Skip to main content
Top
Published in: Environmental Earth Sciences 2/2020

01-01-2020 | Original Article

Analysis of the future land cover change in Beijing using CA–Markov chain model

Authors: Yingchun Huang, Bogang Yang, Miao Wang, Bowen Liu, Xudong Yang

Published in: Environmental Earth Sciences | Issue 2/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the acceleration of urbanization, the degree of human influence on land use is getting increasingly deeper. To research the future land use, this paper collects 5 years of land use from 1992 to 2012 from the government, and integrates cellular automata and Markov models to predict the land use change in 2020 and in 2030 in the Idrisi software. The key findings: (1) in the period 1992–2012, significant land cover type has been changed and the rate of change for farmland, ecological land, construction land and other uses, is greater than 6%; (2) the transition areas from farmland to construction land are 778 km2, the largest among land use patterns, and they are mainly concentrated to the north, east and south of the city center; (3) according to the 2030 projection, land use change will result in a reduction in farmland, from 3633.29 km2 to 3126.21 km2 (22.14–19.05% in percentage), and an increase in construction land, from 2976.87 km2 to 3239.44 km2 (18.14–19.74% in percentage), relative to the total area in 2012. According to the urbanization process, the characteristic of land use change is from original dramatic to the late slowdown, then obtain a stable state finally. But from the results of prediction, the construction land will continue to increase while the farmland will decrease, which counteracts the ability to reach the harmony steady state, and policies related to reduced urban construction development or economic sanctions need to be introduced into land use planning.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Footnotes
1
Data sources come from the Chinese city statistics year book.
 
2
Data come from the Beijing Bureau of Statistics and the Beijing Municipal Bureau of Land Use and Resources.
 
3
The built-up area refers to the downtown, being a part of construction land.
 
4
Water conservancy facilities is different from water bodies, water conservancy facilities is just one part of water bodies.
 
