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Simulating land use change by integrating landscape metrics into ANN-CA in a new way |
Xin YANG1( ), Yu ZHAO2, Rui CHEN2, Xinqi ZHENG3 |
1. School of Computer Engineering, Qingdao Technological University, Qingdao 266520 ,China 2. Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China 3. School of Information Engineering, China University of Geosciences, Beijing 100083, China |
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Abstract Landscape metrics are measurements of land-use patterns and land-use change, but even so, have rarely been integrated into land-use change simulation models. This paper proposes a new artificial neural network-cellular automaton by integrating landscape metrics into the model. In this model, each cell acquires unique landscape metric values. The landscape metric values of each cell are actually the landscape metric values of land use type in its neighborhood, which takes the cell as center. The calculation of landscape metrics ensures that those of each cell can represent cellular spatial environmental characteristics. The model is used to simulate land use change in the Changping district of Beijing, China. Comparisons of the simulated land use map with the actual map show that the proposed model is effective for land use change simulation. The validation is further carried out by comparing the simulated land use map with that simulated by an artificial neural network-cellular automaton model, which has not been integrated with landscape metrics. Results indicate that the proposed model is more appropriate for simulating both quantity and spatial distribution of land use change in the study area.
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| Keywords
land use change
landscape metrics
cellular automata
artificial neural network
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Corresponding Author(s):
Xin YANG
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Just Accepted Date: 17 June 2015
Online First Date: 30 July 2015
Issue Date: 05 April 2016
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| 1 |
S Arekhi, A Jafarzadeh (2014). Forecasting areas vulnerable to forest conversion using artificial neural network and GIS (case study: northern Ilam forests, Ilam province, Iran). Arab J of Geosci, 7(3): 1073–1085
https://doi.org/10.1007/s12517-012-0785-1
|
| 2 |
E Dai, S H Wu, W Z Shi, C K Cheung, A Shaker (2005). Modeling change-pattern-value dynamics on land use: an integrated GIS and artificial neural networks approach. Environ Manage, 36(4): 576–591
https://doi.org/10.1007/s00267-004-0165-z
|
| 3 |
Y X Feng, G P Luo, L Lu, D C Zhou, Q F Han, W Q Xu, C Y Yin, L Zhu, L Dai, Y Z Li, C F Li (2011). Effects of land use change on landscape pattern of the Manas River watershed in Xinjiang, China. Environ Earth Sci, 64(8): 2067–2077
https://doi.org/10.1007/s12665-011-1029-5
|
| 4 |
R S Hu, S C Dong (2013). Land use dynamics and landscape patterns in Shanghai, Jiangsu and Zhejiang. J Resour Ecol, 4(2): 141–148
https://doi.org/10.5814/j.issn.1674-764x.2013.02.006
|
| 5 |
S Isik, L Kalin, J E Schoonover, P Srivastava, B G Lockaby (2013). Modeling effects of changing land use/cover on daily streamflow: an artificial neural network and curve number based hybrid approach. Journal of Hydrology, 485(SI): 103–112
|
| 6 |
X Li, J Y Lin, Y M Chen, X P Liu, B Ai (2013). Calibrating cellular automata based on landscape metrics by using genetic algorithms. Int J Geogr Inf Sci, 27(3): 594–613
https://doi.org/10.1080/13658816.2012.698391
|
| 7 |
X Li, A G Yeh (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci, 16(4): 323–343
https://doi.org/10.1080/13658810210137004
|
| 8 |
Y P Lin, H J Chu, C F Wu, P H Verburg (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
https://doi.org/10.1080/13658811003752332
|
| 9 |
X P Liu, X Li, Y M Chen, Z Z Tan, S Y Li, B Ai (2010). A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecol, 25(5): 671–682
https://doi.org/10.1007/s10980-010-9454-5
|
| 10 |
X P Liu, L Ma, X Li, B Ai, S Y Li, Z J He (2014). Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. Int J Geogr Inf Sci, 28(1): 148–163
https://doi.org/10.1080/13658816.2013.831097
|
| 11 |
Y Mahajan, P Venkatachalam (2009). Neural network based cellular automata model for dynamic spatial modeling in GIS. Computational Science and its Applications-ICCSA 2009 PT 1, 5592: 341–352
|
| 12 |
K McGarigal, S A Cushman (2002). Comparative evaluation of experimental approaches to the study of habitat fragmentation effects. Ecol Appl, 12(2): 335–345
https://doi.org/10.1890/1051-0761(2002)012[0335:CEOEAT]2.0.CO;2
|
| 13 |
Y Mitsuda, S Ito (2011). A review of spatial-explicit factors determining spatial distribution of land use/land-use change. Landsc Ecol Eng, 7(1): 117–125
https://doi.org/10.1007/s11355-010-0113-4
|
| 14 |
Y Pan, A Roth, Z R Yu, R Doluschitz (2010). The impact of variation in scale on the behavior of a cellular automata used for land use change modeling. Comput Environ Urban Syst, 34(5): 400–408
https://doi.org/10.1016/j.compenvurbsys.2010.03.003
|
| 15 |
B C Pijanowski, A Tayyebi, J Doucette, B K Pekin, D Braun, J Plourde (2014). A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environ Model Softw, 51: 250–268
https://doi.org/10.1016/j.envsoft.2013.09.015
|
| 16 |
P Serra, X Pons, D Sauri (2008). Land-cover and land-use change in a Mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Appl Geogr, 28(3): 189–209
https://doi.org/10.1016/j.apgeog.2008.02.001
|
| 17 |
K C Seto, M Fragkias (2005). Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecol, 20(7): 871–888
https://doi.org/10.1007/s10980-005-5238-8
|
| 18 |
A Tayyebi, B C Pijanowski (2014). Modeling multiple land use changes using ANN, CART and MARS: comparing tradeoffs in goodness of fit and explanatory power of data mining tools. Int J Appl Earth Obs Geoinf, 28: 102–116
https://doi.org/10.1016/j.jag.2013.11.008
|
| 19 |
X Yang, X Zheng, R Chen (2014). A land use change model: integrating landscape pattern indexes and Markov-CA. Ecol Modell, 283: 1–7
https://doi.org/10.1016/j.ecolmodel.2014.03.011
|
| 20 |
H Zeng, F Jiang, S J Li (2004). Impacts of urban landscape structure on urban sprawl: a case researches in Nanchang. Acta Ecol Sin, 24(9): 1931–1937
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