Skip to main content
SpringerLink
Log in

Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

  • Research Article
  • Published:
Frontiers of Earth Science Aims and scope Submit manuscript

Abstract

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001–2009 and 2005–2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009–2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Besag J (1972). Nearest-neighbour systems and the auto-logistic model for binary data. J R Stat Soc B, 34(1): 75–83

    Google Scholar 

  • Castella J C, Verburg P H (2007). Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam. Ecol Modell, 202(3–4): 410–420

    Article  Google Scholar 

  • Chen J, Gong P, He C Y, Luo W, Tamura M, Shi P (2002). Assessment of the urban development plan of Beijing by using a CA-based urban growth model. Photogramm Eng Remote Sensing, 68(10): 1063–1071

    Google Scholar 

  • Dai S P, Zhang B (2013). Land use change scenarios simulation in the middle reaches of the Heihe River Basin based on CLUE-S model: a case of Ganzhou district of Zhangye city. Journal of Natural Resources, 28(2): 336–348 (in Chinese)

    Google Scholar 

  • Duan Z Q, Verburg P H, Zhang F R, Yu Z R (2004). Construction of a land-use change simulation model and its application in Haidian district, Beijing. Acta Geogr Sin, 59(6): 1037–1047 (in Chinese)

    Google Scholar 

  • Hu Y C, Zheng Y M, Zheng X Q (2013). Simulation of land-use scenarios for Beijing using CLUE-S and Markov composite models. Chin Geogr Sci, 23(1): 92–100

    Article  Google Scholar 

  • Jiang WG, Chen Z, Lei X, Jia K, Wu Y F (2015). Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model. J Geogr Sci, 25(7): 836–850

    Article  Google Scholar 

  • Jiang Y, Liu J, Cui Q, An X H, Wu C X (2011). Land use/land cover change and driving force analysis in Xishuangbanna region in 1986–2008. Front Earth Sci, 5(3): 288–293

    Google Scholar 

  • Li H X, Liu G H, Fu B J (2012). Estimation of regional evapotranspiration in Alpine area and its response to land use change: a case study in Three-River Headwaters region of Qinghai-Tibet plateau, China. Chin Geogr Sci, 22(4): 437–449

    Article  Google Scholar 

  • Lin Y P, Chu H J, Wu C F, Verburg P H (2011). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study. Int J Geogr Inf Sci, 25(1): 65–87

    Article  Google Scholar 

  • Lin Y Z, Deng X Z, Li X, Ma E J (2014). Comparison of multinomial logistic regression and logistic regression: Which is more efficient in allocating land use? Front Earth Sci, 8(4): 512–523

    Article  Google Scholar 

  • Liu J Y, Deng X Z (2010). Progress of the research methodologies on the temporal and spatial process of LUCC. Chin Sci Bull, 55(14): 1354–1362

    Article  Google Scholar 

  • Liu M, Hu Y M, Chang Y, He X Y, Zhang W (2009). Land use and land cover change analysis and prediction in the upper reaches of the Minjiang River, China. Environ Manage, 43(5): 899–907

    Article  Google Scholar 

  • Liu M, Hu Y M, Zhang W, Zhu J J, Chen H W, Xi F M (2011). Application of land-use change model in Guiding regional planning: a case study in Hun-Taizi river watershed, northeast China. Chin Geogr Sci, 21(5): 609–618

    Article  Google Scholar 

  • Liu M, Li C L, Hu Y M, Sun F Y, Xu Y Y, Chen T (2014a). Combining CLUE-S and SWAT models to forecast land use change and nonpoint source pollution impact at a watershed scale in Liaoning Province, China. Chin Geogr Sci, 24(5): 540–550

    Article  Google Scholar 

  • Liu X P, Li X, Liu L, He J Q, Ai B (2008a). A bottom-up approach to discover transition rules of cellular automata using ant intelligence. Int J Geogr Inf Sci, 22(11–12): 1247–1269

    Article  Google Scholar 

  • Liu X P, Li X, Shi X,Wu S K, Liu T (2008b). Simulating complex urban development using kernel-based non-linear cellular automata. Ecol Modell, 211(1–2): 169–181

    Article  Google Scholar 

  • Liu X P, Li X, Shi X, Zhang X H, Chen YM (2010). Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. Int J Geogr Inf Sci, 24(5): 783–802

    Article  Google Scholar 

  • Liu X P, Ma L, Li X, Ai B, Li S Y, He Z J (2014b). Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. Int J Geogr Inf Sci, 28(1): 148–163

    Article  Google Scholar 

  • Luo G P, Yin C Y, Chen X, Xu W Q, Lu L (2010). Combining system dynamic model and CLUE-S model to improve land use scenario analyses at regional scale: a case study of Sangong watershed in Xinjiang, China. Ecol Complex, 7(2): 198–207

    Article  Google Scholar 

  • Luo J, Zhan J Y, Lin Y Z, Zhao C H (2014). An equilibrium analysis of the land use structure in the Yunnan Province, China. Front Earth Sci, 8(3): 393–404

    Article  Google Scholar 

  • Overmars K P, de Koning G H J, Veldkamp A (2003). Spatial autocorrelation in multi-scale land use models. Ecol Modell, 164(2–3): 257–270

    Article  Google Scholar 

  • Overmars K P, Verburg P H, Veldkamp A (2007). Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model. Land Use Policy, 24(3): 584–599

