• Journal of Wetlands Research
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• v.21 no.spc
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• pp.39-50
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• 2019

This study aims to assess the influence of climate change on the hydrological cycle at a basin level in North Korea. The selected model for this study is MRI-CGCM 3, the one used for the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover, this study adopted the Spatial Disaggregation-Quantile Delta Mapping (SDQDM), which is one of the stochastic downscaling techniques, to conduct the bias correction for climate change scenarios. The comparison between the preapplication and postapplication of the SDQDM supported the study's review on the technique's validity. In addition, as this study determined the influence of climate change on the hydrological cycle, it also observed the runoff in North Korea. In predicting such influence, parameters of a runoff model used for the analysis should be optimized. However, North Korea is classified as an ungauged region for its political characteristics, and it was difficult to collect the country's runoff observation data. Hence, the study selected 16 basins with secured high-quality runoff data, and the M-RAT model's optimized parameters were calculated. The study also analyzed the correlation among variables for basin characteristics to consider multicollinearity. Then, based on a phased regression analysis, the study developed an equation to calculate parameters for ungauged basin areas. To verify the equation, the study assumed the Osipcheon River, Namdaecheon Stream, Yongdang Reservoir, and Yonggang Stream as ungauged basin areas and conducted cross-validation. As a result, for all the four basin areas, high efficiency was confirmed with the efficiency coefficients of 0.8 or higher. The study used climate change scenarios and parameters of the estimated runoff model to assess the changes in hydrological cycle processes at a basin level from climate change in the Amnokgang River of North Korea. The results showed that climate change would lead to an increase in precipitation, and the corresponding rise in temperature is predicted to cause elevating evapotranspiration. However, it was found that the storage capacity in the basin decreased. The result of the analysis on flow duration indicated a decrease in flow on the 95th day; an increase in the drought flow during the periods of Future 1 and Future 2; and an increase in both flows for the period of Future 3.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.3
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• pp.1-12
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• 2016

This study aimed to interpret the fundamental cause of road-kill occurrences and analyzed spatial characteristics of the road-kill locations from Route 44 in Seorak National Park, Korea. Logistic regression analysis was utilized for backward elimination on variables. Seorak National Park Service has constructed GIS-data of 81 road-kill occurrences from 2008 to 2013 and these data were assigned as dependent variables in this study. Considered as independent variables from previous studies and field surveys, vegetation age-class, distance to streams, coverage of fences and retaining walls, and distance to building sites were assigned as road-kill impact factors. The coverage of fences and retaining walls(-1.0135) was shown as the most influential factor whereas vegetation age-class(0.0001) was the least influential among all of the significant factor estimates. Accordingly, the rate of road-kill occurrence can increase as the distance to building sites and stream becomes closer and vegetation age-class becomes higher. The predictive accuracy of road-kill occurrence was shown to be 72.2% as a result of analysis, assuming as partial causes of road-kill occurrences reflecting spatial characteristics. This study can be regarded as beneficial to provide objective basis for spatial decision making including road-kill occurrence mitigation policies and plans in the future.

• Korean Journal of Ecology and Environment
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• v.41 no.4
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• pp.512-518
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• 2008

This study were to evaluate trophic conditions, N : P ratios, and empirical relations of chlorophyll (CHL) systematically using TN, TP, and CHL values in agricultural reservoirs and drinking water supplying dams. During the study, nutrients and CHL varied depending on seasonal conditions and types of the reservoirs, but most reservoirs were diagnozed as eutrophic to hypertrophic. Mass ratios of TN : TP averaged 93.1 (range: $0.68{\sim}1342$) and about 96.6 % of the total observations (n=516) was > 17 in the N : P ratios. This result suggests that P was a potential factor limiting algal growth in the entire reservoir. Thus, TN : TP ratios were a function of phosphorus rather than nitrogen. Regression analysis of log-transformed N : P ratios against TP in DWDRs and ARs showed that ratios were linearly declined with an increase of TP ($R^2$>0.66; p<0.001). Seasonal mean CHL was minimum ($4.3{\mu}g\;L^{-1}$, range: $0.1{\sim}39.7{\mu}g\;L^{-1}$) in premonsoon, and was similar between the monsoon and postmonsoon. In contrast, one of the tremendous features was that values of CHL was greater in the ARs than DWDRs. Thus, the spatial and temporal patterns in CHL were similar to those of TP but not TN. Empirical models of CHL-TP showed that CHL variation could explain average 15.3% and 11.3% in DWDRs and ARs, respectively. Seasonal analysis of empirical models showed that CHL-TP relations were stronger in postmonsoon than those of premonsoon and monsoon.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• Journal of Ecology and Environment
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• v.39 no.1
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• pp.31-42
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• 2016

