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

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 (NE-logistic 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.

Keywords

CLUE-S / land-use change simulation / spatial autocorrelation / self-organization

References

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

[2] 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

[3] 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

[4] 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)

[5] 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)

[6] 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

[7] Jiang W G, 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

[8] 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

[9] 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

[10] 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

[11] 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

[12] 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

[13] 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

[14] 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

[15] 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 non-point source pollution impact at a watershed scale in Liaoning Province, China. Chin Geogr Sci, 24(5): 540–550

[16] 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

[17] 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

[18] Liu X P, Li X, Shi X, Zhang X H, Chen Y M (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

[19] 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

[20] 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

[21] 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

[22] 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

[23] 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

[24] 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)

[25] Park J Y, Park M J, 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

[26] Pontius R G Jr, 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

[27] 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

[28] 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

[29] 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

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

[31] 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

[32] 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

[33] 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

[34] 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)

[35] 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

[36] 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

[37] 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)

[38] 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

[39] 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

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

[41] 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

[42] Zheng H W, 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

[43] 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

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.

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2017 Higher Education Press and Springer-Verlag Berlin Heidelberg