Literature
go back to reference Aaviksoo K (1995) Simulating vegetation dynamics and land-use in a mire landscape using a Markov model. Landsc Urban Plan 31:129–142 Aaviksoo K (1995) Simulating vegetation dynamics and land-use in a mire landscape using a Markov model. Landsc Urban Plan 31:129–142
go back to reference Andaryani S, Trolle DR, Nikjoo M, Moghadam MR, Mokhtari D (2019) Forecasting near-future impacts of land use and climate change on the Zilbier river hydrological regime, northwestern Iran. Environ Earth Sci 78:8193–8197 Andaryani S, Trolle DR, Nikjoo M, Moghadam MR, Mokhtari D (2019) Forecasting near-future impacts of land use and climate change on the Zilbier river hydrological regime, northwestern Iran. Environ Earth Sci 78:8193–8197
go back to reference Baker WL (1989) A review of models of landscape change. Landsc Ecol 2:112–134 Baker WL (1989) A review of models of landscape change. Landsc Ecol 2:112–134
go back to reference Broth A, Hoekman SK, Unnasch S (2013) A review of variability in indirect land use change assessment and modeling in biofuel policy. Environ Sci Policy 29:147–157 Broth A, Hoekman SK, Unnasch S (2013) A review of variability in indirect land use change assessment and modeling in biofuel policy. Environ Sci Policy 29:147–157
go back to reference Chen MX, Ye C, Zhou Y (2011) Urbanization rate and its policy implications: discussion and development of Northam’s curve. Geogr Res 30:1500–1507 (in Chinese) Chen MX, Ye C, Zhou Y (2011) Urbanization rate and its policy implications: discussion and development of Northam’s curve. Geogr Res 30:1500–1507 (in Chinese)
go back to reference Chen X, Yu SX, Zhang YP (2013) Evaluation of spatiotemporal dynamics of simulated land use/cover in China using a probabilistic cellular automata–Markov model. Pedosphere 23:243–255 Chen X, Yu SX, Zhang YP (2013) Evaluation of spatiotemporal dynamics of simulated land use/cover in China using a probabilistic cellular automata–Markov model. Pedosphere 23:243–255
go back to reference Cui XG, Yan TL, Zhu DH, Niu FQ, Zhang XD (2007) Applying a GIS-based model to collect information on agricultural land-use change in Beijing. New Zeal J Agr Res 50:1073–1081 Cui XG, Yan TL, Zhu DH, Niu FQ, Zhang XD (2007) Applying a GIS-based model to collect information on agricultural land-use change in Beijing. New Zeal J Agr Res 50:1073–1081
go back to reference Du JF, Thill JC, Peiser RB, Feng CC (2014) Urban land market and land-use changes in post-reform China: a case study of Beijing. Landsc Urban Plan 124:118–128 Du JF, Thill JC, Peiser RB, Feng CC (2014) Urban land market and land-use changes in post-reform China: a case study of Beijing. Landsc Urban Plan 124:118–128
go back to reference Eastman JR (2009) IDRISI guide to GIS and image processing Accessed in IDRISI Selva 17 (pp. 182e185). Worcester, MA: Clark University. In: IDRISI guide to GIS and image processing Accessed in IDRISI Selva 17. Clark University, Worcester Eastman JR (2009) IDRISI guide to GIS and image processing Accessed in IDRISI Selva 17 (pp. 182e185). Worcester, MA: Clark University. In: IDRISI guide to GIS and image processing Accessed in IDRISI Selva 17. Clark University, Worcester
go back to reference Eskinder G, Oagile D, Reuben S, Eagilwe S, Amanuel Z (2017) Cellular automata and Markov Chain (CA_Markov) model-based predictions of future land use and land cover scenarios (2015–2033) in Raya, northern Ethiopia. Model Earth Syst Environ 3(4):1245–1262 Eskinder G, Oagile D, Reuben S, Eagilwe S, Amanuel Z (2017) Cellular automata and Markov Chain (CA_Markov) model-based predictions of future land use and land cover scenarios (2015–2033) in Raya, northern Ethiopia. Model Earth Syst Environ 3(4):1245–1262
go back to reference Fatemeh J, Seyed HM, Abdolrassoul S, Fatemeh P (2018) Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting. Environ Monit Assess 190:331–349 Fatemeh J, Seyed HM, Abdolrassoul S, Fatemeh P (2018) Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting. Environ Monit Assess 190:331–349
go back to reference Freier KP, Schneider UA, Finckh M (2011) Dynamic interactions between vegetation and land use in semi-arid Morocco: Using a Markov process for modeling rangelands under climate change. Agr Ecosyst Environ 140:462–472 Freier KP, Schneider UA, Finckh M (2011) Dynamic interactions between vegetation and land use in semi-arid Morocco: Using a Markov process for modeling rangelands under climate change. Agr Ecosyst Environ 140:462–472
go back to reference Gong WF, Yuan L, Fan WY, Stott P (2015) Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata–Markov modelling. Int J Appl Earth Obs 34:207–216 Gong WF, Yuan L, Fan WY, Stott P (2015) Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata–Markov modelling. Int J Appl Earth Obs 34:207–216
go back to reference Guan DJ, Gao WJ, Watari K, Fukahori H (2008) Land use change of Kitakyushu based on landscape ecology and Markov model. J Geogr Sci 18:455–468 Guan DJ, Gao WJ, Watari K, Fukahori H (2008) Land use change of Kitakyushu based on landscape ecology and Markov model. J Geogr Sci 18:455–468
go back to reference Guan DJ, Li HF, Inohae T, Su WC, Nagaie T, Hokao K (2011) Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol Model 222:3761–3772 Guan DJ, Li HF, Inohae T, Su WC, Nagaie T, Hokao K (2011) Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol Model 222:3761–3772