The object of this study was to determine long-term temporal and spatial patterns of nutrients (nitrogen and phosphorus), suspended solids, and chlorophyll (Chl) in Chungju Reservoir, based on the dataset of 1992 - 2013, and then to develop the empirical models of nutrient-Chl for predicting the eutrophication of the reservoir. Concentrations of total nitrogen (TN) and total phosphorus (TP) were largely affected by an intensity of Asian monsoon and the longitudinal structure of riverine (Rz), transition (Tz), and lacustrine zone (Lz). This system was nitrogen-rich system and phosphorus contents in the water were relatively low, implying a P-limiting system. Regression analysis for empirical model, however, showed that Chl had a weak linear relation with TP or TN, and this was mainly associated with turbid, and nutrient-rich inflows in the system. The weak relation was associated with non-algal light attenuation coefficients (Kna), which is inversely related water residence time. Thus, values of Chl had negative functional relation (R² = 0.25, p < 0.001) with nonalgal light attenuation. Thus, the low chlorophyll at a given TP indicated a light-limiting for phytoplankton growth and total suspended solids (TSS) was highly correlated (R² = 0.94, p < 0.001) with non-algal light attenuation. The relations of Trophic State Index (TSI) indicated that phosphorus limitation was weak [TSI (Chl) - TSI (TP) < 0; TSI (SD) - TSI (Chl) > 0] and the effects of zooplankton grazing were also minor [TSI (Chl) - TSI (TP) > 0; TSI (SD) - TSI (Chl) > 0].

• Atmosphere
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• v.24 no.1
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• pp.101-110
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• 2014

This study estimates and evaluates the daily January temperature from 2003 to 2012 with 30 m-resolution over South Korea, using a modified Parameter-elevation Regression on Independent Slopes Model (K-PRISM). Several factors in K-PRISM are also adjusted to 30 m grid spacing and daily time scales. The performance of K-PRISM is validated in terms of bias, root mean square error (RMSE), and correlation coefficient (Corr), and is then compared with that of inverse distance weighting (IDW) and hypsometric methods (HYPS). In estimating the temperature over Jeju island, K-PRISM has the lowest bias (-0.85) and RMSE (1.22), and the highest Corr (0.79) among the three methods. It captures the daily variation of observation, but tends to underestimate due to a high-discrepancy in mean altitudes between the observation stations and grid points of the 30 m topography. The temperature over South Korea derived from K-PRISM represents a detailed spatial pattern of the observed temperature, but generally tends to underestimate with a mean bias of -0.45. In bias terms, the estimation ability of K-PRISM differs between grid points, implying that care should be taken when dealing with poor skill area. The study results demonstrate that K-PRISM can reasonably estimate 30 m-resolution temperature over South Korea, and reflect topographically diverse signals with detailed structure features.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1279-1290
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• 2017

R's {shiny} package provides an environment for creating web applications with only R scripts. Shiny does not require knowledge of a separate web programming language and its development is very easy and straightforward. In addition, Shiny has a variety of extensibility, and its functions are expanding day by day. Therefore, the presentation of high-quality results is an excellent tool for R-based analysts. In this paper, we present actual cases of large data analysis using Shiny. First, geological anomaly zone is extracted by analyzing topographical data expressed in the form of contour lines by analysis related to spatial data. Next, we will construct a model to predict major diseases by 16 cities and provinces nationwide using weather, environment, and social media information. In this process, we want to show that Shiny is very effective for data visualization and analysis.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.712-721
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• 2016

This study analyzes important urban characteristics according to location of large-scaled professional sports facilities based on a binary logistic regression model and t-test. For the empirical analysis, this study uses 24 urban characteristics within social, economic, cultural, traffic, and spatial categories in 228 cities. The results show that economic condition can be the main decision factor for the differential feature of the location as well as social and traffic conditions. Concretely, more the number of young adults and middle-aged people who have considerable purchasing power, and available land are more the beneficial to the presence of the sports facilities. Recently local governments focus on location of the facilities due to their impacts on regional economics as well as residents' quality of life. In this point of vies, this study gives good understanding why it is a great option for location of the facilities.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.231-242
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• 2014

On the Pre-Housing-Sale Systems there are many risks that developers might not fulfill the pre-sale obligations. In korea, in order to protect the people who bought houses from these risk, the Housing Sold Guarantee System was introduced and has been operated. Even though this system if there is accident in the pre-sale warranty business, several problems, such as damages caused to the people who bought the houses, occurs. Therefore, research is needed to Housing Sold Guarantee accident factor. But there are few study about it. This study attempted to analyze influencers on the possibility of the accident. We employ 3,026 data which Korea Housing Guarantee Co., Ltd manages and analyze them empirically, using business characteristics, housing market characteristics, and regional characteristics. Especially this study used to the binary logistic regression model. The results of analysis showed that the accident rate of Housing Sold Guarantee had been effected on the business type, house type, project financing guarantee, operator credit rating, housing market, and regional characteristics.