattern of highly accessible core - less accessible periphery areas. Also the subway accessibility network has fairer distribution against the employees' distribution rather than the distribution of general population or possibly transit dependent groups.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.29-38
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• 2010

It is necessary to develop an integration model which can account for various data acquired at different measurement scales in environmental thematic mapping with high-resolution ground survey data and relatively low-resolution remote sensing data. This paper presents and applies a multi-scale geostatistical methodology for downscaling of thematic maps generated from lowresolution remote sensing data. This methodology extends a traditional ordinary kriging system to a block kriging system which can account for both ground data and remote sensing data which can be regarded as point and block data, respectively. In addition, stochastic simulation based on block kriging is also applied to describe spatial uncertainty attached to the downscaling. Two downscaling experiments including SRTM DEM and MODIS Leaf Area Index (LAI) products were carried out to illustrate the applicability of the geostatistical methodology. Through the experiments, multi-scale geostatistics based on block kriging successfully generated relatively high-resolution thematic maps with reliable accuracy. Especially, it is expected that multiple realizations generated from simulation would be effectively used as input data for investigating the effects of uncertain input data on GIS model outputs.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.369-380
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• 2021

In this work, we analyzed the effects of drought on the water temperature (WT) of Nakdong river basin major river sections using Standardized Precipitation Index (SPI) and WT data. The analysis was carried out on a seasonal basis. After calculating the optimal time scale of the SPI through the correlation between the SPI and WT data, we used the copula theory to model the joint probability distribution between the WT and SPI on the optimal time scale. During spring and fall, the possibility of environmental drought caused by high WT increased in most of the river sections. Notably, in summer, the possibility of environmental drought caused by high WT increased in all river sections. On the other hand, in winter, the possibility of environmental drought caused by low WT increased in most river sections. From the risk map, which quantified the sensitivity of WT to the risk of environmental drought, the river sections Nakbon C, Namgang E, and Nakbon K showed increased stress in the water ecosystem due to high WT when drought occurred in summer. When drought occurred in winter, an increased water ecosystem stress caused by falling WT was observed in the river sections Gilan A, Yongjeon A, Nakbon F, Hwanggang B, Nakbon I, Nakbon J, Nakbon K, Nakbon L, and Nakbon M. The methodology developed in this study will be used in the future to quantify the effects of drought on water quality as well as WT.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.83-101
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• 2022

Accurate atmospheric correction is essential for the analysis of land surface and environmental monitoring. Aerosol optical depth (AOD) information is particularly important in atmospheric correction because the radiation attenuation by Mie scattering makes the differences between the radiation calculated at the satellite sensor and the radiation measured at the land surface. Thus, it is necessary to use high-quality AOD data for an appropriate atmospheric correction of high-resolution satellite images. In this study, we examined the Second Simulation of a Satellite Signal in the Solar Spectrum (6S)-based atmospheric correction results for the Sentinel-2 images in South Korea using raster AOD (MODIS) and single-point AOD (AERONET). The 6S result was overall agreed with the Sentinel-2 level 2 data. Moreover, using raster AOD showed better performance than using single-point AOD. The atmospheric correction using the single-point AOD yielded some inappropriate values for forest and water pixels, where as the atmospheric correction using raster AOD produced stable and natural patterns in accordance with the land cover map. Also, the Sentinel-2 normalized difference vegetation index (NDVI) after the 6S correction had similar patterns to the up scaled drone NDVI, although Sentinel-2 NDVI had relatively low values. Also, the spatial distribution of both images seemed very similar for growing and harvest seasons. Future work will be necessary to make efforts for the gap-filling of AOD data and an accurate bi-directional reflectance distribution function (BRDF) model for high-resolution atmospheric correction. These methods can help improve the land surface monitoring using the future Compact Advanced Satellite 500 in South Korea.