• Journal of Korean Society of Forest Science
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• v.102 no.3
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• pp.415-419
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• 2013

To mitigate forest fire damage, it is needed to concentrate suppression resources on the fire having a high probability to become large in the initial stage. The objective of this study is to develop the large fire judgement model which can estimate large fire possibility index between the fire size and the related factors such as weather, terrain, and fuel. The results of logistic regression equation indicated that temperature, wind speed, continuous drought days, slope variance, forest area were related to the large fire possibility positively but elevation has negative relationship. This model may help decision-making about size of suppression resources, local residents evacuation and suppression priority.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.169-181
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• 2004

SK Telecom has already constructed GIMS system as the base common framework of LBS/GIS service system based on OGC(OpenGIS Consortium)'s international standard for the first mobile vector map service in 2002, But as service content appears more complex, renovation has been needed to satisfy multi-purpose, multi-function and maximum efficiency as requirements have been increased. This research is for preparation ion of GML3-based platform to upgrade service from GML2 based GIMS system. And with this, it will be possible for variety of application services to provide location and geographic data easily and freely. In GML 3.0, it has been selected animation, event handling, resource for style mapping, topology specification for 3D and telematics services for mobile LBS multimedia service. And the schema and transfer protocol has been developed and organized to optimize data transfer to MS(Mobile Stat ion) Upgrade to GML 3.0-based GIMS system has provided innovative framework in the view of not only construction but also service which has been implemented and applied to previous research and system. Also GIMS channel interface has been implemented to simplify access to GIMS system, and service component of GIMS internals, WFS and WMS, has gotten enhanded and expanded function.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.116-130
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• 2019

In South Korea, forest fire occurrences are increasing in size and duration due to various factors such as the increase in fuel materials and frequent drying conditions in forests. Therefore, it is necessary to minimize the damage caused by forest fires by appropriately providing the probability of forest fire risk. The purpose of this study is to improve the Daily Weather Index(DWI) provided by the current forest fire forecasting system in South Korea. A new Fire Risk Index(FRI) is proposed in this study, which is provided in a 5km grid through the synergistic use of numerical weather forecast data, satellite-based drought indices, and forest fire-prone areas. The FRI is calculated based on the product of the Fine Fuel Moisture Code(FFMC) optimized for Korea, an integrated drought index, and spatio-temporal weighting approaches. In order to improve the temporal accuracy of forest fire risk, monthly weights were applied based on the forest fire occurrences by month. Similarly, spatial weights were applied using the forest fire density information to improve the spatial accuracy of forest fire risk. In the time series analysis of the number of monthly forest fires and the FRI, the relationship between the two were well simulated. In addition, it was possible to provide more spatially detailed information on forest fire risk when using FRI in the 5km grid than DWI based on administrative units. The research findings from this study can help make appropriate decisions before and after forest fire occurrences.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.2
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• pp.47-61
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• 2000

Agroclimatic zoning was done for paddy rice culture in North Korea based on a simulation experiment. Daily weather data for the experiment were generated by 3 steps consisting of spatial interpolation based on topoclimatological relationships, zonal summarization of grid cell values, and conversion of monthly climate data to daily weather data. Regression models for monthly climatological temperature estimation were derived from a statistical procedure using monthly averages of 51 standard weather stations in South and North Korea (1981-1994) and their spatial variables such as latitude, altitude, distance from the coast, sloping angle, and aspect-dependent field of view (openness). Selected models (0.4 to 1.6$^{\circ}C$ RMSE) were applied to the generation of monthly temperature surface over the entire North Korean territory on 1 km$\times$l km grid spacing. Monthly precipitation data were prepared by a procedure described in Yun (2000). Solar radiation data for 27 North Korean stations were reproduced by applying a relationship found in South Korea ([Solar Radiation, MJ m$^{-2}$ day$^{-1}$ ] =0.344 + 0.4756 [Extraterrestrial Solar Irradiance) + 0.0299 [Openness toward south, 0 - 255) - 1.307 [Cloud amount, 0 - 10) - 0.01 [Relative humidity, %), $r^2$=0.92, RMSE = 0.95 ). Monthly solar irradiance data of 27 points calculated from the reproduced data set were converted to 1 km$\times$1 km grid data by inverse distance weighted interpolation. The grid cell values of monthly temperature, solar radiation, and precipitation were summed up to represent corresponding county, which will serve as a land unit for the growth simulation. Finally, we randomly generated daily maximum and minimum temperature, solar irradiance and precipitation data for 30 years from the monthly climatic data for each county based on a statistical method suggested by Pickering et a1. (1994). CERES-rice, a rice growth simulation model, was tuned to accommodate agronomic characteristics of major North Korean cultivars based on observed phenological and yield data at two sites in South Korea during 1995~1998. Daily weather data were fed into the model to simulate the crop status at 183 counties in North Korea for 30 years. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to score the suitability of the county for paddy rice culture.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.307-321
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• 2016

Planetary Boundary Layer Height (PBLH) is a major input parameter for weather forecasting and atmosphere diffusion models. In order to estimate the sub-grid scale variability of PBLH, we need to monitor PBLH data with high spatio-temporal resolution. Accordingly, we introduce a LIdar observation VEhicle (LIVE), and analyze PBLH derived from the lidar loaded in LIVE. PBLH estimated from LIVE shows high correlations with those estimated from both WRF model ($R^2=0.68$) and radiosonde ($R^2=0.72$). However, PBLH from lidar tend to be overestimated in comparison with those from both WRF and radiosonde because lidar appears to detect height of Residual Layer (RL) as PBLH which is overall below near the overlap height (< 300 m). PBLH from lidar with 10 min time resolution shows typical diurnal variation since it grows up after sunrise and reaches the maximum after 2 hours of sun culmination. The average growth rate of PBLH during the analysis period (2014/06/26 ~ 30) is 1.79 (-2.9 ~ 5.7) m $min^{-1}$. In addition, the lidar signal measured from moving LIVE shows that there is very low noise in comparison with that from the stationary observation. The PBLH from LIVE is 1065 m, similar to the value (1150 m) derived from the radiosonde launched at Sokcho. This study suggests that LIVE can observe continuous and reliable PBLH with high resolution in both stationary and mobile systems.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.1
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• pp.16-23
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• 2000