go back to reference Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015) Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov–CA. Appl Geogr 63:101–112 Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015) Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov–CA. Appl Geogr 63:101–112
go back to reference Hao W, Zhen L, Keith CC, Shi WZ, Fang LC, Lin AQ, Zhou J (2019) Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. Int j Geogr Inf Sci 33(5):1040–1061 Hao W, Zhen L, Keith CC, Shi WZ, Fang LC, Lin AQ, Zhou J (2019) Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. Int j Geogr Inf Sci 33(5):1040–1061
go back to reference Hou JW (2011) Economic reform of China: cause and effects. Soc Sci J 48:419–434 Hou JW (2011) Economic reform of China: cause and effects. Soc Sci J 48:419–434
go back to reference Hu YC, Zheng YM, Zheng XQ (2013) Simulation of land-use scenarios for Beijing using CLUE-S and Markov composite models. Chinese Geogr Sci 23:92–100 Hu YC, Zheng YM, Zheng XQ (2013) Simulation of land-use scenarios for Beijing using CLUE-S and Markov composite models. Chinese Geogr Sci 23:92–100
go back to reference Huang YC, Nian PH, Zhang WX (2015) The prediction of interregional land use differences in Beijing: a Markov model. Environ Earth Sci 73:4077–4090 Huang YC, Nian PH, Zhang WX (2015) The prediction of interregional land use differences in Beijing: a Markov model. Environ Earth Sci 73:4077–4090
go back to reference Itami RM (1994) Simulating spatial dynamics—cellular-automata theory. Landsc Urban Plan 30:27–47 Itami RM (1994) Simulating spatial dynamics—cellular-automata theory. Landsc Urban Plan 30:27–47
go back to reference Kuang WH (2012) Spatio-temporal patterns of intra-urban land use change in Beijing, China between 1984 and 2008. Chinese Geogr Sci 22:210–220 Kuang WH (2012) Spatio-temporal patterns of intra-urban land use change in Beijing, China between 1984 and 2008. Chinese Geogr Sci 22:210–220
go back to reference Kuang WH, Liu JY, Dong JW, Chi WF, Zhang C (2015) The rapid and massive urban and industrial land expansions in China between 1990 and 2010: a CLUD-based analysis of their trajectories, patterns, and drivers. Landsc Urban Plan 145:21–33 Kuang WH, Liu JY, Dong JW, Chi WF, Zhang C (2015) The rapid and massive urban and industrial land expansions in China between 1990 and 2010: a CLUD-based analysis of their trajectories, patterns, and drivers. Landsc Urban Plan 145:21–33
go back to reference Li J (2014) Land sale venue and economic growth path: evidence from China's urban land market. Habitat Int 41:307–313 Li J (2014) Land sale venue and economic growth path: evidence from China's urban land market. Habitat Int 41:307–313
go back to reference Lu YT, Wu PH, Ma XS, Li XH (2019) Detection and prediction of land use/land cover change using spatiotemporal data fusion and the cellular automata–Markov model. Environ Monit Assess 191:67–85 Lu YT, Wu PH, Ma XS, Li XH (2019) Detection and prediction of land use/land cover change using spatiotemporal data fusion and the cellular automata–Markov model. Environ Monit Assess 191:67–85
go back to reference Menon S, Pontius RG, Rose J, Khan ML, Bawa KS (2001) Identifying conservation-priority areas in the tropics: a land-use change modeling approach. Conserv Biol 15:501–512 Menon S, Pontius RG, Rose J, Khan ML, Bawa KS (2001) Identifying conservation-priority areas in the tropics: a land-use change modeling approach. Conserv Biol 15:501–512
go back to reference Moon Y, Zhang YS, Song Y, Park E, Moon HS (2013) Multivariate statistical analysis and 3D-coupled Markov chain modeling approach for the prediction of subsurface heterogeneity of contaminated soil management in abandoned Guryong Mine Tailings, Korea. Environ Earth Sci 68:1527–1538 Moon Y, Zhang YS, Song Y, Park E, Moon HS (2013) Multivariate statistical analysis and 3D-coupled Markov chain modeling approach for the prediction of subsurface heterogeneity of contaminated soil management in abandoned Guryong Mine Tailings, Korea. Environ Earth Sci 68:1527–1538
go back to reference Muller MR, Middleton J (1994) A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landsc Ecol 9:151–157 Muller MR, Middleton J (1994) A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landsc Ecol 9:151–157
go back to reference Mulligan GF (2013) Revisiting the urbanization curve. Cities 32:113–122 Mulligan GF (2013) Revisiting the urbanization curve. Cities 32:113–122
go back to reference Nourqolipour R, Shariff ARBM, Ahmad NB, Balasundram SK, Sood AM, Buyong T, Amiri F (2015) Multi-objective-based modeling for land use change analysis in the South West of Selangor, Malaysia. Environ Earth Sci 74:4133–4143 Nourqolipour R, Shariff ARBM, Ahmad NB, Balasundram SK, Sood AM, Buyong T, Amiri F (2015) Multi-objective-based modeling for land use change analysis in the South West of Selangor, Malaysia. Environ Earth Sci 74:4133–4143
go back to reference Pijanowski BC, Brown DG, Shellito BA, Manik GA (2002) Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Comput Environ Urban 26:553–575 Pijanowski BC, Brown DG, Shellito BA, Manik GA (2002) Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Comput Environ Urban 26:553–575
go back to reference Pontius GR, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19:243–265 Pontius GR, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19:243–265
