Simulating Spatial-Temporal Changes of Land-Use Based on Ecological Redline Restrictions and Landscape Driving Factors: A Case Study in Beijing
<p>The geographical location of Beijing.</p> "> Figure 2
<p>Overview of the proposed model.</p> "> Figure 3
<p>Actual land-use maps in Beijing during 2010–2015 ((<b>a</b>) 2010; (<b>b</b>) 2015).</p> "> Figure 4
<p>Spatial restriction maps in Beijing ((<b>a</b>) Traditional restricted regions; (<b>b</b>) Ecological redline areas).</p> "> Figure 5
<p>Scenarios of land use simulation in 2015.</p> "> Figure 6
<p>Comparison of land-use maps in 2010 and 2020 ((<b>a</b>) 2010; (<b>b</b>) 2020).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. CLUE-S Model
2.3.1. The Prediction on Land-Use Demands
2.3.2. Driving Factors Analysis
2.3.3. Settings in the Spatial Restrictions
2.3.4. Conversion Rules
2.3.5. Land Spatial Allocation
2.3.6. Assessment on the Model Accuracy
3. Results
3.1. Spatial Changes of Land-Use Pattern
3.2. Ecological Redline Analysis
3.3. Accuracy Assessment of Land Use Simulation
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Funding
References
- Turner, B.L.; Lambin, E.F.; Reenberg, A. The emergence of land change science for global environmental change and sustainability. Proc. Natl. Acad. Sci. USA 2007, 104, 20666–20671. [Google Scholar] [CrossRef] [PubMed]
- Vitousek, P.M.; Mooney, H.A.; Lubchenco, J.; Melillo, J.M. Human domination of earth’s ecosystems. Science 1997, 277, 494–499. [Google Scholar] [CrossRef]
- Aquilué, N.; Cáceres, M.D.; Fortin, M.J.; Fall, A.; Brotons, L. A spatial allocation procedure to model land-use/land-cover changes: Accounting for occurrence and spread processes. Ecol. Model. 2017, 344, 73–86. [Google Scholar] [CrossRef]
- Wu, J.G.; Hobbs, R. Key issues and research priorities in landscape ecology: An idiosyncratic synthesis. Landsc. Ecol. 2002, 17, 355–365. [Google Scholar] [CrossRef]
- Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K. Global consequences of land use. Science 2005, 309, 570–574. [Google Scholar] [CrossRef] [PubMed]
- Bürgi, M.; Hersperger, A.M.; Schneeberger, N. Driving forces of landscape change-Current and new directions. Landsc. Ecol. 2004, 19, 857–868. [Google Scholar] [CrossRef]
- Luck, M.; Wu, J.G. A gradient analysis of urban landscape pattern: A case study from the Phoenix metropolitan region, Arizona, USA. Landsc. Ecol. 2002, 17, 327–339. [Google Scholar] [CrossRef]
- Pickett, S.T.A.; Cadenasso, M.L.; Grove, J.M.; Nilon, C.H.; Pouyat, R.V.; Zipperer, W.C.; Costanza, R. Urban ecological systems: Linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Ann. Rev. Ecol. Syst. 2001, 32, 127–157. [Google Scholar] [CrossRef]
- Reginster, I.; Rounsevell, M. Scenarios of future urban land use in Europe. Environ. Plan. B Plan. Des. 2006, 33, 619–636. [Google Scholar] [CrossRef]
- Lambin, E.F.; Geist, H.J.; Lepers, E. Dynamics of land-use and land-cover change in tropical regions. Ann. Rev. Environ. Resour. 2003, 28, 205–241. [Google Scholar] [CrossRef]
- Tian, G.J.; Ma, B.R.; Xu, X.L.; Liu, X.P.; Xu, L.Y.; Liu, X.J.; Xiao, L.; Kong, L.Q. Simulation of urban expansion and encroachment using cellular automata and multi-agent system model-A case study of Tianjin metropolitan region, China. Ecol. Indic. 2016, 70, 439–450. [Google Scholar] [CrossRef]