A topography-precipitation relationship derived from the southern part of Korean Peninsula was applied to North Korea where climate stations are few and widely separated. Two hundred and seventy seven rain gauge stations of South Korea were classified into 8 different groups depending on the slope orientation (aspect) of the region they are located. Monthly precipitation averaged over 10 year period (1986-1995) was regressed to topographical variables of the station locations. A 'trend precipitation' for each gauge station was extracted from the precipitation surface interpolated from the monthly precipitation data of 24 standard stations of the Korea Meteorological Administration and used as a substitute for y-axis intercept of the regression equation. These regression models were applied to the corresponding regions of North Korea, which were identified by slope orientation, to obtain monthly precipitation surface for the aspect regions. 'Trend precipitation' from the 10 year data of 27 North Korean standard stations was also used in the model calculation. Output grids for each aspect region were mosaicked to form the monthly and annual precipitation surface with a 1km$\times$1km resolution for the entire territory of North Korea. Spatially averaged annual precipitation of North Korea was 938 mm with the standard deviation of 246 mm.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.2
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• pp.113-125
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• 2011

In this study, recent trends of the annual precipitation, the annual maximum precipitation of different durations and the rain days over several thresholds(i.e. 0, 10, 20, 40, 60 and 80 mm/day) according to the different local features were analyzed using daily precipitation data of 59 weather stations between 1973 and 2009. To analyze the variability according to the regional characteristics, 59 weather stations were classified by elevations, latitudes, longitudes, river basins, inland or shore(east sea, south sea, west sea) area and the level of urbanization. Results demonstrated that overall trend of variables increases except rain day. Results according to the regional characteristics showed that the increase trend becomes stronger with elevation increase. The increase trend of Han river basin is largest and that of Youngsan river basin is smallest. Also the increase trend becomes stronger with latitude increase and that of East coast is larger than that of South coast since it may be caused by the regional difference of elevation. The increase trend of urban area is larger than that of rural area. Overall trend showed that increase trend becomes stronger with elevation and latitude increase.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.903-915
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• 2020

Long-term high-resolution hydro-meteorological data has been recognized as an essential element in establishing the water resources plan. The increasing demand for spatial precipitation in various areas such as climate, hydrology, geography, ecology, and environment is apparent. However, potential limitations of the existing area-weighted and numerical interpolation methods for interpolating precipitation in high altitude areas remains less explored. The proposed PRISM (Precipitation-Elevation Regressions on Independent Slopes Model) model can produce gridded precipitation that can adequately consider topographic characteristics (e.g., slope and altitude), which are not substantially included in the existing interpolation techniques. In this study, the PRISM model was optimized with SCEM-UA (Shuffled Complex Evolution Metropolis-University of Arizona) to produce daily gridded precipitation. As a result, the minimum impact radius was calculated 9.10 km and the maximum 34.99 km. The altitude of coastal weighted was 681.03 m, the minimum and maximum distances from coastal were 9.85 km and 38.05 km. The distance weighting factor was calculated to be about 0.87, confirming that the PRISM result was very sensitive to distance. The results showed that the proposed PRISM model could reproduce the observed statistical properties reasonably well.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.309-320
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• 2020

Bayesian networks, also known as directed acyclic graphs (DAG), are used in many areas of medicine, meteorology, and genetics because relationships between variables can be modeled with graphs and probabilities. In particular, Bayesian network classifiers, which are used to predict discrete data, have recently become a new method of data mining. Bayesian networks can be grouped into different models that depend on structured learning methods. In this study, Bayesian network models are learned with various properties of structure learning. The models are compared to the simplest method, the naïve Bayes model. Classification results are compared by applying learned models to various real data. This study also compares the relationships between variables in the data through graphs that appear in each model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.208-220
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• 2016

Automatic Mountain Meteorology Observation System (AMOS) is an important ingredient for several climatological and forest disaster prediction studies. In this study, we select the optimal sites for AMOS in the mountain areas of Honam and Jeju in order to prevent forest disasters such as forest fires and landslides. So, this study used spatial dataset such as national forest map, forest roads, hiking trails and 30m DEM(Digital Elevation Model) as well as forest risk map(forest fire and landslide), national AWS information to extract optimal site selection of AMOS. Technical methods for optimal site selection of the AMOS was the firstly used multifractal model, IDW interpolation, spatial redundancy for 2.5km AWS buffering analysis, and 200m buffering analysis by using ArcGIS. Secondly, optimal sites selected by spatial analysis were estimated site accessibility, observatory environment of solar power and wireless communication through field survey. The threshold score for the final selection of the sites have to be higher than 70 points in the field assessment. In the result, a total of 159 polygons in national forest map were extracted by the spatial analysis and a total of 64 secondary candidate sites were selected for the ridge and the top of the area using Google Earth. Finally, a total of 26 optimal sites were selected by quantitative assessment based on field survey. Our selection criteria will serve for the establishment of the AMOS network for the best observations of weather conditions in the national forests. The effective observation network may enhance the mountain weather observations, which leads to accurate prediction of forest disasters.