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Frontiers of Earth Science

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Front. Earth Sci.    2018, Vol. 12 Issue (2) : 299-310    https://doi.org/10.1007/s11707-017-0639-y
RESEARCH ARTICLE
Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China
Zhixiong MEI(), Hao WU, Shiyun LI
School of Geography, South China Normal University, Guangzhou 510631, China
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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     
Corresponding Author(s): Zhixiong MEI   
Just Accepted Date: 20 March 2017   Online First Date: 13 April 2017    Issue Date: 09 May 2018
 Cite this article:   
Zhixiong MEI,Hao WU,Shiyun LI. Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China[J]. Front. Earth Sci., 2018, 12(2): 299-310.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-017-0639-y
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I2/299
Fig.1  Location of the study area.
VariableArable landWoodlandTraffic landResidential/industrial landWater areaUnused land
DRoad/km0.0010.002–0.013–0.0060.0020.001
DWater/km0.0010.002–––0.001–
DRSettlement/km–0.0040.0010.002–0.0060.0060.003
DTown/km0.001–––0.0030.001–
PDens/( people·km?2)–0.0200.0070.0080.0200.015–0.007
RSDens/%––7.419––4.318–12.377
Elevation/m–0.1770.068––0.064–0.050–0.064
Slope/(°)–0.2410.175–0.072–0.064–0.232–0.135
Aspect/(°)–0.0240.037–0.005–0.021–0.008
Curvature–3.7410.460–0.911––2.823–2.020
Constant0.815–1.106–3.031–1.165–2.676–2.548
ROC0.8180.8480.7570.8340.8240.782
Tab.1  Logistic regression results for different land-use types
VariableArable landWoodlandTraffic landResidential/industrial landWater areaUnused land
DRoad/km0.0010.002–0.009–0.0010.001
DWater/km–0.001––0.001–0.003–
DRSettlement/km0.0030.0040.0030.0030.0040.004
DTown/km––––0.001––
PDens/( people·km?2)–0.032–0.0100.0150.004–
RSDens/%–– 3.608––3.302–4.419
Elevation/m–0.0860.047––0.026–0.023–0.037
Slope/(°)–0.1210.134–0.030––0.055–0.048
Aspect/(°)–0.0170.039–0.006–0.013–0.007
Curvature–4.4830.482–0.911––2.553–2.197
Autocov5.8224.7323.6086.6576.0504.826
Constant–5.221–5.719–5.601–7.030–6.805–5.800
ROC0.9140.8830.8490.9440.9330.899
Tab.2  Autologistic regression results for different land-use types
VariableArable landWoodlandTraffic landResidential/industrial landWater areaUnused land
DRoad/km–0.001–0.0100.001–0.001
DWater/km0.001––––0.001–
DRSettlement/km––0.0020.002–0.005––
DTown/km––––––
PDens/( people·km?2)–0.003–0.006––0.011–
RSDens/%–3.081–––2.933–1.779
Elevation/m–0.0590.023––0.047–0.014–0.026
Slope/(°)–0.1140.055–0.054––0.115–0.069
Aspect/(°)–0.0190.037–––0.016–0.010
Curvature–5.3100.324–0.784––3.205–2.723
NE12.621–0.630–0.137–0.337–0.842–0.292
NE2–1.228–1.669–0.311–1.602–0.653–0.603
NE3––0.056–1.197–0.181–0.112–
NE4–0.178–0.430–1.921–0.094–0.193
NE5––0.343–0.084–0.161––
NE6–0.142–0.188–0.137–0.202–0.184–
Constant–3.065–3.392–4.250–2.686–3.056–4.018
ROC0.8740.8570.8200.9410.9050.786
Tab.3  NE-logistic regression results for different land-use types
VariableArable landWoodlandTraffic landResidential/industrial landWater areaUnused land
DRoad/km–0.001–0.009–––
DWater/km0.001––––0.002–
DRSettlement/km––0.0020.002–0.005––
DTown/km––––––
PDens/(people·km?2)–0.004–0.007––0.009–
RSDens/%–2.767–––2.530–3.421
Elevation/m–0.0620.024––0.039–0.017–0.022
Slope/(°)–0.1050.055–0.053––0.175–0.066
Aspect/(°)–0.0170.037–––0.014–0.008
Curvature–4.7040.283–––2.412–2.299
Autocov4.9868.6174.9795.5635.9254.873
NE10.493–0.631–0.128–0.372–0.866–0.302
NE2–1.334–2.961–0.335–1.784–0.743–0.727
NE3––0.053–0.633–0.196–0.109–
NE4–0.176–0.435–0.173–0.114–0.231
NE50.042–0.344–0.080–0.173––
NE6–0.134–0.191–0.136–0.223–0.173–
Constant–3.427–3.404–4.439–2.802–3.289–4.088
ROC0.9190.8980.8540.9510.9390.899
Tab.4  NE-autologistic regression results for different land-use types
Fig.2  The actual 2009 land-use map (a), and simulated 2009 maps by NE-autologistic-CLUE-S based on the land-use data of (b) 2001 and (c) 2005.
ScenarioArable landWoodlandTraffic landResidential/industrial landWater areaUnused land
NG196.84997.2275.29192.0766.8886.53
EP221.781,013.9069.56179.2871.2159.10
ED181.48968.9080.74242.2270.6770.82
Tab.5  Land-use area (km2) demands under three scenarios in 2020
Fig.3  Simulated land-use maps in Zengcheng in 2020 under three scenarios: (a) natural growth scenario; (b) ecological protection scenario; (c ) economic development scenario.
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