- Liu, Y.S.; Yang, Y.Y.; Li, Y.R.; Li, J.T. Conversion from rural settlements and arable land under rapid urbanization in Beijing during 1985–2010. J. Rural Stud. 2017, 51, 141–150. [Google Scholar] [CrossRef]
- Lü, Y.H.; Ma, Z.M.; Zhang, L.W.; Fu, B.J.; Gao, G.Y. Redlines for the greening of China. Environ. Sci. Policy 2013, 33, 346–353. [Google Scholar] [CrossRef]
- Xie, H.L.; He, Y.F.; Xie, X. Exploring the factors influencing ecological land change for China’s Beijing–Tianjin–Hebei region using big data. J. Clean. Prod. 2017, 142, 677–687. [Google Scholar] [CrossRef]
- Bai, Y.; Jiang, B.; Wang, M.; Li, H.; Alatalo, J.M.; Huang, S. New ecological redline policy (ERP) to secure ecosystem services in China. Land Use Policy 2016, 55, 348–351. [Google Scholar] [CrossRef]
- Stürck, J.; Schulp, C.J.E.; Verburg, P.H. Spatio-temporal dynamics of regulating ecosystem services in Europe-The role of past and future land use change. Appl. Geogr. 2015, 63, 121–135. [Google Scholar] [CrossRef]
- You, W.B.; Ji, Z.R.; Wu, L.Y.; Deng, X.P.; Huang, D.H.; Chen, B.R.; Yu, J.A.; He, D.J. Modeling changes in land use patterns and ecosystem services to explore a potential solution for meeting the management needs of a heritage site at the landscape level. Ecol. Indic. 2017, 73, 68–78. [Google Scholar] [CrossRef]
- Kleemann, J.; Baysal, G.; Bulley, H.N.N.; Fürst, C. Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa. J. Environ. Manag. 2017, 196, 411–442. [Google Scholar] [CrossRef] [PubMed]
- Luo, G.P.; Yin, C.Y.; Chen, X.; Xu, W.Q.; Lu, L. 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. 2010, 7, 198–207. [Google Scholar] [CrossRef]
- Manuschevich, D.; Beier, C.M. Simulating land use changes under alternative policy scenarios for conservation of native forests in south-central Chile. Land Use Policy 2016, 51, 350–362. [Google Scholar] [CrossRef]
- Peng, J.; Zhao, M.Y.; Guo, X.N.; Pan, Y.J.; Liu, Y.X. Spatial-temporal dynamics and associated driving forces of urban ecological land: A case study in Shenzhen City, China. Habitat Int. 2017, 60, 81–90. [Google Scholar] [CrossRef]
- Wassenaar, T.; Gerber, P.; Verburg, P.H.; Rosales, M.; Ibrahim, M.; Steinfeld, H. Projecting land use changes in the Neotropics: The geography of pasture expansion into forest. Glob. Environ. Chang. 2007, 17, 86–104. [Google Scholar] [CrossRef]
- Zhang, P.; Liu, Y.H.; Pan, Y.; Yu, Z.R. Land use pattern optimization based on CLUE-S and SWAT models for agricultural non-point source pollution control. Math. Comput. Model. 2013, 58, 588–595. [Google Scholar] [CrossRef]
- Leitao, A.B.; Ahern, J. Applying landscape ecological concepts and metrics in sustainable landscape planning. Landsc. Urban Plan. 2002, 59, 65–93. [Google Scholar] [CrossRef]
- Cabral, A.I.R.; Costa, F.L. Land cover changes and landscape pattern dynamics in Senegal and Guinea Bissau borderland. Appl. Geogr. 2017, 82, 115–128. [Google Scholar] [CrossRef]
- Cushman, S.A.; McGarigal, K.; Neel, M.C. Parsimony in landscape metrics: Strength, universality, and consistency. Ecol. Indic. 2008, 8, 691–703. [Google Scholar] [CrossRef]
- Yang, X.; Zheng, X.Q.; Lv, L.N. A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol. Model. 2012, 233, 11–19. [Google Scholar] [CrossRef]
