• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1797-1806
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• 2014

By far, groundwater management has been conducted by 'safe yield' policy based on the estimation of annual average of groundwater recharge throughout the world. However, as groundwater recharge show spatiotemporal variation, dynamic analysis must be carried out to evaluate the sustainable groundwater resources. In this study, an integrated surface-groundwater model, SWAT-MODFLOW was used to compute the spatial distribution of groundwater recharge in Gyungju region. Frequency analysis is adopted to evaluate the existing values of potential amount of groundwater development which is made by the 10 year drought frequency rainfall multiplied by recharge coefficient. The conservative methods for estimating recharge rates of 10 year drought frequency in subbains are newly suggested and compared with the existing values of potential amount of groundwater development. This process will promote the limitations for existing precesses used for computing potential amount of groundwater development.

• Journal of Korea Water Resources Association
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• v.35 no.4 s.129
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• pp.411-423
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• 2002

Accurate and real time forecasting of runoff has a high priority in the drainage basins prone to short, high intensity rainfall events causing flash floods. To take into account the resolution of hydrological variables within a drainage basin, use of distributed system models is preferred. The Landsat Thematic Mapper(TM) observations enable detailed information on distribution of land cover and other related factors within a drainage basin and permit the use of distributed system models. This paper describes monitoring technique of rainfall excess by SCS curve number method. The time series maps of rainfall excess were generated for all the storm events to show the spatiotemporal distribution of rainfall excess within study basin. A combination of the time series maps of rainfall excess with a flow routing technique would simulate the flow hydrograph at the drainage basin outlet.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.56-68
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• 2006

For tracing tag locations, the trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that a tag stays in a reader is missing from the trajectory represented only as a point, it is impossible to find the tag that remains in a reader. To solve this problem we propose the data model in which trajectories are defined as time-parameterized intervals and new index scheme called the Time Parameterized Interval R-tree. We also propose new insert and split algorithms that reduce the area of nodes to enable efficient query processing. We evaluate the performance of the proposed index scheme and compare it with previous indexes on various datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.767-785
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• 2021

With the rapid development of information technology, it is now possible to analyze the spatial patterns of cultivated land and its evolution by combining GIS, geostatistical analysis models and spatiotemporal big data for the dynamic monitoring and management of cultivated land resources. The spatial pattern of cultivated land and its evolutionary patterns in Luoyang City, China from 2009 to 2019 were analyzed using spatial autocorrelation and spatial autoregressive models on the basis of GIS technology. It was found that: (1) the area of cultivated land in Luoyang decreased then increased between 2009 and 2019, with an overall increase of 0.43% in 2019 compared to 2009, with cultivated land being dominant in the overall landscape of Luoyang; (2) cultivated land holdings in Luoyang are highly spatially autocorrelated, with the 'high-high'-type area being concentrated in the border area directly north and northeast of Luoyang, while the 'low-low'-type area is concentrated in the south and in the municipal area of Luoyang, and being heavily influenced by topography and urbanization. The expansion determined during the study period mainly took place in the Luoyang City, with most of it being transferred from the 'high-low'-type area; (3) elevation, slope and industrial output values from analysis of the bivariate spatial autocorrelation and spatial autoregressive models of the drivers all had significant effects on the amount of cultivated land holdings, with elevation having a positive effect, and slope and industrial output having a negative effect.

• Journal of Environmental Health Sciences
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• v.47 no.2
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• pp.144-154
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• 2021

Objectives: This study was intended to identify hazard contribution by region, media, and chemical by calculating a hazard-based index using pollutant release and transfer register (PRTR) data. Methods: PRTR data for the period 2011 to 2016 was analyzed to examine the regional trends in toxic releases in terms of quantity and to create a corresponding hazard-based index. For the hazard-based index, the Risk-Screening Environmental Indicators (RSEI) Model was used. Results: The results of the trend analysis show that total releases decreased slightly, but health hazard levels increased consistently. According to the outcome of regional contribution analysis of the hazard-based index, Chungcheongnam-do, Jeollabuk-do and Gyeonggi-do Provinces showed a high ratio in the index for air and water release pollutants, while Gyeongsangbuk-do and Gyeongsangnam-do Provinces showed a high ratio in the index of soil release and waste transfer pollutants. Also, as a result of the analysis of the top ranked substances in the hazard-based index, it was found that chromium, cobalt and its compounds, and ethylene oxide contributed greatly to air release substances, while chromium, benzene, and lead and its compounds contributed greatly to water release substances. Conclusion: These results showed considerable disparities between total release and health hazard levels, especially in the analysis of contribution by regions and by chemical substance. Therefore, the hazard-based index should be used both to support a more comprehensive and robust approach to screening of chemicals for environmental health policy and for management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.162-162
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• 2020

