• Journal of Ecology and Environment
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• v.37 no.2
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• pp.53-60
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• 2014

The arctic tundra is an important ecosystem in terms of the organic carbon cycle and climate change, and therefore, detailed analysis of vegetation distribution patterns is required to determine their association. We used grid-sampling method and applied geostatistics to analyze spatial variability and patterns of vegetation within a two-dimensional space, and calculated the Moran's I statistics and semivariance to assess the spatial autocorrelation of vegetation. Spatially autocorrelated vegetation consisted of moss, Eriophorum vaginatum, Betula nana, and Rubus chamaemorus. Interpolation maps and cross-correlograms revealed spatial specificity of Carex aquatilis and a strong negative spatial correlation between E. vaginatum and C. aquatilis. These results suggest differences between the species in water requirements for survival in the arctic tundra. Geostatistical methods could offer valuable information for identifying the vegetation spatial distribution.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.337-350
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• 2006

The characteristics of neighborhood correlation images for change detection were explored at different spatial resolution scales. Bi-temporal QuickBird datasets of Las Vegas, NV were used for the high spatial resolution image analysis, while bi-temporal Landsat $TM/ETM^{+}$ datasets of Suwon, South Korea were used for the mid spatial resolution analysis. The neighborhood correlation images consisting of three variables (correlation, slope, and intercept) were evaluated and compared between the two scales for change detection. The neighborhood correlation images created using the Landsat datasets resulted in somewhat different patterns from those using the QuickBird high spatial resolution imagery due to several reasons such as the impact of mixed pixels. Then, automated binary change detection was also performed using the single and multiple neighborhood correlation image variables for both spatial resolution image scales.

• Spatial Information Research
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• v.19 no.1
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• pp.61-72
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• 2011

Greenhouses gas emission due to usage of fossil fuel has been known as one of the main causes of global warming. Fundamentally, greenhouse gas is a by-product of economic activity. Since majority of economic activity happens in an urban setting, a countermeasure in an urban setting is needed. Therefore, an analysis of relationship between carbon dioxide emission and urban form will be investigated for urban planning and management in the future. The purpose of this study is to analyze the relationship between carbon dioxide emission and urban spatial patterns, and suggesting an urban form with low carbon dioxide emission. In order to achieve this, first theoretical analysis was carried out on urban spatial patterns related to physical size, usage rate, and activity level. Secondly, Seoul's dam on electricity, natural gas, local heating, petroleum, and water usage and mapping a carbon dioxide emission map. Thirdly, relationship between carbon dioxide emission and urban spatial patterns are analyzed and urban spatial patterns that affects energy usage in urban setting was elucidated, and elicited implications on future directions on urban planning based on our analyses above.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.87-107
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• 2018

With more than 10 million inhabitants, in particular, Seoul, the capital of Korea, has already experienced a number of severe heat wave. To alleviate the potential impacts of heat wave and the vulnerability to heat wave, policy-makers have generally considered the option of heat wave strategies containing adaptation elements. From the perspective of sustainable planning for adaptation to heat wave, the objective of this study is to identify the elements of vulnerability and assess heat wave-vulnerability at the dong level. This study also performs an exploratory investigation of the spatial pattern of vulnerable areas in Seoul to heat wave by applying exploratory spatial data analysis. Then this study attempts to select areas with the relatively highest and lowest level of adaptive capacity to heat wave based on an framework of climate change vulnerability assessment. In our analysis, the adaptive capacity is the relatively highest for Seongsan-2-dong in Mapo and the relatively lowest for Changsin-3-dong in Jongno. This study sheds additional light on the spatial patterns of heat wave-vulnerability and the relationship between adaptive capacity and heat wave.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.266-268
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• 2003

This is based on the AGENT-LUC model framework. Luangprabang Province has the largest percentage of shifting cultivation area in Laos PDR. The model simulates the spatial and temporal patterns of the shifting cultivation in the study area, using a GIS database while the total area of shifting cultivation is controlled by supply and demand balance of food. The model simulation period is from 1990 to 1999, at a spatial resolution of 500m. The results are evaluated using statistical data and remote sensing images. Through the validation, it is concluded that the trends simulated agrees to that of statistical data and the spatial and temporal patterns are also replicated satisfactorily.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1727-1737
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• 2018

In this paper, we propose a system to detect spatial structures in land cover maps and to detect time-series spatial structure changes. At first, the proposed system detects patches in a certain area at different times and calculates their measures to analyse spatial structure patterns of the area. Then the system conducts patch mapping among the detected time-series patches and decides 6 types of patch changes such as keeping, creating, disappearing, splitting, merging, and changing in a mixed way. Also, the system stores the patch-based spatial structure patterns of time-series land cover maps in binary form to extract changes. This demonstrated that the proposed change detection system can be used as a basis for planning the reconstruction of the urban spatial structure by measuring the degree of urban sprawl.

