Browse > Article

Change Detection of land-surface Environment in Gongju Areas Using Spatial Relationships between Land-surface Change and Geo-spatial Information  

Jang Dong-Ho (National Research Laboratory, Kongju National University)
Publication Information
Journal of the Korean Geographical Society / v.40, no.3, 2005 , pp. 296-309 More about this Journal
Abstract
In this study, we investigated the change of future land-surface and relationships of land-surface change with geo-spatial information, using a Bayesian prediction model based on a likelihood ratio function, for analysing the land-surface change of the Gongju area. We classified the land-surface satellite images, and then extracted the changing area using a way of post classification comparison. land-surface information related to the land-surface change is constructed in a GIS environment, and the map of land-surface change prediction is made using the likelihood ratio function. As the results of this study, the thematic maps which definitely influence land-surface change of rural or urban areas are elevation, water system, population density, roads, population moving, the number of establishments, land price, etc. Also, thematic maps which definitely influence the land-surface change of forests areas are elevation, slope, population density, population moving, land price, etc. As a result of land-surface change analysis, center proliferation of old and new downtown is composed near Gum-river, and the downtown area will spread around the local roads and interchange areas in the urban area. In case of agricultural areas, a small tributary of Gum-river or an area of local roads which are attached with adjacent areas showed the high probability of change. 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 capability of forest damage is very high. As a result of validation using a prediction rate curve, a capability of prediction of urban area is $80\%$, agriculture area is $55\%$, forest area is $40\%$ in higher $10\%$ of possibility which the land-surface change would occur. This integration model is unsatisfactory to Predict the forest area in the study area and thus as a future work, it is necessary to apply new thematic maps or prediction models In conclusion, we can expect that this way can be one of the most essential land-surface change studies in a few years.
Keywords
likelihood ratio; bayesian; land-surface change; prediction map; Gongju;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 박성미, 1997, 원격탐사 및 GIS 기법을 이용한 지표환경 분석 연구 : 하남지역의 응용사례, 서울대학교 석사학위 논문
2 박미선, 1997, 수도권 준농림지역의 토지이용 특성에 관한 연구, 서울대학교 석사학위논문
3 양인태.김흥규.신계종, 1999, '퍼지집합이론을 이용한 Landsat TM 영상의 감독분류 정확도 향상,' 대한 토목학회논문집, 19(3), 445-455
4 장동호.지광훈.이현영, 2002, '퍼지논리연산을 이용한 안면도 지표환경 변화 예측,' 대한지리학회지, 37(4), 371-384
5 Duda, R.O., Hart, P. and Nilsson, N., 1976, Subjective Bayesian methods for rule-based inference systems. Proceedings of the 1976 national Computer Conference, 1075-1082
6 Heiko, B., Paul, W.B. and Wolfgana K., 1998, Cellular automata models for vegetation dynamics, Ecological Modelling, 107, 113-125   DOI   ScienceOn
7 Howarth, P.J. and Boasson, E., 1983, Landsat digital enhancement for change detection in urban environment, Remote Sensing of Environment,13, 149-160   DOI   ScienceOn
8 Johnson, J.H., 1974, Suburban Grouth, John Wiley & Sons, Inc
9 Pain, C.F, 1985, Mapping of landforms from Landsat imagery: an example from eastern new South Wales, Australia, Remote Sensing of environment, 17(1), 35-46
10 Press, S. J., 1972, Applied Multivariate Analysis. Holt, Rinehart and Winston, INC., New York
11 Stringer, W.J., Groves, J.E. and Olmsted, C., 1988, Landsat determined geographic change, Photogrammetric Engineering & Remote Sensing, 54(3), 347-351
12 박노욱.지광훈.Chang-Jo F. Chung.권병두, 2005, '산사태 취약성 분석을 위한 GIS기반 확률론적 추정 모델과 모수적 모델의 적용,' 자원환경지질학회 지, 38(1), 45-55
13 Yan, L. and Stuart, R.P., 2003, Modelling urban development with cellular automata incorporating fuzzy-set approaches, Computers, Environment and Urban Systems, 27(6), 637-658   DOI   ScienceOn
14 장동호.박노욱.지광훈.김만규.정창조, 2004, 'GIS 기반 베이지안 예측모델을 이용한 보은지역의 산사태 취약성 분석,' 한국지형학회지, 11(3), 13-23
15 Bryant, C.R., 1984, The recent evolution of farming landscapes in urban centered regions, Landscape in Urban Planning 11, 307-326   DOI   ScienceOn
16 김대식, 1999, 지리정보시스템과 다기준 평가법을 이용한 농촌중심마을 모의 모형의 개발에 관한 연구, 서울 대학교 박사학위논문
17 Yeqiao, W. and Xinsheng, Z., 2001, A dynamic modeling approach to simulating socioeconomic effects on landscape changes, Ecological Modeling, 140, 141-162   DOI   ScienceOn
18 공주시, 2004, 공주시 통계연보
19 한국지질자원연구원, 2001, 공간정보를 이용한 지표환경 변화 탐지 및 통합 기술개발 : 대전시 사례연구
20 Toll, D.L., 1984, An evaluation of simulated thematic mapper data and Landsat MSS data for 1discriminating suburban and regional use and land cover, Photogrammetric Engineering & Remote Sensing, 50(12), 1713-1724
21 Ojima, D.S., Galvin, K.A. and Turner, B.L., 1994, The global impact of landuse change, BioScience, 44(50), 300-304   DOI   ScienceOn
22 김경아, 1998, 수도권 자연보존 권역에서 토지이용 규제가 지피변화에 미치는 영향, 서울대학교 석사학위논문
23 김훈희.이진희, 2001, '토지이용변화와 확률모형 구축 및 적용에 관한 연구,' 대한국토.도시계획학회지, 36(4), 1-17
24 Batty, M., Yichun, X. and Zhanli, S., 1999, Modeling urban dynamics through GIS-based cellular automata, Computers, Environment and Urban Systems, 23, 205-233   DOI   ScienceOn
25 Jang, D.H. and Chung, F.C., 2004a, Updating land cover classification using integration of multi-spectral and temporal remotely sensed data, Journal of the Korean Geographical Society, 39(5), 786-803
26 Silverman, B.W., 1986, Density Estimation for Statistics and Data Analysis, Chapman & Hall
27 Jang, D.H. and Chung, F.C., 2004b, Integration of multispectral remote sensing images and GIS thematic data for supervised land cover classification, Korean Journal of Remote Sensing, 20(5), 315-327
28 서창완.전성우, 1998, '원격탐사와 GIS기법을 이용한 접경지역 토지피복연구,' 한국환경영향평가학회지, 7(1), 11-22
29 Batty, M. and Yichun, X., 1994, Modeling inside GIS: Part2. Selecting and calibrating urban models using Arc/Info, International Journal of Geographical Information Systems, 8(5), 429-450   DOI   ScienceOn
30 Chung, F.C. and Fabbri, A.G., 1999, Probability prediction models for landslide hazard mapping, Photogrammetric Engineering & Remote Sensing, 65(12), 1389-1399
31 Takeshi, A. and Tetsuya, A., 2004, Empirical analysis for estimating land use transition potential functions-case in the Tokyo metropolitan region, Computer, Environment and Urban Systems, 28(1), 65-84   DOI   ScienceOn