토양수분은 TDR(Time Domain Reflectometry)이나 Tensiometer 등의 장비를 이용하여 측정을 시행하고 있으나, 이를 위해서는 많은 인력과 경제적 자원이 소비될 뿐만 아니라 시공간적으로 측정할 수 있는 범위에 한계가 있다. 지상 관측의 대안으로 MIRAS(Microwave Imaging Radiometer with Aperture Synthesis)나 SMAP(Soil Moisture Active Passive), AMSR2(Advanced Microwave Scanning Radiometer 2) 등의 수동 마이크로파 위성 센서를 이용한 공간 토양수분 관측이 수행되었으나, 낮은 공간 해상도(9~36km)는 지역 규모의 토양수분 분포를 나타내기 충분하지 않고, 높은 불확실성을 내포하고 있다. 본 연구에서는 금강 상류의 용담댐 유역(930.0㎢)을 대상으로 Sentinel-1 C-band SAR(Synthetic Aperture Radar) 영상을 이용한 토지 피복 및 토양 속성을 고려한 10m 해상도의 토양수분 산출을 수행하였다. 용담댐 유역은 산림 79.7%, 논 9.0%, 밭 5.4%, 주거지 2.9%의 토지 피복 비율을 가지며 토양은 사양토(66.6%)와 양토(20.9%)가 우세하다. Sentinel-1 C-band SAR 영상은 SeNtinel Application Platform(SNAP)을 이용하여 전처리 후, 후방산란계수로 변환하였다. 토양수분 알고리즘은 TU-Wien change detection algorithm과 Regression model을 활용하였고, 검증을 위한 실측 토양수분 자료는 한국수자원공사(K-water)에서 제공하는 5년(2014~2018)간의 토양수분 관측자료를 이용하였다. 산출된 토양수분은 결정계수(Coefficient of determination, R²) 및 평균제곱근오차(Root Mean Square Error, RMSE)를 이용하여 실측 토양수분과 비교하였다. Sentinel-1 C-band SAR 영상을 이용한 고해상도의 토양수분 산출은 토지 피복 및 토양 속성을 고려한 지역 규모의 공간 토양수분 분포 및 시간적 변화를 표현 가능할 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.1
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• pp.66-77
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• 2005

We have investigated microclimatic controls on the spatiotemporal variations of net primary production (NPP) of a rugged forested watershed using the process-based biogeochemical model (BIOME-BGC). To validate the model simulation of water and carbon cycles at the plot scale, we have conducted field survey over deciduous broadleaf forest (DBF) and evergreen needleleaf forest (ENF) since 2000. The modeled values of soil temperature, soil moisture and soil respiration showed high correlation with those from the field measurements. The modeled seasonal changes of NPP showed high correlation with air temperature but no significant correlation with water related parameters. The precipitation frequency turned out to be the best climatic factor to explain the annual variation of NPP. Furthermore, NPP of ENF was more sensitive to precipitation frequency than that of DBF. With changes in vegetation cover and topography, the spatial distribution of NPP was of great heterogeneity, which was negatively correlated with the magnitude of NPP. Despite the annual precipitation of 1,400mm, NPP at the study site was constrained by the amount of water available for the vegetation. Such a modeling result should be verified by the field measurements.

• Korean Journal of Optics and Photonics
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• v.31 no.2
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• pp.59-70
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• 2020

We discuss a modified numerical model based on the Monte Carlo radiative transfer (MCRT) method, i.e., the MCRT matrix method, for the analysis of atmospheric scattering effects in three-dimensional flash LIDAR systems. Based on the MCRT method, the radiative transfer function for a LIDAR signal is constructed in a form of a matrix, which corresponds to the characteristic response. Exploiting the superposition and convolution of the characteristic response matrices under the paraxial approximation, an extended computer simulation model of an overall flash LIDAR system is developed. The MCRT matrix method substantially reduces the number of tracking signals, which may grow excessively in the case of conventional Monte Carlo methods. Consequently, it can readily yield fast acquisition of the signal response under various scattering conditions and LIDAR-system configurations. Using the computational model based on the MCRT matrix method, we carry out numerical simulations of a three-dimensional flash LIDAR system operating under different atmospheric conditions, varying the scattering coefficient in terms of visible distance. We numerically analyze various phenomena caused by scattering effects in this system, such as degradation of the signal-to-noise ratio, glitches, and spatiotemporal spread and time delay of the LIDAR signals. The MCRT matrix method is expected to be very effective in analyzing a variety of LIDAR systems, including flash LIDAR systems for autonomous driving.