• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.646-659
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• 2018

This study proposed "Reservoir Park", which added disaster prevention function to urban green spaces such as city parks through domestic and overseas related laws review, case studies, field trips. This is a combination of urban parks and reservoirs as urban planning facilities, which can provide both space for daily use by urban residents and disaster mitigation functions in case of emergency. In order to prevent flooding in urban areas due to climate change, facilities should be installed in the form of parks, etc., as the reservoir facility should be systematically reviewed together with urban planning facilities. However it was found that the reservoir park was not clear as a theme park. In this study, the types of storage facilities in urban areas were reclassified into five types of storage parks reflecting the characteristics of urban green spaces through domestic case studies and field trips. The classification of the reservoir parks is classified into 5 kinds such as ecological type, vegetation cover type, exercise facility type, underground burial type and hybrid type based on groundwater level, human use, and reservoir size. This classification system can be used to determine the types of facilities to be built after designating the location of future storage facilities.

• Korean Journal of Agricultural Science
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• v.38 no.1
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• pp.153-162
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• 2011

This study analyzed the spatio-temporal change detection of land-use and urbanization in Yongin and Anseong regions, Kyunggi Province, using three Landsat-5 TM images for 1990, 1996, and 2000. Remote sensing (RS) and geographic information system (GIS) techniques were used for image classification and result analysis. Six land-use types were classified using supervised maximum likelihood classification. In the two study areas, the land-use changed significantly, especially the decrease of arable land and forest and increase of built-up area. Spatially, the urban expansion of Yongin region showed a spreading trend mainly along the national road and expressways. But in Anseong region the expansion showed 'urban sprawl phenomenon' with irregular shape like starfish. Temporally, the urban expansion showed disparity - the growth rates of urbanized area rose from the period 1990-1996 to 1996-2000 in both study areas. The increased built-up areas were converted mainly from paddy, dry vegetation, and forest.

• Journal of Environmental Impact Assessment
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• v.16 no.2
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• pp.157-168
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• 2007

In case there is a need to run the multi-year urban scale air qulaity model, it is a difficult task due to the computational demand, requiring the statistical approach for the long time atmospheric environmental assessment. In an effort to approach toward long term urban assessment, the sixteen synoptic meteorological conditions are statistically classified from the estimated geostrophic wind speeds and directions of 850 hPa geopotential height field during 2000 ~ 2005. The geostrophic wind directions are subdivided into four even intervals (north, east, south, and west), geostrophic wind speeds into two classes(${\leq}5m/s$ and >5m/s), and daily mean cloud amount into 2 classes(${\leq}5/10$ and >5/10), which result into sixteen classes of the synoptic meteorological cases for each season. The frequency distributions for each 16 synoptic meteorological case are examined and some discussions on how these synoptic classifications can be used in the environmental assessment are presented.

• Journal of Korean Society of Rural Planning
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• v.26 no.4
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• pp.107-116
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• 2020

The purpose of this study is to analyze the characteristics of the 11 southeastern states in the United States by using regional characteristics variables and to classify the regions. First, 19 variables from four categories of population, society, industry-economy and urban service were selected and factor analysis were conducted, and the result showed five major factors of population, economic condition, job and commuting. Based on the following factor scores, a cluster analysis was conducted, and eight types of big city, medium-sized city, bed town, small town, urban hinterland, retirement town, and rural village were derived. These types of spatial distribution characteristics showed big cities were by different types of regions and they formed metropolitan areas. Each types of classified regions were located along the road network with hierarchy. The study focused on cases in the southeastern regions of the United States and can be used as a comparison with Korean cases. If the same research method is applied to Korea in the future, or if the time series of changes is tracked by analyzing different time points, it will greatly help identify the characteristics of urban and rural mixed areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• Proceedings of the KSRS Conference
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• v.1
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• pp.413-416
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• 2006

As even small features can be classified as high resolution imagery, urban remote sensing is regarded as one of the important application fields in time of wide use of the commercialized high resolution satellite imageries. In this study, we have analyzed the variogram properties of high resolution imagery, which was obtained in urban area through the simple modeling and applied to the real image. Based on the grasped variogram characteristics, we have tried to decomposed two high-resolution imagery such as IKONOS and QuickBird reducing window size until the unique variogram that urban feature has come out and then been indexed. Modeling results will be used as the fundamental data for variographic analysis in urban area using high resolution imagery later on. Index map also can be used for determining urban complexity or land-use classification, because the index is influenced by the feature size.

