공간최적화 모델을 활용한 환경계획의 공간화 방안

Suggestion for Spatialization of Environmental Planning Using Spatial Optimization Model

  • 윤은주 (서울대학교 협동과정 조경학) ;
  • 이동근 (서울대학교 조경지역시스템공학부) ;
  • 허한결 (서울대학교 협동과정 조경학) ;
  • 성현찬 (고려대학교 환경 GIS/RS 센터)
  • Yoon, Eun-Joo (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Lee, Dong-Kun (Dept. of Landscape Architecture and Rural System Engineering, Seoul National University) ;
  • Heo, Han-Kyul (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Sung, Hyun-Chan (Environmental GIS/RS Center, Korea University)
  • 투고 : 2018.03.09
  • 심사 : 2018.04.20
  • 발행 : 2018.04.30


Environmental planning includes resource allocation and spatial planning process for the conservation and management of environment. Because the spatialization of the environmental planning is not specifically addressed in the relevant statutes, it actually depends on the qualitative methodology such as expert judgement. The results of the qualitative methodology have the advantage that the accumulated knowledge and intuition of the experts can be utilized. However, it is difficult to objectively judge whether it is enough to solve the original problem or whether it is the best of the possible scenarios. Therefore, this study proposed a methodology to quantitatively and objectively spatialize various environmental planning. At first, we suggested a quantitative spatial planning model based on an optimization algorithm. Secondly, we applied this model to two kinds of environmental planning and discussed about the model performance to present the applicability. Since the models were developed based on conceptual study site, there was a limitation in showing possibility of practical use. However, we expected that this study can contribute to the fields related to environmental planning by suggesting flexible and novel methodology.


