Simulating land use change by integrating landscape metrics into ANN-CA in a new way

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• 作者：Xin Yang ; Yu Zhao ; Rui Chen ; Xinqi Zheng
• 关键词：land use change ; landscape metrics ; cellular automata ; artificial neural network
• 刊名：Frontiers of Earth Science
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：10
• 期：2
• 页码：245-252
• 全文大小：1,016 KB
• 参考文献：Arekhi S, Jafarzadeh A (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): 1073–1085CrossRef
  Dai E, Wu S H, Shi W Z, Cheung C K, Shaker A (2005). Modeling change-pattern-value dynamics on land use: an integrated GIS and artificial neural networks approach. Environ Manage, 36(4): 576–591CrossRef
  Feng Y X, Luo G P, Lu L, Zhou D C, Han Q F, Xu WQ, Yin C Y, Zhu L, Dai L, Li Y Z, Li C F (2011). Effects of land use change on landscape pattern of the Manas River watershed in Xinjiang, China. Environ Earth Sci, 64(8): 2067–2077CrossRef
  Hu R S, Dong S C (2013). Land use dynamics and landscape patterns in Shanghai, Jiangsu and Zhejiang. J Resour Ecol, 4(2): 141–148CrossRef
  Isik S, Kalin L, Schoonover J E, Srivastava P, Lockaby B G (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): 103–112CrossRef
  Li X, Lin J Y, Chen Y M, Liu X P, Ai B (2013). Calibrating cellular automata based on landscape metrics by using genetic algorithms. Int J Geogr Inf Sci, 27(3): 594–613CrossRef
  Li X, Yeh A G (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci, 16(4): 323–343CrossRef
  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): 65–87CrossRef
  Liu X P, Li X, Chen Y M, Tan Z Z, Li S Y, Ai B (2010). A new landscape index for quantifying urban expansion using multitemporal remotely sensed data. Landscape Ecol, 25(5): 671–682CrossRef
  Liu X P, Ma L, Li X, Ai B, Li S Y, He Z J (2014). Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. Int J Geogr Inf Sci, 28(1): 148–163CrossRef
  Mahajan Y, Venkatachalam P (2009). Neural network based cellular automata model for dynamic spatial modeling in GIS. Computational Science and its Applications-ICCSA 2009 PT 1, 5592: 341–352CrossRef
  McGarigal K, Cushman S A (2002). Comparative evaluation of experimental approaches to the study of habitat fragmentation effects. Ecol Appl, 12(2): 335–345CrossRef
  Mitsuda Y, Ito S (2011). A review of spatial-explicit factors determining spatial distribution of land use/land-use change. Landsc Ecol Eng, 7(1): 117–125CrossRef
  Pan Y, Roth A, Yu Z R, Doluschitz R (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): 400–408CrossRef
  Pijanowski B C, Tayyebi A, Doucette J, Pekin B K, Braun D, Plourde J (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: 250–268CrossRef
  Serra P, Pons X, Sauri D (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): 189–209CrossRef
  Seto K C, Fragkias M (2005). Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecol, 20(7): 871–888CrossRef
  Tayyebi A, Pijanowski B C (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: 102–116CrossRef
  Yang X, Zheng X, Chen R (2014). A land use change model: integrating landscape pattern indexes and Markov-CA. Ecol Modell, 283: 1–7CrossRef
  Zeng H, Jiang F, Li S J (2004). Impacts of urban landscape structure on urban sprawl: a case researches in Nanchang. Acta Ecol Sin, 24(9): 1931–1937
  
• 作者单位：Xin Yang (1)
  Yu Zhao (2)
  Rui Chen (2)
  Xinqi Zheng (3)
  
  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
  
• 刊物主题：Earth Sciences, general;
• 出版者：Springer Berlin Heidelberg
• ISSN：2095-0209

文摘

Landscape metrics are measurements of landuse 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.