DOI QR코드

DOI QR Code

Urban Growth Prediction each Administrative District Considering Social Economic Development Aspect of Climate Change Scenario

기후변화시나리오의 사회경제발전 양상을 고려한 행정구역별 도시성장 예측

  • 김진수 (부경대학교 공간정보연구소) ;
  • 박소영 (부경대학교 공간정보연구소)
  • Received : 2013.03.05
  • Accepted : 2013.06.03
  • Published : 2013.06.30

Abstract

Land-use/cover changes not only amplify or alleviate influence of climate changes but also they are representative factors to affect environmental change along with climate changes. Thus, the use of land-use/cover changes scenario, consistent climate change scenario is very important to evaluate reliable influences by climate change. The purpose for this study is to predict and analyze the future urban growth considering social and economic scenario from RCP scenario suggested by the 5th evaluation report of IPCC. This study sets land-use/cover changes scenario based on storyline from RCP 4.5 and 8.5 scenario. Urban growth rate for each scenario is calculated by urban area per person and GDP for the last 25 years and regression formula based on double logarithmic model. In addition, the urban demand is predicted by the future population and GDP suggested by the government. This predicted demand is spatially distributed by the urban growth probability map made by logistic regression. As a result, the accuracy of urban growth probability map is appeared to be 89.3~90.3% high and the prediction accuracy for RCP 4.5 showed higher value than that of RCP 8.5. Urban areas from 2020 to 2050 showed consistent growth while the rate of increasing urban areas for RCP 8.5 scenario showed higher value than that of RCP 4.5 scenario. Increase of urban areas is predicted by the fact that famlands are damaged. Especially RCP 8.5 scenario indicated more increase not only farmland but also forest than RCP 4.5 scenario. In addition, the decrease of farmland and forest showed higher level from metropolitan cities than province cities. The results of this study is believed to be used for basic data to clarify complex two-way effects quantitatively for future climate change, land-use/cover changes.

토지이용과 피복 변화는 기후변화 영향을 증폭시키거나 완화시킬 뿐만 아니라, 기후변화와 함께 환경 변화에 영향을 주는 대표적인 인자들이다. 따라서 기후변화시나리오와 일관된 토지이용 및 피복 변화 시나리오를 사용하는 것은 신뢰성 있는 기후변화 영향평가를 위해 매우 중요하다. 본 연구의 목적은 IPCC의 5차 평가보고서에 제시된 RCP 시나리오의 사회경제 시나리오를 고려한 미래 도시성장을 예측 및 분석하는 것이다. 이를 위해 RCP 4.5와 8.5 시나리오의 스토리라인을 기반으로 토지이용 및 피복 변화 시나리오를 설정하였다. 시나리오별 도시성장량은 지난 25년 간 1인당 도시면적과 GDP를 이용한 이중로그모델에 의해 도출되었다. 또한, 정부에서 제공된 미래 인구수 및 GDP에 의해 미래 도시 수요량이 추정되었다. 이렇게 추정된 수요량은 로지스틱 회귀분석에 의해 작성된 도시성장확률지도에 의해 공간적으로 배분되었다. 그 결과, 도시성장확률지도의 예측 정확도는 89.3~90.3%로 높게 나타났고, RCP 4.5의 예측 정확도가 RCP 8.5 보다 높게 나타났다. 또한, 2020년부터 2050년까지 도시지역은 꾸준한 증가세를 보였고, RCP 8.5 시나리오의 도시면적 증가율이 RCP 4.5 시나리오보다 더 높게 나타났다. 도시지역의 면적 증가는 주로 농지면적 훼손에 의해 발생되는 것으로 예측되었다. 특히, RCP 4.5 시나리오보다 RCP 8.5 시나리오에서 농지뿐만 아니라 산지면적 훼손이 더욱 증가되는 것으로 예측되었다. 이러한 농지와 산지의 면적 감소는 지방도시에 비하여 광역도시에서 더 높게 나타났다. 본 연구의 결과는 향후 기후 및 토지이용 및 피복 변화의 복합적인 쌍방향 영향을 정량적으로 밝힐 수 있는 기초 자료로 활용될 수 있을 것이라 판단된다.