go back to reference Ruiz Benito P, Cuevas JA, Bravo De La Parra R, Prieto F, Garcia Del Barrio JM, Zavala MA (2010) Land use change in a Mediterranean metropolitan region and its periphery: assessment of conservation policies through CORINE Land Cover data and Markov models. Forest Syst 19:315–328 Ruiz Benito P, Cuevas JA, Bravo De La Parra R, Prieto F, Garcia Del Barrio JM, Zavala MA (2010) Land use change in a Mediterranean metropolitan region and its periphery: assessment of conservation policies through CORINE Land Cover data and Markov models. Forest Syst 19:315–328
go back to reference Sang LL, Zhang C, Yang JY, Zhu DH, Yun WJ (2011) Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model 54:938–943 Sang LL, Zhang C, Yang JY, Zhu DH, Yun WJ (2011) Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model 54:938–943
go back to reference Theobald DM, Hobbs NT (1998) Forecasting rural land-use change: a comparison of regression- and spatial transition-based models. Geogr Environ Model 2:65–82 Theobald DM, Hobbs NT (1998) Forecasting rural land-use change: a comparison of regression- and spatial transition-based models. Geogr Environ Model 2:65–82
go back to reference Trubins R (2013) Land-use change in southern Sweden: before and after decoupling. Land Use Policy 33:161–169 Trubins R (2013) Land-use change in southern Sweden: before and after decoupling. Land Use Policy 33:161–169
go back to reference Veldkamp A, Lambin EF (2001) Predicting land-use change. Agr Ecosyst Environ 85:1–6 Veldkamp A, Lambin EF (2001) Predicting land-use change. Agr Ecosyst Environ 85:1–6
go back to reference Wei YD, Ye X (2014) Urbanization, urban land expansion and environmental change in China. Stoch Env Res Risk A 28:757–765 Wei YD, Ye X (2014) Urbanization, urban land expansion and environmental change in China. Stoch Env Res Risk A 28:757–765
go back to reference Weng QH (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J Environ Manag 64:273–284 Weng QH (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J Environ Manag 64:273–284
go back to reference Weon SH, Kue-Young K, Sungwook C, Jina J, Na-Hyun J, Eungyu P (2014) Non-parametric simulations-based conditional stochastic predictions of geologic heterogeneities and leakage potentials for hypothetical CO2 sequestration sites. Environ Earth Sci 71:2739–2752 Weon SH, Kue-Young K, Sungwook C, Jina J, Na-Hyun J, Eungyu P (2014) Non-parametric simulations-based conditional stochastic predictions of geologic heterogeneities and leakage potentials for hypothetical CO2 sequestration sites. Environ Earth Sci 71:2739–2752
go back to reference Wolfram S (1984) Cellular automata as models of complexity. Nature 311:419–424 Wolfram S (1984) Cellular automata as models of complexity. Nature 311:419–424
go back to reference Wu Q, Li HQ, Wang RS, Paulussen J, He Y, Wang M, Wang BH, Wang Z (2006) Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landsc Urban Plan 78:322–333 Wu Q, Li HQ, Wang RS, Paulussen J, He Y, Wang M, Wang BH, Wang Z (2006) Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landsc Urban Plan 78:322–333
go back to reference Wu L, Liu X, Ma XY (2018) Prediction of land-use change and its driving forces in an ecological restoration watershed of the Loess hilly region. Environ Earth Sci 77:7413–7419 Wu L, Liu X, Ma XY (2018) Prediction of land-use change and its driving forces in an ecological restoration watershed of the Loess hilly region. Environ Earth Sci 77:7413–7419
go back to reference Xie YC, Fang CL, Lin GCS, Gong HM, Qiao B (2007) Tempo-spatial patterns of land use changes and urban development in globalizing China: a study of Beijing. Sensors 7:2881–2906 Xie YC, Fang CL, Lin GCS, Gong HM, Qiao B (2007) Tempo-spatial patterns of land use changes and urban development in globalizing China: a study of Beijing. Sensors 7:2881–2906
go back to reference Yang X, Zheng XQ, Lv LN (2012) A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol Model 233:11–19 Yang X, Zheng XQ, Lv LN (2012) A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol Model 233:11–19
go back to reference Ye BY, Bai ZK (2008) Simulating land use/cover changes of Nenjiang County based on CA–Markov model. In: Li DL (ed) International Federation For Information Processing, vol 258. Springer, New York, pp 321–329 Ye BY, Bai ZK (2008) Simulating land use/cover changes of Nenjiang County based on CA–Markov model. In: Li DL (ed) International Federation For Information Processing, vol 258. Springer, New York, pp 321–329
go back to reference Zhang N, Fang LN, Zhou J, Song JP, Jiang J (2010) The study on spatial expansion and its driving forces in the urban fringe of Beijing. Geogr Res 29:471–480 (in Chinese) Zhang N, Fang LN, Zhou J, Song JP, Jiang J (2010) The study on spatial expansion and its driving forces in the urban fringe of Beijing. Geogr Res 29:471–480 (in Chinese)
Metadata
Title
Analysis of the future land cover change in Beijing using CA–Markov chain model
Authors
Yingchun Huang
Bogang Yang
Miao Wang
Bowen Liu
Xudong Yang
Publication date
01-01-2020
Publisher
Springer Berlin Heidelberg
Published in
Environmental Earth Sciences / Issue 2/2020
Print ISSN: 1866-6280
Electronic ISSN: 1866-6299
DOI
https://doi.org/10.1007/s12665-019-8785-z

Other articles of this Issue 2/2020

Environmental Earth Sciences 2/2020 Go to the issue