Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
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Prediction of Land-cover Change in the Gongju Areas using Fuzzy Logic and Geo-spatial Information

퍼지 논리와 지리공간정보를 이용한 공주지역 토지피복 변화 예측

  • Jang, Dong-Ho (National Research Laboratory, Kongju National University)
  • 장동호 (공주대학교 국가지정연구실)
  • Received : 2005.09.13
  • Accepted : 2005.10.31
  • Published : 2005.12.31
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Abstract

In this study, we tried to predict the change of future land-cover and relationships between land-cover change and geo-spatial information in the Gongju area by using fuzzy logic operation. Quantitative evaluation of prediction models was carried out using a prediction rate curve using. Based on the analysis of correlations between the geo-spatial information and land-cover change, the class with the highest correlation was extracted. Fuzzy operations were used to predict land-cover change and determine the land-cover prediction maps that were the most suitable. It was predicted that in urban areas, the urban expansion of old and new towns would occur centering on the Gem-river, and that urbanization of areas along the interchange and national roads would also expand. Among agricultural areas, areas adjacent to national roads connected to small tributaries of the Gem-river and neighboring areas would likely experience changes. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the possibility of forest damage is very high. As a result of validation using the prediction rate curve, it was indicated that among fuzzy operators, the maximum fuzzy operator was the most suitable for analyzing land-cover change in urban and agricultural areas. Other fuzzy operators resulted in the similar prediction capabilities. However, in the prediction rate curve of integrated models for land-cover prediction in the forest areas, most fuzzy operators resulted in poorer prediction capabilities. Thus, it is necessary to apply new thematic maps or prediction models in connection with the effective prediction of changes in the forest areas.

Keywords

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