Keywords

References

  1. Aguilar A.G., Ward P.M., Smith Sr. C.B., 2003, Globalization, regional development, and mega-city expansion in Latin America: analyzing Mexico City's peri-urban hinterland, Cities Vol. 20, pp. 3-21. https://doi.org/10.1016/S0264-2751(02)00092-6
  2. Allen J., Lu K., 2003, Modeling and prediction of future urban growth in the Charleston region of South Carolina : a GIS-based integrated approach, Conservation Ecology Vol. 8, [online] URL: http://www.consecol.org/vol8/iss2/art2/
  3. Ahn S.R., Lee Y.J., Park G.A., Kim S.J., 2008, Analysis of future land use and climate change impact on stream discharge, Korean Society of Civil Engineers B Vol. 28, No. 2, 215-224. (in Korean)
  4. Chawla S., Shekhar S., Wu W.L., Ozesmi U., 2001, Modeling spatial dependencies for mining geospatial data. In: H. Miller and H. Han, eds. Geographic data mining and knowledge discovery. London: Taylor and Francis, pp. 131-159.
  5. Civerolo K., Hogrefe C., Lynn B., Resenthal J., Ku JY, Solecki W., Cox J., Small C., Rosenzweig C., Goldberg R., Knowlton K., Kinney P., 2007, Estimating the effects of increased urbanization on surface meteorology and ozone concentrations in the New York City metropolitan region, Atmospheric Environment Vol. 41, pp. 1803-1818. https://doi.org/10.1016/j.atmosenv.2006.10.076
  6. EPA (U. S. Environmental Protection Agency) 2009, Land-Use Scenarios : National-Scale Housing-Density Scenarios Consistent with Climate Change Storylines. Global Change Research Program, National Center for Environmental Assessment, Washington, DC; EPA/600/R-08/076F, Available from : National Technical Information Service, Springfield, VA.
  7. IGBP (International Geosphere-Biosphere Program, Committee on Global Change), 1988, Toward an Understanding of Global Change, National Academy Press, Washington, DC.
  8. Ju S.J., Kim S.J., 2012, Assessment of the impact of climate change on marine ecosystem in the south sea of Korea, Ocean and Polar Research Vol. 34, No. 2, pp. 197-199. (in Korean) https://doi.org/10.4217/OPR.2012.34.2.197
  9. KEI(Korea Environment Institute), 2011, Development of Future Land Use Scenarios Consistent with Climate Change Storylines. Korea Environment Institute, Seoul, South Korea. (in Korean)
  10. Kim D.H., Kim E.G., Park S.B., Kim H.G., Kim H.H., 2012, Article : analysis of the effect of climate change on the site index of Larix Leptolepis, Journal of Korean Forest Society Vol. 101, No. 1, pp. 53-61. (in Korean)
  11. Kim S.H., Cho J.G., Ham J.H., Do K.R., 2012, Changes of cultivation area of major fruit crops from the RCP 8.5 scenario in Korea, Korea Journal of Horticultural Science & technology Vol. 30, No. S2, pp. 108-109. (in Korean)
  12. KME(Korea Ministry of Environment), 2005, White paper of environmental conservation value assessment map. (in Korean) (in Korean)
  13. KSIS(Korean Statistical Information Service), 2012, http://kosis.kr (Accessed 25 October, 2012)
  14. Lee D.K., Sung S.Y., Jung H.C., 2010, Estimating the effect of climate change and land use change on surface runoff change - case study of 2020 Gwasheon city master plan, Journal of Korea Planners Association Vol. 45, No. 5, pp. 241-248. (in Korean)
  15. Landis J.D., Zang M., 1997, Modeling Land Use Change : The Next Generation of the California Urban Future Model. Submitted to the Land Use Modeling Workshop, USGS EROS Data Center, Sioux Falls, South Dakota, USA, 5-6 June.
