• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.4
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• pp.71-80
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• 2009

Although desire for living without hazardous damages grows these days, threats from natural disasters which we are currently exposed to are quiet different from what we have experienced. To cope with this changing situation, it is necessary to assess the characteristics of the natural disasters. Therefore, the main purpose of this research is to suggest a methodology to estimate the potential property loss and assess the flood risk using a regional regression analysis. Since the flood damage mainly consists of loss of lives and property damages, it is reasonable to express the results of a flood risk assessment with the loss of lives and the property damages that are vulnerable to flood. The regional regression analysis has been commonly used to find relationships between regional characteristics of a watershed and parameters of rainfall-runoff models or probability distribution models. In our research, however, this model is applied to estimate the potential flood damage as follows; 1) a nonlinear model between the flood damage and the hourly rainfall is found in gauged regions which have sufficient damage and rainfall data, and 2) a regression model is developed from the relationship between the coefficients of the nonlinear models and socio-economic indicators in the gauged regions. This method enables us to quantitatively analyze the impact of the regional indicators on the flood damage and to estimate the damage through the application of the regional regression model to ungauged regions which do not have sufficient data. Moreover the flood risk map is developed by Flood Vulnerability Index (FVI) which is equal to the ratio of the estimated flood damage to the total regional property. Comparing the results of this research with Potential Flood Damage (PFD) reported in the Long-term Korea National Water Resources Plan, the exports' mistaken opinions could affect the weighting procedure of PFD, but the proposed approach based on the regional regression would overcome the drawback of PFD. It was found that FVI is highly correlated with the past damage, while PFD does not reflect the regional vulnerabilities.

• Korean journal of applied entomology
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• v.50 no.3
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• pp.235-245
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• 2011

Temperature-related parameters of Panonychus citri (McGregor) (Acarina: Tetranychidae) development were estimated and a stage-structured matrix model was developed. The lower threshold temperatures were estimated as $8.4^{\circ}C$ for eggs, $9.9^{\circ}C$ for larvae, $9.2^{\circ}C$ for protonymphs, and $10.9^{\circ}C$ for deutonymphs. Thermal constants were 113.6, 29.1, 29.8, and 33.4 degree days for eggs, larvae, protonymphs, and deutonymphs, respectively. Non-linear development models were established for each stage of P. citri. In addition, temperature-dependent total fecundity, age-specific oviposition rate, and age-specific survival rate models were developed for the construction of an oviposition model. P. citri age was categorized into five stages to construct a matrix model: eggs, larvae, protonymphs, deutonymphs and adults. For the elements in the projection matrix, transition probabilities from an age class to the next age class or the probabilities of remaining in an age class were obtained from development rate function of each stage (age classes). Also, the fecundity coefficients of adult population were expressed as the products of adult longevity completion rate (1/longevity) by temperature-dependent total fecundity. To evaluate the predictability of the matrix model, model outputs were compared with actual field data in a cool early season and hot mid to late season in 2004. The model outputs closely matched the actual field patterns within 30 d after the model was run in both the early and mid to late seasons. Therefore, the developed matrix model can be used to estimate the population density of P. citri for a period of 30 d in citrus orchards.

• Journal of the Korea Concrete Institute
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• v.14 no.1
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• pp.126-135
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• 2002

Plate bonding technique has been widely used in strengthening of existing concrete structures, although it has often a serious problem of premature falure such as interface separation and rip-off. However, this premature failure problem has not been well explored yet especially in view of local failure mechanism around the interface of plate ends. The purpose of the present study is, therefore, to identify the local failure of strengthened plates and to derive a separation criterion at the interface of plates. To this end, a comprehensive experimental program has been set up. The double lap pull-out tests considering pure shear force and half beam tests considering combined flexure-shear force were performed. The main experimental parameters include plate thickness, adhesive thickness, and plate end arrangement. The strains along the longitudinal direction of steel plates have been measured and the shear stress were calculated from those measures strains. The effects of plate thickness, bonded length, and plate end treatment have been also clarified from the present test results. Nonlinear finite element analysis has been performed and compared with test results. The Interface properties are also modeled to present the separation failure behavior of strengthened members. The cracking patterns as well as maximum failure loads agree well with test data. The relation between maximum shear and normal stresses at the interface has been derived to propose a separation failure criterion of strengthened members. The present study allows more realistic analysis and design of externally strengthened flexural member with steel plates.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.665-679
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• 2019

Mesoscale soil moisture measurement from the promising Cosmic-Ray Neutron Probe (CRNP) is expected to bridge the gap between large scale microwave remote sensing and point-based in-situ soil moisture observations. Traditional calibration based on $N_0$ method is used to convert neutron intensity measured at the CRNP to field scale soil moisture. However, the static calibration parameter $N_0$ used in traditional technique is insufficient to quantify long term soil moisture variation and easily influenced by different time-variant factors, contributing to the high uncertainties in CRNP soil moisture product. Consequently, in this study, we proposed a modified traditional calibration method, so-called Dynamic-$N_0$ method, which take into account the temporal variation of $N_0$ to improve the CRNP based soil moisture estimation. In particular, a nonlinear regression method has been developed to directly estimate the time series of $N_0$ data from the corrected neutron intensity. The $N_0$ time series were then reapplied to generate the soil moisture. We evaluated the performance of Dynamic-$N_0$ method for soil moisture estimation compared with the traditional one by using a weighted in-situ soil moisture product. The results indicated that Dynamic-$N_0$ method outperformed the traditional calibration technique, where correlation coefficient increased from 0.70 to 0.72 and RMSE and bias reduced from 0.036 to 0.026 and -0.006 to $-0.001m^3m^{-3}$. Superior performance of the Dynamic-$N_0$ calibration method revealed that the temporal variability of $N_0$ was caused by hydrogen pools surrounding the CRNP. Although several uncertainty sources contributed to the variation of $N_0$ were not fully identified, this proposed calibration method gave a new insight to improve field scale soil moisture estimation from the CRNP.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• 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.