- Basse, R.M.; Omrani, H.; Charif, O.; Gerber, P.; Bódis, K. Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl. Geogr. 2014, 53, 160–171. [Google Scholar] [CrossRef]
- Verburg, P.H.; Soepboer, W.; Limpiada, R.; Espaldon, M.V.O.; Sharifa, M.; Veldkamp, A. Land use change modelling at the regional scale: The CLUE-S model. Environ. Manag. 2002, 30, 391–405. [Google Scholar] [CrossRef] [PubMed]
- Batty, M. Urban evolution on the desktop: Simulation with the use of extended cellular automata. Environ. Plan. A 1998, 30, 1943–1967. [Google Scholar] [CrossRef]
- Martellozzo, F.; Amato, F.; Murgante, B.; Clarke, K.C. Modelling the impact of urban growth on agriculture and natural land in Italy to 2030. Appl. Geogr. 2018, 91, 156–167. [Google Scholar] [CrossRef]
- Amato, F.; Maimone, B.; Martellozzo, F.; Nolè, G.; Murgante, B. The effects of urban policies on the development of urban areas. Sustainability 2016, 8, 297. [Google Scholar] [CrossRef] [Green Version]
- Li, X.; Yeh, G.O.A. Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int. J. Geogr. Inf. Syst. 2002, 16, 323–343. [Google Scholar] [CrossRef]
- Le, Q.B.; Park, S.J.; Vlek, P.L.G.; Cremers, A.B. Land-use dynamic simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system. I. Structure and theoretical specification. Ecol. Inform. 2008, 3, 135–153. [Google Scholar] [CrossRef]
- Liu, G.; Jin, Q.W.; Li, J.Y.; Li, L.; He, C.X.; Huang, Y.Q.; Yao, Y.F. Policy factors impact analysis based on remote sensing data and the CLUE-S model in the Lijiang River Basin, China. CATENA 2017, 158, 286–297. [Google Scholar] [CrossRef]
- Verburg, P.H.; Overmars, K.P. Dynamic simulation of land-use change trajectories with the CLUE-S model. Model. Land-Use Chang. 2007, 321–337. [Google Scholar]
- Verburg, P.H.; Overmars, K.P. Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landsc. Ecol. 2009, 24, 1167–1181. [Google Scholar] [CrossRef]
- Verburg, P.H.; Veldkamp, A. Projecting land use transitions at forest fringes in the Philippines at two spatial scales. Landsc. Ecol. 2004, 19, 77–98. [Google Scholar] [CrossRef]
- Liu, R.Z.; Zhang, K.; Zhang, Z.J.; Borthwick, A.G.L. Land-use suitability analysis for urban development in Beijing. J. Environ. Manag. 2014, 145, 170–179. [Google Scholar] [CrossRef] [PubMed]
- Liu, J.Y.; Liu, M.L.; Tian, H.Q.; Zhuang, D.F.; Zhang, Z.X.; Zhang, W.; Tang, X.M.; Deng, X.Z. Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data. Remote Sens. Environ. 2005, 98, 442–456. [Google Scholar] [CrossRef]
- Guan, D.J.; Li, H.F.; Inohae, T.; Su, W.; Nagaie, T.; Hokao, K. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol. Model. 2011, 222, 3761–3772. [Google Scholar] [CrossRef]
- Reveshty, M.A. The assessment and predicting of land use changes to urban area using multi-temporal satellite imagery and GIS: A case study on Zanjan, IRAN (1984–2011). J. Geogr. Inf. Syst. 2011, 3, 298–305. [Google Scholar] [CrossRef]
- Xia, H.Q.; Liu, H.; Zheng, C.Y. A Markov-Kalman model of land-use change prediction in Xiuhe Basin, China. Commun. Comput. Inf. Sci. 2013, 399, 75–85. [Google Scholar]
- Zheng, H.W.; Shen, G.Q.; Wang, H.; Hong, J.K. Simulating land use change in urban renewal areas: A case study in Hong Kong. Habitat Int. 2015, 46, 23–34. [Google Scholar] [CrossRef]