  16. Lin Y.P., Cheng B.Y., Chu H.J., Chang T.K., Yu H.L., 2011, Assessing how heavy metal pollution and human activity are related by using logistic regression and kriging methods. Geoderma Vol. 163, pp. 275-282. https://doi.org/10.1016/j.geoderma.2011.05.004
  17. Lopez, E., Bocco, G., Mendoza, M., Duhau, E., 2001, Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and Urban Planning. Vol. 55, pp. 271-285. https://doi.org/10.1016/S0169-2046(01)00160-8
  18. Messerli B., 1997, Geography in a rapidly changing world. IGU Bulletin Vol. 47, No. 1, pp. 65-75.
  19. NABO(National Assembly Budget Office), 2012, Long-term financial outlook and analysis from 2012 to 2060, p. 18. (in Korean)
  20. Quan H.C., Lee B.G., Lee C.S., Ko J.W., 2011, The landslide probability analysis using logistic regression analysis and artificial neural network methods in Jeju, Journal of The Korean Society for Geo-Spatial Information System Vol. 19, No. 3, pp. 33-40.
  21. Rosenthal J.K., 2007, Links between the built environment, climate and population health: interdisciplinary environmental change research in New York city, Annals Academy of Medicine Singapore Vol. 36, pp. 834-846.
  22. Shoshany M., Goldshleger N., 2002, Land-use and population density changes in Israel-1950 to 1990: analysis of regional and local trends. Land Use Policy Vol. 19, No. 2, pp. 123-133. https://doi.org/10.1016/S0264-8377(02)00008-X
  23. Steffen W.L., Sanderson A., Tyson P.D., Jager J., Matson P.A., Moore B., Oldfield F., Richardson L., Schellnguber H.J., Turner B.L., Wasson R.J. (eds) 2004, Global Changes and the Earth System: A Planet under Pressure. Springer Verlag. Heidelberg.
  24. Stow D.A., Chen D.M., 2002, Sensitivity of multitemporal NOAA AVHRR data of an urbanizing region to land-use/land cover changes and misregistration. Remote Sensing of Environment Vol. 80, pp. 297-307. https://doi.org/10.1016/S0034-4257(01)00311-X
  25. van Vuuren, Edmonds D.P., Kainuma J., Riahi M., Thomson K., Hibbard A.K., Hurtt G.C., Kram T., Krey V., Lamarque J.F., Masui T., Meinshausen M., Nakicenovic N., Smith S.J., Rose S.K., 2011, The representative concentration pathways: an overview. Climatic Change Vol. 109, pp. 5-31. https://doi.org/10.1007/s10584-011-0148-z
  26. Ward P.J., 2009, The impact of land use and climate change on late Holocene and future suspended sediment yield of the Meuse catchment, Geomorphology Vol. 103, No. 3, pp. 389-400. https://doi.org/10.1016/j.geomorph.2008.07.006
  27. Xiao J., Shen Y., Ge J., Tateishia R., Tanga C., Liang Y., Huang Z., 2006, Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape and Urban Planning Vol. 75, pp. 69-80. https://doi.org/10.1016/j.landurbplan.2004.12.005
  28. Yoo S.J., Lee W.K., Oh S.H., Btun J.Y., 2012, Vulnerability assessment for public health to climate change using spatio-temporal information based on GIS, Korea Spatial Information Society Vol. 20, No. 2, pp. 13-24. (in Korean) https://doi.org/10.12672/ksis.2012.20.6.013

Cited by

  1. Impact of IPCC RCP Scenarios on Streamflow and Sediment in the Hoeya River Basin vol.22, pp.3, 2014, https://doi.org/10.7319/kogsis.2014.22.3.011
  2. Estimation of Future Land Cover Considering Shared Socioeconomic Pathways using Scenario Generators vol.9, pp.3, 2013, https://doi.org/10.15531/ksccr.2018.9.3.223