• Spatial Information Research
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• v.11 no.3
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• pp.227-240
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• 2003

The primary focus of this study is to provide a general methodology which can be utilized to understand and analyze environmental issues such as long term ecosystem dynamics and land use/cover change by development of 2D dynamic landscape models and model-based simulation. Change processes in land cover and ecosystem function can be understood in terms of the spatial and temporal distribution of land cover resources. In development of a system to understand major processes of change and obtain predictive information, first of all, spatial heterogeneity is to be taken into account because landscape spatial pattern affects on land cover change and interaction between different land cover types. Therefore, the relationship between pattern and processes is to be included in the research. Landscape modeling requires different approach depending on the definition, assumption, and rules employed for mechanism behind the processes such as spatial event process, land degradation, deforestration, desertification, and change in an urban environment. The rule-based models are described in the paper for land cover change by natural fires. Finally, a case study is presented as an example using spatial modeling and simulation to study and synthesize patterns and processes at different scales ranging from fine-scale to global scale.

• Journal of Forest and Environmental Science
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• v.33 no.3
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• pp.161-171
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• 2017

Global environmental changes have the capacity to make dramatic alterations to floral and faunal composition, and elucidation of the mechanism is important for predicting its outcomes. Studies on global climate change have traditionally focused on statistical summaries within relatively wide scales of spatial and temporal changes, and less attention has been paid to variability in microclimates across spatial and temporal scales. Microclimate is a suite of climatic conditions measured in local areas near the earth's surface. Environmental variables in microclimatic scale can be critical for the ecology of organisms inhabiting there. Here we examine the effect of spatial and temporal changes in microclimates on those of carabid beetle communities in Hyangnobong, Korea. We found that climatic variables and the patterns of annual changes in carabid beetle communities differed among sites even within the single mountain system. Our results indicate the importance of temporal survey of communities at local scales, which is expected to reveal an additional fraction of variation in communities and underlying processes that has been overlooked in studies of global community patterns and changes.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.382-385
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• 2008

MODIS images have a great advantage of high temporal resolution to monitor land cover changes in a large area. The moderate and low spatial resolution satellite images are incomparably economic than high resolution satellite images. As diverse satellite images are provided recently, strategies using satellite images are necessary for continuous, effective and long-term land monitoring. This research purposed to use MODIS images to monitor land cover in Korean peninsula for long-term and continuous change detection. To maximize the advantages of high temporal resolution, the change detection was based on the MODIS temporal profiles of the surface reflectance for one year. In this study as the reflectance patterns of year 2005 were compared with the reflectance patterns of year 2007, the changed pixels could be detected during two years. To set up the threshold value for the decision of change, ASTER images with the higher spatial resolution, 15m, were used for this study. The test area covered the suburban area of metropolitan city, Seoul, where the landcover changes have been frequently happened.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.329-332
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• 2007

Annual vegetation growth patterns are determined by the intrinsic phenological characteristics of each land cover types. So, if typical growth patterns of each land cover types are well-estimated, and a NDVI time-series data of a certain area is compared to those estimated patterns, we can implement more advanced analyses such as a land surface-type classification or a land surface type change detection. In this study, we utilized Terra MODIS NDVI 250m data and compressed full annual NDVI time series data into several indices using the Harmonic Analysis of Time Series(HANTS) algorithm which extracts the most significant frequencies expected to be presented in the original NDVI time-series data. Then, we found these frequencies patterns, described by amplitude and phase data, were significantly different from each other according to vegetation types and these could be used for land cover classification. However, in spite of the capabilities of the HANTS algorithm for detecting and interpolating cloud-contaminated NDVI values, some distorted NDVI pixels of June, July and August, as well as the long rainy season in Korea, are not properly corrected. In particular, in the case of two or three successive NDVI time-series data, which are severely affected by clouds, the HANTS algorithm outputted wrong results.