- Lin, Y.P.; Chu, H.J.; Wu, C.F.; Verburg, P.H. 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. 2011, 25, 65–87. [Google Scholar] [CrossRef] [Green Version]
- Wu, J.Y.; Xue, D.Y.; Zhao, F.W.; Wang, Y.J. Progress of the study on investigation and conservation of biodiversity in China. J. Ecol. Rural Environ. 2013, 29, 146–151. [Google Scholar]
- Verburg, P.H.; Schot, P.P.; Dijst, M.J.; Veldkamp, A. Land use change modelling: Current practice and research priorities. Geojournal 2004, 61, 309–324. [Google Scholar] [CrossRef]
- Pontius, R.G. Quantification error versus location error in comparison of categorical maps. Photogramm. Eng. Remote Sens. 2000, 66, 1011–1016. [Google Scholar]
- Zheng, B.; Liu, G.; Wang, H.; Cheng, Y.; Lu, Z.; Liu, H.; Zhu, X.; Wang, M.; Yi, L. Study on the delimitation of the urban development boundary in a special economic zone: A case study of the central urban area of Doumen in Zhuhai, China. Sustainability 2018, 10, 756. [Google Scholar] [CrossRef]
Year | Demanded Areas (Hectares) | ||||
---|---|---|---|---|---|
Cropland | Forestland | Grassland | Water Body | Construction Land | |
2010 | 423,511.25 | 688,466.75 | 117,059.50 | 44,881.00 | 275,457.00 |
2011 | 416,524.11 | 686,750.60 | 114,402.31 | 42,668.97 | 289,029.52 |
2012 | 410,040.96 | 684,895.10 | 111,911.33 | 40,670.64 | 301,857.47 |
2013 | 404,030.93 | 682,915.91 | 109,576.69 | 38,865.29 | 313,986.69 |
2014 | 398,465.11 | 680,826.68 | 107,388.08 | 37,234.26 | 325,461.37 |
2015 | 393,316.06 | 678,640.01 | 105,335.93 | 35,760.68 | 336,322.81 |
2016 | 388,558.98 | 676,365.21 | 103,408.46 | 34,429.40 | 346,613.45 |
2017 | 384,168.14 | 674,014.91 | 101,599.91 | 33,226.64 | 356,365.89 |
2018 | 380,120.21 | 671,599.07 | 99,902.59 | 32,140.02 | 365,613.61 |
2019 | 376,393.11 | 669,126.83 | 98,309.31 | 31,158.35 | 374,387.89 |
2020 | 372,965.99 | 666,606.63 | 96,813.38 | 30,271.54 | 382,717.96 |
Land-Use Type | Driving Factor Type | Driving Factor | β Value |
---|---|---|---|
cropland | natural factors | DEM | −0.001395 |
slope | −0.192737 | ||
aspect | −0.000882 | ||
vegetation index | −1.166396 | ||
distance to administrative centers | 0.000028 | ||
distance to green lands | −0.000023 | ||
socio-economic factors | population density | −0.000097 | |
landscape fragmentation factors | PD of cropland | −2.637404 | |
PD of construction land | 3.010286 | ||
constant | 0.598003 | ||
forestland | natural factors | DEM | 0.002750 |
slope | 0.105144 | ||
aspect | 0.002238 | ||
vegetation index | −2.098534 | ||
distance to administrative centers | −0.000020 | ||
distance to main roads | −0.000028 | ||
distance to rivers | 0.000067 | ||
socio-economic factors | population density | −0.000936 | |
permanent migrant population | 0.059828 | ||
landscape fragmentation factors | PD of cropland | −28.046097 | |
PD of grassland | 16.887445 | ||
PD of water body | 10.555316 | ||
PD of construction land | −12.742629 | ||
constant | 2.028251 | ||
grassland | natural factors | DEM | −0.002323 |
slope | 0.019970 | ||
aspect | −0.001509 | ||
vegetation index | −0.654531 | ||
distance to metro lines | 0.000045 | ||
distance to green lands | 0.000032 | ||
