References
- Ahn KW.Shin YK.Kim JY.Lee YK.Lim JC.Ha JW.Kwon HS.Suh JH and Kim MJ. 2015. A review on the public appeals of the ecosystem and nature map. Journal of Environmental Impact Assessment. 24(1) : 99-109. (in Korean) https://doi.org/10.14249/eia.2015.24.1.99
- Araya YH and Cabral P. 2010. Analysis and modeling of urban land cover change in Setubal Sesimbra, Portugal. Remote Sensing. 2(6) : 1549-1563. https://doi.org/10.3390/rs2061549
- Eastman JR. 2012. IDRISI-TerrSet. Worcester. MA: Clark University.
- Eastman JR. 2016. IDRISI Terrset manual . Worcester. MA: Clark University.
- El-Hallaq MA and Habboub MO. 2015. Using cellular automata-markov analysis and multi criteria evaluation for predicting the shape of the Dead Sea. Advances in Remote Sensing. 4(01) : 83-95. https://doi.org/10.4236/ars.2015.41008
- ESRI AD. 2011. Release 10.5. Environmental Systems Research Institute. Redlands. CA.
- Jin S.Yang L.Danielson P.Homer C.Fry J and Xian G. 2013. A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment. 132: 159-175. https://doi.org/10.1016/j.rse.2013.01.012
- Jung TJ.Song IB.Lee JS.Lee SJ.Cho KJ.Song KH.Kim KD.Cha JY.Cho JS.Leem HS and Joung HJ. 2017. The analysis on causes of areas with public appeals to the ecosystem and nature map. Journal of the Korea Society of Environmental Restoration Technology. 20(1) : 25-34. (in Korean) https://doi.org/10.13087/kosert.2017.20.1.25
- KFS. 2019. Statistical yearbook of forestry. Korea Forest Service. (in Korean)
- KRIHS. 2005. Policy directions for the efficient management of forestland. Korea Research Institute for Human Settlements. (in Korean)
- Kim SJ and Lee YJ. 2007. The effect of spatial scale and resolution in the prediction of future land use using CA-Markov technique. Journal of the Korean Association of Geographic Information Studies. 10(2) : 57-69. (in Korean)
- Lee YH and Kim SJ. 2007. A Modified CA-Markov Technique for Prediction of Future Land Use Change. Journal of the Korean Society of Civil Engineers D. 27(6D) : 809-817. (in Korean)
- Lee DK.Kim JU and Park C. 2010. A prediction of forest vegetation based on land cover change in 2090. Journal of Environmental Impact Assessment. 19(2) : 117-125. (in Korean)
- Lee DK.Ryu DH.Kim HG and Lee SH. 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. (in Korean) https://doi.org/10.13087/KOSERT.2011.14.6.119
- Li X.Lin J.Chen Y.Liu X and Ai B. 2013. Calibrating cellular automata based on landscape metrics by using genetic algorithms. International Journal of Geographical Information Science. 27(3) : 594-613. https://doi.org/10.1080/13658816.2012.698391
- Li X and Yeh AGO. 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science. 16(4) : 323-343. https://doi.org/10.1080/13658810210137004
- Li X and Yeh AGO. 2004. Data mining of cellular automata's transition rules. International Journal of Geographical Information Science 18(8) : 723-744. https://doi.org/10.1080/13658810410001705325
- Mas JF. 1999. Monitoring land-cover changes -a comparison of change detection techniques. International Journal of Remote Sensing. 20(1) : 139-152. https://doi.org/10.1080/014311699213659
- NIE. 2018. Research for ecosystem and nature map. National Institute of Ecology. (in Korean)
- Oh KY.Lee MJ and No WY. 2016. A study on the improvement of sub-divided land cover map classification system -based on the land cover map by ministry of environment. Korean Journal of Remote Sensing. 32(2) : 105-118. (in Korean) https://doi.org/10.7780/kjrs.2016.32.2.4
- Park GA and Kim JS. 2007a. Prediction of the urbanization progress using factor analysis and CA-Markov technique. Journal of the Korean Society of Agricultural Engineers. 49(6) : 113-122. (in Korean)
- Park SH and Kim JI. 2007b. The characteristics of land use change at the urban fringe. Journal of the Korean Association of Geographic Information Studies. 10(2) : 35-45. (in Korean)
- R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna. Austria. URL https://www.R-project.org/.
- Sang L.Zhang C.Yang J.Zhu D and Yun W. 2011. Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Mathematical and Computer Modeling. 54(3-4) : 938-943. https://doi.org/10.1016/j.mcm.2010.11.019
- Sangermano F.Eastman JR and Zhu H. 2010. Similarity weighted instance-based learning for the generation of transition potentials in land use change modeling. Transactions in GIS. 14(5) : 569-580. https://doi.org/10.1111/j.1467-9671.2010.01226.x
- Seo HJ and Jun BW. 2017. Modeling the spatial dynamics of urban green spaces in Daegu with a CA-Markov model. Journal of the Korean Geographical Society. 52(1) : 123-141. (in Korean)
- Singh A. 1989. Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing. 10(6) : 989-1003. https://doi.org/10.1080/01431168908903939
- Subedi P.Subedi K and Thapa B. 2013. Application of a hybrid cellular automata-markov(CA-Markov) model in land-use change prediction -a case study of Saddle Creek Drainage basin. Florida. Applied Ecology and Environmental Sciences. 1(6) : 126-132. https://doi.org/10.12691/aees-1-6-5
- Weng Q. 2002. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management. 64(3) : 273-284. https://doi.org/10.1006/jema.2001.0509