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NTIS 바로가기한국지리정보학회지 = Journal of the Korean Association of Geographic Information Studies, v.24 no.4, 2021년, pp.99 - 112
곽두안 (국립산림과학원 산림정책연구과) , 박소희 (국립산림과학원 산림정책연구과)
This study was performed to predict spatial change of future forestland area in South Korea at regional level for supporting forest-related plans established by local governments. In the study, land use was classified to three types which are forestland, agricultural land, and urban and other lands....
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Bae, J.S., R.W. Joo and Y.S. Kim. 2012. Forest transition in South Korea: Reality, path and drivers. Land Use Policy 29(1):198-207.
Berkhoff, K. and S. Herrmann. 2009. Modeling land use change: A GIS based modeling framework to support integrated land use planning(NabanFrame). In: Advances in GIScience. Springer, Berlin, Heidelberg. pp.309-328.
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Hu, Z. and C.P. Lo. 2007. Modeling urban growth in Atlanta using Logistic Regression. Remote Sensing of Environment 31(6):667-688.
Kim, J.H. 2002. An analysis of land use change in the urban fringe using GIS and Logistic Regression in Korea. The Korea Spatial Planning Review 33:175- 200.
Kim, J., J. Park, I. Song, J.H. Song, S.M. Jun and M.S. Kang. 2015a. Analysis of land use change using RCP-based Dyna-CLUE model in the Hwangguji river watershed. Journal of the Korean Society and Rural Planning 21(2):33-49.
Kim, O.S., S. Ahn, J.H. Yoon, S.J. Bin and K.H. Kim. 2015b. Development of integrated land-use model to support climate change adaptation policy-Part I, Korea Environment Institute, Sejong, Korea. 119pp.
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.
Lee, Y.J. and S.J. Kim. 2007. A modified CA-Markov technique for prediction of future land use change. KSCE Journal of Civil Engineering 27(6D):809-817.
Ministry of Environment. 2019. A report on Korean-sustainable development goals(KSDGs). Ministry of Environment Presidential Commission on Sustainable Development, Sejong, Korea. 50pp.
Mirici, M.E., S. Berberoglu, A. Akin and O. Satir. 2018. Land use/cover change modelling in a Mediterranean rural landscape using Multi-Layer Perceptron and Markov Chain (MLPMC). Applied Ecology and Environmental Research 16(1):467-486.
Mitsova, D., W. Shuster and X. Wang. 2011. A Cellular Automata model of land cover change to integrate urban growth with open space conservation. Landscape Urban Plan 99(2):141-153.
Nasiri, V., A.A. Darvishsefat, R. Rafiee, A. Shirvany and M.A. Hemat. 2019. Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis(case study: Arasbaran region, Iran). Journal of Forestry Research 30(3):943-957.
Park, H. and K. Cho. 2008. An empirical analysis of land use changes in Daegu metropolitan city by using probabilistic choice model. The Korea Spatial Planning Review 58:137-150.
Seo, H.J. and B.W. Jun. 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.
Shen, L., J.B. Li, R. Wheate, J. Yin and S.S. Paul. 2020. Multi-Layer Perceptron Neural Network and Markov Chain based geospatial analysis of land use and land cover change. Journal of Environmental Informatics Letters 3(1):29-39.
Verburg, P.H. and K.P. Overmars. 2009. Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology 24(9):1167-1181.
Verburg, P.H., W. Soepboer, A. Veldkamp, R. Limpiada, V. Espaldon and S.S.A. Mastura. 2002. Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management 30(3):391-405.
Wolfram, S. 1984. Computation theory of Cellular Automata. Communications in Mathematical Physics 96(1):15-57.
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