• The Korean Journal of Ecology
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• v.23 no.5
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• pp.359-368
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• 2000

Landscape change is the modification and replacement of landscape elements in accordance with human management and natural disturbance on land mosaics. During landscape change, changes in patterns such as heterogeneity, diversity and shape, and juxtaposition of spatial elements are also accompanied. For the sustainable landscape system, therefore, spatial characteristics of the landscape should be considered in implementation of landscape conservation and restoration planning. Short-term changes of land-use and landscape pattern during the 10 years of 1980s and 1990s were investigated in the agriculture-forestry dominated landscape system through the statistics and the analysis of landscape-vegetation map. Study area is Yangdong-myon, Yangpyung-gun (37°27′30"N, 127°46′50"E), Kyonggi-do, in central Korea. Landscape change of this region was significantly related to the recent industrialization according to socio-economic development. Analyses of landscape pattern show that the area of secondary forest sustained by human activity decreased and it was replaced with large exotic plantations during this period. Area of paddy field was also extended. Fractal dimension of the total landscape increased, but that of paddy field area decreased due to rearrangement for mechanized farming. Moreover, the area of landscape management regimes such as plantation and cultivation increased in land mosaics during this period.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.281-291
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• 2015

Sun glint correction methods of hyperspectral data that have been developed so far have not considered the various situations and are often adequate for only certain conditions. Also there is an inaccurate assumption that the signal in NIR wavelength is zero. Therefore, this study attempts to analyze the NIR spectral properties of sun glint effect in coastal waters. For the analysis, CASI-1500 airborne hyperspectral data, bathymetry data and in-situ data obtained at coastal area near Sin-Cheon, Jeju Island, South Korea were used. The spectral characteristics of radiance and reflectance at the five NIR wavelengths (744 nm, 758 nm, 772 nm, 786 nm, and 801 nm) are analyzed by using various statistics, spatial and spectral variation of sun-glinted area under conditions of the bottom types of benthos, barren rocks and sand with similar water depth. Through the quantitative analysis, we found that the relation of water depth or bottom type with sun glint is relatively less which is a similar result with the previous studies. However the sun glint are distributed similarly with the patterns of the direction of wave propagation. It is confirmed that the areas with changed direction of wave propagation were not affected by the sun glint. The spatial and spectral variations of radiance and reflectance are mainly caused by the effect of sun glint and waves. The radiance or reflectance of more sun-glinted areas are increased approximately 1.5 times and the standard deviations are also increased three times compared to the less sun glinted areas. Through this study, the further studies of sun glint correction method in coastal water using the patterns of wave propagation and diffraction will be placed.

• Land and Housing Review
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• v.5 no.4
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• pp.215-224
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• 2014

National street has become one of open space for enhancing environmental qualities of city and country. In the developed countries such as France, U.K. U.S.A are designed symbolic malls, squares and street which are represented the historicity, culture and symbolic place. For place identity, we analyzed the relationship between element and user satisfaction in Sejong Avenue, Gwangwhamun Square. Data were analyzed using statistical methods such as descriptive statistics, ANOVA and correlation. Results of this study are as follows, i) factor analysis is carried out to extract spatial components and satisfaction. The satisfaction concerning transportation access was the higher than other factors, ii) user satisfaction was strongly correlated on the spatial elements, iii) user showed high user perception to study site. Results of this study can identify representative street management plan based on landscape elements and user satisfaction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.3
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• pp.287-294
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• 2012

The spread of infectious diseases in the event of livestock is getting faster and the route of spread gets more varied. It is important for the responsible agency to detect early and establish a prevention and surveillance system. If the spread cannot be contained effectively, great damage and loss will be inevitable in terms of social, environment and economic aspects as well as the welfare of the farmers. At present in Korea, a web-based Infectious Livestock Diseases Statistics System (AIMS: Animal Infectious Disease Data Management System) has been already implemented for this purpose and the service is available to the general public. But this system does not provide geospatial information and does not provide support for decision making and does not provide multi-dimensional information. In this study, an open source-based SOLAP (Spatial On-Line Analytical Processing) technology is applied to enable many diverse forms of data analysis from many aspects to support decision making. The SOLAP system was designed to integrate geospatial information and the analysis of information has been largely divided into map-based analysis and table-based analysis.

