DOI QR코드

DOI QR Code

산림의 시가지 변화요인을 통한 잠재개발지 분석 및 관리방안

Analysis and Management of Potential Development Area Using Factor of Change from Forest to Build-up

  • 이지연 (고려대학교 환경생태공학과) ;
  • 임노을 (고려대학교 환경생태공학과) ;
  • 이성주 (한국환경연구원 환경평가본부) ;
  • 조효진 (고려대학교 환경생태공학과) ;
  • 성현찬 (고려대학교 오정리질리언스연구원) ;
  • 전성우 (고려대학교 환경생태공학과)
  • LEE, Ji-Yeon (Korea University Environmental Science & Ecological Engineering) ;
  • LIM, No-Ol (Korea University Environmental Science & Ecological Engineering) ;
  • LEE, Sung-Joo (Korea Environment Institute, Environmental Assessment Group) ;
  • CHO, Hyo-Jin (Korea University Environmental Science & Ecological Engineering) ;
  • SUNG, Hyun-Chan (Korea University OJEong Resilience Institute) ;
  • JEON, Seong-Woo (Korea University Environmental Science & Ecological Engineering)
  • 투고 : 2022.04.18
  • 심사 : 2022.05.17
  • 발행 : 2022.06.30

초록

국토의 지속가능한 개발과 보전을 위해서는 계획적인 개발과 효율적인 환경보전이 수반되어야 한다. 이를 위해서는 기존에 개발된 지역에 대한 분석을 통해 개발에 영향을 준 요인들을 도출하고, 개발압력이 높은 지역에 대하여 적절한 관리방안을 적용함으로써 개발과 보전이 조화를 이룰 수 있도록 유도할 수 있다. 본 연구는 용인시를 대상으로 토지피복이 산림에서 시가화·건조 지역으로 변화된 지역과 여러 가지 사회·지형·제한적 요소 간의 관계를 로지스틱 회귀분석을 통해 회귀식으로 구현하고, 잠재적 개발지를 분석하였다. 잠재 개발지 분석에 가장 큰 영향을 미치는 요인은 개발제한구역, 보호지역과 같은 제한요소이고, 영향력이 가장 작은 요인은 인구밀도였다. 용인시 산림의 약 148km2(52%)이 잠재적 개발지로 분석되었고, 잠재개발지 중 보호지역에 해당하면서 국토환경성평가지도 1등급으로 환경적 가치가 우수한 지역은 약 13km2으로 도출되었다. 개발 잠재력이 높은 보호지역은 수변구역과 특별대책지역으로 나타나 수변으로 구성된 자연경관이 우수한 지역들이 개발지역으로 선호되고 있었다. 보호지역은 개인의 재산권을 보호하기 위해 일부 행위에 대해 허가를 내주고 있으나 뚜렷한 허가기준이 부재하고, 허가로 인한 환경적 영향을 파악하고 있지 않다. 이는 보호지역이 제 역할을 하지 못하게 하는 요인으로 파악됨에 따라 명확한 예외적 허가기준과 환경영향 모니터링을 통해 관리될 필요가 있다.

For the sustainable development and conservation of the national land, planned development and efficient environmental conservation must be accompanied. To this end, it is possible to induce development and conservation to harmonize by deriving factors affecting development through analysis of previously developed areas and applying appropriate management measures to areas with high development pressure. In this study, the relationship between the area where the land cover changed from forest to urbanization and various social, geographical, and restrictive factors was implemented in a regression formula through logistic regression analysis, and potential development sites were analyzed for Yongin City. The factor that has the greatest impact on the analysis of potential development area is the restrict factors such as Green Belt and protected areas, and the factor with the least impact is the population density. About 148km2(52%) of Yongin-si's forests were analyzed as potential development area. Among the potential development sites, the area with excellent environmental value as a protected area and 1st grade on the Environment Conservation Value Assessment Map was derived as about 13km2. Protected areas with high development potential were riparian buffer zone and special measurement area, and areas with excellent natural scenery and river were preferred as development areas. Protected areas allow certain actions to protect individual property rights. However, there is no clear permit criteria, and the environmental impact of permits is not understood. This is identified as a factor that prevents protected areas from functioning properly. Therefore, it needs to be managed through clear exception permit criteria and environmental impact monitoring.