socio-economic factors | population density | −0.000285 | |
landscape fragmentation factors | PD of cropland | −18.853907 | |
PD of water body | −7.701693 | ||
constant | −0.951428 | ||
water body | natural factors | DEM | −0.002463 |
slope | −0.069213 | ||
aspect | −0.003705 | ||
vegetation index | 0.841059 | ||
distance to metro lines | 0.000036 | ||
distance to rivers | −0.001671 | ||
socio-economic factors | population density | −0.000199 | |
landscape fragmentation factors | PD of grassland | 4.144435 | |
constant | −2.275303 | ||
construction land | natural factors | slope | −0.144154 |
aspect | 0.001116 | ||
vegetation index | 2.690150 | ||
distance to administrative centers | −0.000061 | ||
distance to main roads | 0.000056 | ||
distance to metro lines | −0.000068 | ||
socio-economic factors | population density | 0.000178 | |
constant | −2.033424 |
Land-Use Type | Conversion Elasticity |
---|---|
cropland | 0.5 |
forestland | 0.8 |
grassland | 0.6 |
water body | 0.8 |
construction land | 0.8 |
Land Use Type | Areas (Hectares) | |
---|---|---|
2010 | 2015 | |
cropland | 423,511.25 | 386,248.75 |
forestland | 688,466.75 | 681,141.00 |
grassland | 117,059.50 | 101,728.75 |
water body | 44,881.00 | 33,284.50 |
construction land | 275,457.00 | 346,971.75 |
Ecological Redline Type | Areas (Hectares) |
---|---|
headwater conservation | 212,625.86 |
biodiversity maintaining | 106,036.30 |
vital ecological function | 110,328.32 |
soil and water conservation | 167,632.75 |
drinking water source protection | 73,876.43 |
wind prevention and sand fixation | 11,323.74 |
important natural and artificial landscapes | 178,075.14 |
Scenario | Kappa Coefficient | |
---|---|---|
excluding landscape pattern indicators | no spatial restrictions | 0.765695 |
traditional spatial restrictions | 0.768237 | |
ecological redline restrictions | 0.770568 | |
including landscape pattern indicators | no spatial restrictions | 0.766417 |
traditional spatial restrictions | 0.768458 | |
ecological redline restrictions | 0.771676 |
Scenario | Kappa Coefficient | ||||
---|---|---|---|---|---|
Cropland | Forestland | Grassland | Water Body | Construction Land | |
no spatial restrictions | 0.681815 | 0.876915 | 0.765927 | 0.590958 | 0.695101 |
traditional spatial restrictions | 0.683145 | 0.879591 | 0.770764 | 0.594813 | 0.696435 |
ecological redline | 0.690017 | 0.882000 | 0.774720 | 0.603926 | 0.696602 |
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Jia, Z.; Ma, B.; Zhang, J.; Zeng, W. Simulating Spatial-Temporal Changes of Land-Use Based on Ecological Redline Restrictions and Landscape Driving Factors: A Case Study in Beijing. Sustainability 2018, 10, 1299. https://doi.org/10.3390/su10041299
Jia Z, Ma B, Zhang J, Zeng W. Simulating Spatial-Temporal Changes of Land-Use Based on Ecological Redline Restrictions and Landscape Driving Factors: A Case Study in Beijing. Sustainability. 2018; 10(4):1299. https://doi.org/10.3390/su10041299
Chicago/Turabian StyleJia, Zimu, Bingran Ma, Jing Zhang, and Weihua Zeng. 2018. "Simulating Spatial-Temporal Changes of Land-Use Based on Ecological Redline Restrictions and Landscape Driving Factors: A Case Study in Beijing" Sustainability 10, no. 4: 1299. https://doi.org/10.3390/su10041299