• Economic and Environmental Geology
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• v.36 no.4
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• pp.313-320
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• 2003

Slope disaster is one of the repeated occurring geological disasters in rainy season resulting in about 23 human losses in Korea every year. The slope disaster, however, mainly depends on the spatial and climate properties. such as geology, geomorphology, and heavy rainfall, and, hence, the prediction or hazard analysis of the slope disaster is a difficult task. Therefore, GIS and various statistical methods are implemented for slope disaster analysis. In particular, GIS technique is widely used for the analysis because it effectively handles large amount of spatial data. The GIS technique. however, only considers the statistics between slope disaster occurrence and related factors, not the mechanism. Accordingly. an infinite slope model that mechanically considers the balance of forces applied to the slope is suggested here with GIS for slope disaster analysis. According to the research results, the infinite slope model has a possibility that can be utilized for landslide prediction and hazard evaluation since 87.5％ of landslide occurrence areas have been predicted by this technique.

• Journal of the Korean Geographical Society
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• v.45 no.6
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• pp.806-819
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• 2010

Population data has been essential and fundamental in spatial analysis and commonly aggregated into political boundaries. A conventional method for population distribution estimation was a regression model with land use data, but the estimation process has limitation because of spatial autocorrelation of the population data. This study aimed to improve the accuracy of population distribution estimation by adopting a Regression-Kriging method, namely RK Model, which combines a regression model with Kriging for the residuals. RK Model was applied to a part of Seoul metropolitan area to estimate population distribution based on the residential zones. Comparative results of regression model and RK model using RMSE, MAE, and G statistics revealed that RK model could substantially improve the accuracy of population distribution. It is expected that RK model could be adopted actively for further population distribution estimation.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1193-1201
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• 2016

In epidemiology or etiology, we are often interested in identifying areas of elevated risk, so called, hot spot or cluster. Many existing clustering methods only tend to a result if there exists any clustering pattern in study area. Recently, however, lots of newly introduced clustering methods can identify the location, size, and shape of clusters and test if the clusters are statistically significant as well. In this paper, one of most commonly used clustering methods, scan statistic, and its implementation SaTScan software, which is freely available, will be introduced. To exemplify the usage of SaTScan software, we used cancer data from the SEER program of National Cancer Institute of U.S.A.We aimed to help researchers and practitioners, who are interested in spatial cluster detection, using female lung cancer mortality data of the SEER program.

• Journal of Environmental Health Sciences
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• v.30 no.3
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• pp.293-299
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• 2004

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristics of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends is towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a model of vehicle emission calculation by using real-time traffic data was studied. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It is possible that characteristics of hourly air pollutants emission rates is obtained from hourly traffic volume and speed. An emission rates model is allocated with a high resolution space by using geographic information system (GIS). Vehicle emission model was developed with a high resolution spatial, gridded and hourly emission rates.

• Spatial Information Research
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• v.19 no.2
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• pp.29-36
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• 2011

This study performed quantitative assessment of the performance of adjustment models by a-priori analysis of the statistics of the surface parameter estimates against modeling factors. Lidar, airborne imagery, and SAR imagery have been used to acquire the earth surface elevation, where the shape properties of the surface need to be determined through neighboring observations around target location. In this study, parameters which are selected to be estimated are elevation, slope, second order coefficient. In this study, several factors which are needed to be specified to compose adjustment models are classified into three types: mathematical functions, kernel sizes, and weighting types. Accordingly, a-priori standard deviations of the parameters are computed for varying adjustment models. Then their corresponding confidence regions for both the standard deviation of the estimate and the estimate itself are calculated in association with probability distributions. Thereafter, the resulting confidence regions are compared to each other against the factors constituting the adjustment models and the quantitative performance of adjustment models are ascertained.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.