• Journal of the Economic Geographical Society of Korea
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• v.18 no.1
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• pp.45-59
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• 2015

The urban-rural consolidated city has emerged in order to promote the balanced development of urban region and rural region. Targeted 11 urban-rural consolidated cities in Gyeonggi Province, this paper investigated the changes of economic space in accordance with the location of industrial activities. The number of establishments has increased in all cities. From the analysis of the changes in the economic space divided into urban regions and rural regions, concentration phenomenon of economic activities in urban regions was strengthened in 9 cities with the exception of Icheon-si and Anseong-si. Concentration of economic activities in urban region was noticeable in Namyangju-si, Pocheon-si, and Yongin-si. 5 types were derived from the classification on the basis of locational changes of economic activities in urban region and rural region. They are 1) urban-rural gap deepen type, 2) urban region growth slowdown type, 3) rural region growth type, 4) urban-rural balanced growth type, and 5) urban region-led growth type. While urban-rural gap of the cities close to highly urbanized city or metropolis has intensified, the growth of urban region was weakened in the cities located away from the metropolis.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.134-137
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• 2005

Unsupervised spectral angle classification (USAC) is the algorithm that can extract ground object information with the minimum 'Spectral Angle' operation on behalf of 'Spectral Euclidian Distance' in the clustering process. In this study, our algorithm uses the unit vector instead of the spectral distance to compute the mean of cluster in the unsupervised classification. The proposed algorithm (MUSAC) is applied to the Hyperion and ETM+ data and the results are compared with K-Meails and former USAC algorithm (FUSAC). USAC is capable of clearly classifying water and dark forest area and produces more accurate results than K-Means. Atmospheric correction for more accurate results was adapted on the Hyperion data (Hyperion-FLAASH) but the results did not have any effect on the accuracy. Thus we anticipate that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but also hyperspectral images. Furthermore the cluster unit vector can be an efficient technique for determination of each cluster mean in the USAC.

• Journal of Korean Elementary Science Education
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• v.26 no.1
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• pp.41-48
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• 2007

The purpose of this study is to examine how many plant names elementary school children how, and what kind of criteria they use for classifying these plants. The sample involved 926 students from the 2nd, the 4th, and the 6th grades dwelling in one urban, three suburban, and six rural areas. Their level of perception on the name of plants increased in correlation to the elevation of the grade level. However, different patterns of increases were shown depending on the local environments in which they live. The most well-known plant names for students were the rose of Sharon, the rose and the pine tree. The students mostly classified the plants using the following criteria such as 'with or without flower' and 'edible or inedible' regardless as to whether they had prior loaming experience of plant classification. 65.3% of the 6th graders correctly grouped 5 kinds of plants into the flowering and the non-flowering plant categories at the 1st level of classification. However, only 17.9% and 7.7% correctly divided the flowering and the non-flowering plants into two subgroups at the 2nd level of classification respectively. Therefore, their abilities in plant classification was shown overall to be poor. The students living in suburban areas appeared to be harmonized with both the natural and urbanized surroundings and classified the plants more scientifically than those from the urban or rural areas were able to. This suggests that the conception of plant classification by children is affected by the environment in which they live. If children have more opportunities to observe plants in surroundings such as their classrooms and school gardens, it will help them to form the relevant scientific concepts as well as to correct any alternative conceptions related to classification.