키워드

과제정보

본 결과물은 환경부의 재원으로 한국환경산업기술원의 ICT기반 환경영향평가 의사결정 지원 기술개발사업의 지원을 받아 연구되었습니다.(2020002990009)

참고문헌

  1. Bera, B., S. Saha and S. Bhattacharjee. 2020. Forest cover dynamics(1998 to 2019) and prediction of deforestation probability using binary logistic regression(BLR) model of Silabati watershed, India. Trees, Forests and People 2:100034. https://doi.org/10.1016/j.tfp.2020.100034
  2. Cho, Y.H and Y.K. Lee. 2010. A study on surveying and improving management of protected areas in Korea. Journal of the Korean Institute of Landscape Architecture 38(1):64-73.
  3. Cho, Y.S. 2014. Development of a system dynamics model for estimating the volume of forest resources and function of public benefit. Korean System Dynamics Review 15(3):5-36.
  4. Choi J.Y and Y.T. Cho. 2011. Research on the residential environment of new town & surrounding area. LHI Journal of Land, Housing, and Urban Affairs 2(1):1-8. https://doi.org/10.5804/LHIJ.2011.2.1.001
  5. Gayen, A., and S. Saha. 2018. Deforestation probable area predicted by logistic regression in Pathro river basin: a tributary of Ajay River. Spatial Information Research 26(1): 1-9. https://doi.org/10.1007/s41324-017-0151-1
  6. Heo, S and S.W. Lee. 2011. Quantile regression analysis on the residential land values in Seoul. Korea Industrial Economics Association 24(2):591-612.
  7. Hwang, H.K., C.M. Lee and M.K. Kim. 2008. Effect of visibility of the Han river on housing price. Housing Studies 16(2): 51-72.
  8. Jang, S.M and C.H. Yi. 2015. An estimation of the spatial development patterns based on the characteristic city indicators -The case of Gangnam district-. Journal of Korea spatial information society 23(3): 23-33. https://doi.org/10.12672/ksis.2015.23.3.023
  9. Joo, K.S and Y.W. Park. 2010. A study on the growth and spatial differentiation of housing market in Yongin City. Journal of Korean Geographical Society 45(2): 240-255.
  10. Joo Y.J., H. Sagong., S.K. Choi., S.B. Lee and J.E. Jeon. 2013. An environmentally friendly management plan for small-scale development projects in riparian areas. Korea Environment Institute 2013(9):1-111.
  11. Kim, D.J and H.S. Koo. 2012. Application of spatiotemporal pattern analysis to predict land-use change. Journal of Korea Planning Association 47(6):65-81.
  12. Kim, D.W., J.S. Kim and M.K. Kim. 2018. Application of relative favorability function model for land-use and land-cover change (LUCC) prediction in South Korea. Journal of the association of Korean geographers 7(3):463-478. https://doi.org/10.25202/JAKG.7.3.14
  13. Kim, G.H., G.S. Lee., O.S. Kim and H.S. Choi. 2019. Urban growth prediction using zoning district and logistic regression analysis. Journal of the association of Korean geographers 8(3):517-527. https://doi.org/10.25202/JAKG.8.3.12
  14. Kim, H.Y. 2016. Simulation of land use change by storylines of shared socioeconomic reference pathways. Journal of the Korean Association of Geographic Information Studies 19(2):1-13. https://doi.org/10.11108/KAGIS.2016.19.2.001
  15. Kim, H.Y and J.S. Kim. 2018. Analysis of characteristics and land use regulation of urban growth potential area in Busan metropolitan. Journal of the Korean Association of Geographic Information Studies 21(3):138-148. https://doi.org/10.11108/KAGIS.2018.21.3.138
  16. Kim, I.K and H.S. Kwon. 2018. Simulation of land use changes in Hanam city using an object-based cellular automata model. Journal of the Korean Association of Geographic Information Studies 21(4): 202-217. https://doi.org/10.11108/KAGIS.2018.21.4.202