연구 과제번호 : 기후변화 영향 및 취약성 통합평가 모델

연구 과제 주관 기관 : 한국환경산업기술원


  1. Bae, M. 2017. A Study on Environmental Conservation Plan Based on Spatialization Method in Local Goverments. Envornmental Policy 25(2): 25-60.
  2. Cho, H.J., Kim, D.H., Shin, M.S., Kang, T. and Lee, M. 2015. Predicting the Goshawk's habitat area using Species Distribution Modeling: Case Study area Chungcheongbukdo, South Korea. Korean J. Environ Ecol. 29(3): 333-343.(In Korean)
  3. Choi, H.S. and Kwan, Y.H. 2009. Improving of Planning System for Sustainable Urban Development: Focus on Introducing Envrionmental and Ecological Planning. Environmental Policy 8(3): 27-51.
  4. Eum, J.H. 2016. Vulnerability Assessment to Urban Thermal Environment for Spatial Planning: A Case Study of Seoul, Korea. J. KILA 44(4): 109-120.(In Korean)
  5. Kim, E.Y., Jeon, S.W., Song, W.K. and Kwak, J.Y., Lee, J. 2012. Applilcation of ECVAM as a indicator for monitoring national environment in Korea. Environmental Policy 11(2): 3-16.(In Korean)
  6. Kim, H.J. and Cho, S.K. 1998. Applicaton of Hemeroby for Environmental Assessment with Environmental Planning: Focused on the Case "LG Village". Kor. J. Env. 12(3): 253-258.(In Korean)
  7. Kim, J.S. and Park, S.Y. 2017. Landslide susceptibility mapping using ensemble FR and LR models at the Inje area, Korea. The Korea Society For Geospatial Information System 25(1): 19-27.(In Korean)
  8. Kim, C.S., Lee, Y.D. and Lee, H.W. 2009. A case study of calculating flood inundation area by HEC-GeoRAS. Korean Society of Disaster & Security 2(4): 43-48.(In Korean)
  9. Kim, H., Lee, D.K., Mo, Y., Kil, S. and Park, C., Lee, S. 2013. Pridiction of landslides occurrence probability under climate change using MaxEnt model. Journal of Environmental Impact Assessment 22(1): 39-50.(In Korean)
  10. Kim, T.H. 2015. Linking and Utilizing Urban, Environmental, Disaster Prevention Spatial Data for a Climate Chnage Adaptation Spatial Planning. Environmental Policy 14(1): 85-112.
  11. Lee, W.S. An Evaluation of Natural-Ecological Function for Planning and Management on Forest. J. KILA 39(5): 1-11.
  12. Ministry of Environment. 2017. Developemnt of Economic Assessment Technique for Climate Change Impact and Adaptation Considering Uncertainties. R&D report(Development of technology in integrated management of climate change adaptation)(In Korean)
  13. Mo, Y.W., Park, J.H., Son, Y.H. and Lee, D.K. 2016. Technical Articles: Eastablishment of additional protected areas and applying payment for ecosystem services(PES) for sustainability of Suncheonman-Bay. The Korea Society of Environmental Restoration Technology 19(1): 171-184.(In Korean)
  14. Mo, W.Y., Lee, D.K., Kim, H.G., Baek, G.H. and Nam, S.J. 2013. Efficient establishment of protected areas in Pyoungchang county, Kangwon province to support spatial decision making. The Korea Society of Environmental Restoration Technology 16(1): 171-180.(In Korean)
  15. Park, H., Lim, J., Lee, J. and Lee, G. 2017. Predicting the potential distributions of invasive species using the Landsat imagery and MaxEnt: Focused on "Ambrosia trifida L. var. trifida" in Korean Demilitarized zone. The Korea Society of Environmental Restoration Technology 20(1): 1-12.(In Korean)
  16. Park, H.C., Lee, J.H. and Lee, G.G. 2014. Predicting the suitable habitat of the Pinus pumila under climate change. Journal of Environmental Impact Assessment. 23(5): 379-392.(In Korean)
  17. Yoon, E.J. and Lee, D.K. 2017. Basic study on spatial optimization model for sustainability using Genetic Algorithm: Based on literature review. The Korea Society of Environmental Restoration Technology 20(6): 103-119.(In Korean)
  18. Lee, Y.H., Hong, S.H., Na, C.S., Sohn, S.I., Kim, M.H., Kim, C.S. and Oh, Y. 2016. Predicting the suitable habitat of Amaranthus viridis based on climate change scenarios by MaxEnt. Korean Journal of Environmental Biology 34(4): 240-245.(In Korean)
  19. Korean Environment Institute. 2017. A study on development of evaluation indicators for the development-environmental plan linkage system(In Korean)
  20. Duan, H.F., Li, F. and Yan, H. 2016. Multi-objective optimal design of detention tanks in the urban stormwater drainage system: LID implementation and analysis. Water Resour. Manag. 30: 4635-4648.
  21. Eikelboom, T., Janssen, R. and Stewart, T.J. 2015. A spatial optimization algorithm for geodesign. Landsc Urban Plan 144: 10-21.
  22. Janssen, R. and Herwijnen, M. 2008. Multiobjective decision support for land-use planning. Environment and Planning B: Planning and Design 35: 740-756.
  23. Neema, M.N. and Ohgai, A. 2010. Multi-objective locaton modeling of urban parks and open spaces: Continuous optimization. Comput Environ Urban Syst 34: 359-376.
  24. Porta, J., Parapar, J., Doallo, R., Rivera, F. F., Sante, I. and Crecente, R. 2013. High performance genetic algorithm for land use planning. Computers, Environment and Urban Systems 37: 45-58.
  25. Schwaab, J., Deb, K., Goodman, E., Lautenbach, S., Van Strien,, M.J. and Gret-Regamey, A. 2017. Improving the performance of genetic algorithms for land-use allocation problems. International Journal of Geographical Information Science (DOI:10.1080/13658816.2017.1419249)
  26. Stewart, T.J., Janssen, R. and Van Herwijnen, M. 2004. A genetic Algorithm approach to multi objective land use planning. Computers & Operations Research 31(4): 2293-2313.
  27. Yoon, E.J., Lee, D.K., Kim, H.G., Kim, H. R., Jung, E. and Yoon, H. 2017. Multi-objective land use allocation considering landslide risk under climate change: Case study in Pyeongchang-gun, Korea. Sustainability 9(12).
  28. Zhang, K. and Chui, T.F.M. 2018. A comprehensive review of spatial allocation of LID-BMP-GI practices: Strategies and optimization tool. Science of the Total Environmnet 621: 915-929.
  29. Zhang, W. and Huang, B. 2015. Soil erosion evaluation in a rapidly urbanizing city (Shenzhen, China) and implementation of spatial land-use optimization. Environ Sci Pollut Res 22: 4475-4490.
  30. Zitzler, E., Laumanns, M. and Bleuler, S. 2004. A tutorial on evolutionary multiobjective optimization. Technical Report. Computer Engineering and Networks Laboratory (TIK), Zurich, Switzerland: Swiss Federal Institute of Technology (ETH) ZURICH.

피인용 문헌

  1. Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk vol.14, pp.2, 2019,