    Article  Google Scholar 

  • Pan Y, Liu Y H, Wang J, Yu Z R (2011). Non-point pollution control for landscape conservation analysis based on CLUE-S simulations in Miyun county. Acta Ecol Sin, 31(2): 529–537 (in Chinese)

    Google Scholar 

  • Park J Y, Park MJ, Joh H K, Shin H J, Kwon H J, Srinivasan R, Kim S J (2011). Assessment of MIROC3.2 hires climate and CLUE-S land use change impacts on watershed hydrology using SWAT. Trans ASABE, 54(5): 1713–1724

    Article  Google Scholar 

  • Pontius R G, Schneider L C (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ, 85(1–3): 239–248

    Article  Google Scholar 

  • Stevens D, Dragicevic S (2007). A GIS-based irregular cellular automata model of land-use change. Environ Plann B Plann Des, 34(4): 708–724

    Article  Google Scholar 

  • Syartinilia S T, Tsuyuki S (2008). GIS-based modeling of Javan Hawk- Eagle distribution using logistic and autologistic regression models. Biol Conserv, 141(3): 756–769

    Article  Google Scholar 

  • Veldkamp A, Fresco L O (1996). CLUE: a conceptual model to study the conversion of land use and its effects. Ecol Modell, 85(2–3): 253–270

    Article  Google Scholar 

  • Verburg P H (2006). Simulating feedbacks in land use and land cover change models. Landsc Ecol, 21(8): 1171–1183

    Article  Google Scholar 

  • Verburg P H, de Nijs T C M, Ritsema van Eck J, Visser H, de Jong K (2004a). A method to analyse neighbourhood characteristics of land use patterns. Comput Environ Urban Syst, 28(6): 667–690

    Article  Google Scholar 

  • Verburg P H, Schot P P, Dijst M J, Veldkamp A (2004b). Land use change modelling: current practice and research priorities. GeoJournal, 61(4): 309–324

    Article  Google Scholar 

  • Verburg P H, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura S S (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manage, 30(3): 391–405

    Article  Google Scholar 

  • Wang Q, Meng J J, Mao X Y (2014). Scenario simulation and landscape pattern assessment of land use change based on neighborhood analysis and auto-logistic model: a case study of Lijiang River Basin. Geogr Res, 33(6): 1073–1084 (in Chinese)

    Google Scholar 

  • Wear D N, Bolstad P (1998). Land-use changes in southern Appalachian landscapes: spatial analysis and forecast evaluation. Ecosystems (N Y), 1(6): 575–594

    Article  Google Scholar 

  • Wu F L (2002). Calibration of stochastic cellular automata: the application to rural-urban land conversions. Int J Geogr Inf Sci, 16(8): 795–818

    Article  Google Scholar 

  • Wu G P, Zeng Y N, Feng X Z, Xiao P F, Wang K (2010a). Dynamic simulation of land use change based on the improved CLUE-S model: a case study of Yongding county, Zhangjiajie. Geogr Res, 29 (3): 460–470 (in Chinese)

    Google Scholar 

  • Wu G P, Zeng Y N, Xiao P F, Feng X Z, Hu X T (2010b). Using autologistic spatial models to simulate the distribution of land-use patterns in Zhangjiajie, Hunan Province. J Geogr Sci, 20(2): 310–320

    Google Scholar 

  • Wu M, Ren X Y, Che Y, Yang K (2015). A coupled SD and CLUE-S model for exploring the impact of land use change on ecosystem service value: a case study in Baoshan district of Shanghai, China. Environ Manage, 56(2): 402–419

    Article  Google Scholar 

  • Yang J, Cai Y L, Yuan T (2014). Dynamic simulation on the spatiotemporal patterns of land use changes in Chongming County based on CLUE-S model. Chinese Agricultural Science Bulletin, 30(11): 258–264 (in Chinese)

    Google Scholar 

  • Zhang P, Liu Y H, Pan Y, Yu Z R (2013). Land use pattern optimization based on CLUE-S and SWAT models for agricultural non-point source pollution control. Math Comput Model, 58(3–4): 588–595

    Article  Google Scholar 

  • Zheng HW, Shen G Q P,Wang H, Hong J K (2015). Simulating land use change in urban renewal areas: a case study in Hong Kong. Habitat Int, 46: 23–34

    Article  Google Scholar 

  • Zheng X Q, Zhao L, Xiang W N, Li N, Lv L N, Yang X (2012). A coupled model for simulating spatio-temporal dynamics of land-use change: a case study in Changqing, Jinan, China. Landsc Urban Plan, 106(1): 51–61

    Article  Google Scholar 

Download references

Acknowledgements

This research was financially supported by the National Natural Science Foundation of China (Grant Nos. 41001078 and 41271060). The authors would like to thank the Editor and anonymous reviewers for their valuable comments on the manuscript.

Author information

Authors and Affiliations

  1. School of Geography, South China Normal University, Guangzhou, 510631, China

    Zhixiong Mei, Hao Wu & Shiyun Li

Authors
  1. Zhixiong Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shiyun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhixiong Mei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mei, Z., Wu, H. & Li, S. Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China. Front. Earth Sci. 12, 299–310 (2018). https://doi.org/10.1007/s11707-017-0639-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11707-017-0639-y

Keywords

Search

Navigation