  17. Kim, O.S and J.H. Yoon. 2015. Modeling land-change of South Korea under a business-as-usual scenario. Journal of the Korean Urban Geographical Society 18(3):121-135.
  18. Kim, T.J and H.S. Sakong. 2006. Determinants of urban sprawl in Seoul metropolitan region. Seoul Studies 7(2):95-116.
  19. Kim, T.Y., C.M. Lee., J.H. Cho and H. Park. 2007. Differential values of categorical landscape in apartment price. Journal of the Korea Real Estate Analysts Association 13(3):169-186.
  20. Korea Forest Service. 2021. 2020 Forest Ba sic Statistics. http://www.forest.go.kr/kfsweb/cop/bbs/selectBoardList.do?mn=NKFS_04_05_10&pageIndex=1&pageUnit=10&searchtitle=title&searchcont=&searchkey=&searchwriter=&searchdept=&searchWrd=&ctgryLrcls=CTGRY070&ntcStartDt=&ntcEndDt=&bbsId=BBSMSTR_1016. (Assessed May 18, 2022).
  21. Lee, D.K., D.H. Ryu., H.G. Kim and S.H. Lee. 2011. Analyzing the future land use change and its effects for the region of Yangpyeong-gun and Yeoju-gun in Korea with the Dyna-CLUE model. Journal of the Korea Society of Environmental Restoration Technology 14(6):119-130. https://doi.org/10.13087/KOSERT.2011.14.6.119
  22. Lee, S.J., J.E. Ryu and S.W. Jeon. 2020. An analysis of environmental policy effect on green space change using logistic regression model : The case of Ulsan metropolitan city. Journal of the Korea Society of Environmental Restoration Technology 23(4):13-30. https://doi.org/10.13087/KOSERT.2020.23.4.13
  23. Lee, Y.G., Y.H. Cho and S.J. Kim. 2016. Prediction of land-use change based on urban growth scenario in South Korea using CLUE-s model. Journal of the Korean Association of Geographic Information Studies 19(3):75-88. https://doi.org/10.11108/KAGIS.2016.19.3.075
  24. Park, I.H and S.R. Ha. 2013. Land use change prediction of Cheongju using SLEUTH model. Journal of Environmental Impact Assessment 22(1):109-116. https://doi.org/10.14249/EIA.2013.22.1.109
  25. Park, J.S and S.W. Park. 2015. Logistic regression with sampling techniques for the classification of imbalanced data. Journal of The Korean Data Analysis Society 17(4):1877-1888.
  26. Rasyid, A.R., N.P. Bhandary and R. Yatabe. 2016. Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters 3(1):1-16. https://doi.org/10.1186/s40677-016-0036-y
  27. Ryu, J.W and S.H. Chi. 2021. Politics of urban sprawl: A case of Urban Planning Ordinance Amendments in Yongin. Journal of the Korean Geographical Society 56(2): 149-160. https://doi.org/10.22776/KGS.2021.56.2.149
  28. Saha, S., M. Saha., K. Mukherjee., A. Arabameri., P.T.T. Ngo and G.C. Paul. 2020. Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India. Science of the Total Environment 730:139197. https://doi.org/10.1016/j.scitotenv.2020.139197
  29. Senaviratna, N.A.M.R and T.M.J.A. Cooray. 2019. Diagnosing multicollinearity of logistic regression model. Asian Journal of Probability and Statistics 5(2):1-9. https://doi.org/10.9734/ajpas/2019/v5i230132
  30. Seo, H.J and B.W. Jun. 2017. Analyzing the driving forces for the change of urban green spaces in Daegu with logistic regression and geographical detector. Journal of The Korean Association of Regional Geographers 23(2):403-419. https://doi.org/10.26863/jkarg.2017.05.23.2.403
  31. Yongin City. 2018. Yongin-si 2035 Urban Master Plan. http://www.yongin.go.kr/home/www/www09/www09_01/www09_01_01.jsp. (Assessed May 18, 2022)