Integration of Neural Network, Markov Chain and CA Markov Models to Simulate Land Use Change Region of Behbahan

Document Type : Original Article

Authors

1 Malayer University

2 Behbahan Khatam Alanbia University of Technology

3 Tarbiat Modares University

4 Shahid Chamran University of Ahvaz

Abstract

Purpose- Land is the place of earthly natural ecosystem functionality that has been used by humans in multiple methods. Land-use change (LUC) simulation is the most important method for researching LUC, which leads to some environmental issues such as the decreasing supply of forestry products and increasing levels of greenhouse gas emissions. Therefore, the present study aims at (i) using the Landsat imagery to prepare land use-cover (LULC) maps for 2000 and 2014; (ii) assessing Land use changes based on land change modeler (LCM) for the period from 2000 to 2014, and (iii) predicting the plausible land cover pattern in the region of Behbahan, using an algorithm based on ANN for 2028.
Design/methodology/approach- A hybrid model consisting of a neural network model, Markov chain (MC), and cellular automata (CA Markov) was designed to improve the performance of the standard network model. The modeling of transfer power is done by multilayer Perceptron of an artificial neural network and six variables. The change allocated to each use and the forecasting is computed by Markov chain and CA Markov. Operation model calibration and verification of land use data at two points were conducted in 2000 and 2014.
Findings- Modeling results indicate that the model validation phase has a good ability to predict land-use change on the horizon is 14 years old (2028). The comparison between modeling map and map related to 2013 shows that residential area and agricultural land continue to their growth trend so that residential area will be increased from 3157 hectares in 2014 to 4180 hectares in 2028 and it has 2% growth that has been 2% from 2000 to 2014. The results of this study can provide a suitable perspective for planners to manage land use regarding land-use changes in the past, present, and future. They are also can be used for development assessment projects, the cumulative effects assessment, and the vulnerable and sensitive zone recognition.

Keywords

References

  1. Abd El-Kawy, O. R. (2011). Land use and land cover change detection in the western Nile delta of Egypt using sensing data. Applied Geography, 31, 2 483-494. https://doi.org/10.1016/j.apgeog.2010.10.012
  2. Arsanjani, J. J., Helbich, M., Kainz, W., & Darvishi Boloorani, A. (2013). Integration of logistic regression, Markov chain, and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, 265-275. https:// doi.org/ 10.1016/ j.jag.2011.12.014
  3. Bakr, N. (2010). Monitoring land cover changes in a newly reclaimed area of Egypt using multitemporal Landsat data. Applied Geography, 30, 4 592-605. https://doi.org/10.1016/j.apgeog.2009.10.008
  4. Bonilla-Bedoya, S., Mora, A., Vaca, A., Estrella, A., & Ángel Herrera, M. (2020). Modeling the relationship between urban expansion processes and urban forest characteristics: An application to the Metropolitan District of Quito. Computers, Environment and Urban Systems, 79, 16 101-120. https://doi.org/10.1016/j.compenvurbsys.2019.101420
  5. Cabral, P., & Zamyatin, A. (2009). Markov processes in modeling land use and land cover changes in Sintra-Cascais, Portugal. Dyna, 76(158), 191-198. http://www.scielo.org.co/scielo.php?pid=S0012-73532009000200018&script=sci_abstract&tlng=pt
  6. Caldas, M., Simmons, C., Walker, R., Perz, S., Aldrich, S., Pereira, R., Leite, F. & Arima, E. (2010). Settlement Formation and Land Cover and Land Use Change: A Case Study in the Brazilian Amazon. Journal of American Latin Geography, 9, 1 125-144. https://doi.org/1353/lag.0.0066
  7. Chuvieco, E. (2002). Teledetección ambiental: La observación de la Tierra desde eespacio. Editorial Ariel. Barcelona, España. ISBN: 84-344-8047.
  8. Clancy, D., Tanner, J. E., McWilliam, S., & Spencer, M. (2010). Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo. Ecological Modelling, 221(10), 1337-1347. https://doi.org/10.1016/j.ecolmodel.2010.02.001
  9. Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and planning B: Planning and design, 24(2), 247-261. https://doi.org/10.1068%2Fb240247
  10. Coppedge, B. , Engle, D. M., & Fuhlendorf, S. D. (2007). Markov models of land cover dynamics in a southern Great Plains grassland region. Landscape Ecology 22, 9 1383-1393. https://doi.org/ 10.1007/s10980-007-9116-4
  11. Dadashpoor, H., & Salarian, F. (2019). Urban sprawl on natural lands: Analyzing and predicting the trend of land-use changes and sprawl in Mazandaran city region, Iran. Environment, De­velopment, and Sustainability, 22, 2 593–614 (2020). https://doi.org/10.1007/s10668-018-0211-2
  12. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29,3 390–401. https://doi.org/10.1016/j.apgeog.2008.12.005.
  13. Dietzel, C., & Clarke, K. (2006). The effect of disaggregating land use categories in cellular automata during model calibration and forecasting. Computers, Environment and Urban Systems, 30(1), 78-101. https://doi.org/10.1016/j.compenvurbsys.2005.04.001
  14. Eastman, J. R. (2006). IDRISI Andes. Guide to GIS and Image Processing. Worcester: Clark Labs, Clark University.
  15. Fan, F., Wang, Y., & Wang, Z. (2008). Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ Environmental Monitoring Assessment, 137, 3, 127-147. https://doi.org/10.1007/s10661-007-9734-y
  16. Gholamalifard, M., Joorabian Shooshtari, Sh., Hosseini Kahnuj, S.M., & Mirzaei, M. (2013). Modeling land-use changes using LCM coast of the province in GIS environment. Journal of ecological, 38, 4 109-124. (In Persian). Doi: 22059/jes.2013.29867
  17. Haibo, Y., Longjiang, D., Hengliang, G., & Jie, Z. (2011). Tai'an land uses Analysis and Prediction Based on RS and Markov Model. Procedia Environmental Sciences, 10, 2625–2630. https://doi.org/10.1016/ j.proenv.2011.09.408
  18. Huilei, L., Jian, P., Yanxu, L., & Yina, H. (2017). Urbanization impact on landscape patterns in Beijing City, China: A spatial heterogeneity perspective. Ecological Indicators 82, 2 50–60. https://doi.org/ 10.1016/ j.ecolind.2017.06.032
  19. Irwin, E. G., & Geoghegan, J. (2001). Theory, data, and methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85(1-3), 7-24. https://doi.org/10.1016/S0167-8809(01)00200-6.
  20. Jensen, J.R. (2007). An Earth Resource Perspective. Pearson Prentice Hall, Remote Sensing of The Environment, 17, 350-355.
  21. Joorabian Shooshtari, Sh. (2012). Monitoring land cover change, degradation, and restoration of the Hyrcanian Forests in Northern Iran (1977–2010). International Journal of Environmental Sciences, 3, 3 1038-1056. [In Persian]
  22. Koomen, E., Stillwell, J., Bakema, A. & Scholten, H.J. (2007). Modeling Land-use Change, Progress, and Applications. Netherlands, Springer, 6, 225-228.
  23. Lambin, E. F., Geist, H., & Rindfuss, R. R. (2006). Introduction: local processes with global impacts. In Land-use and land-cover change(pp. 1-8). Springer, Berlin, Heidelberg.
  24. Lillesand, T., & Kiefer, R.W. (2000). Remote sensing and image interpretation. New York: John Wiley and Sons.
  25. Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277-293. https://doi.org/10.1016/j.isprsjprs.2017.06.001
  26. Macedo, C. F., Pani, P., Cardoso, V., & Crispino, L. C. (2013). Into the lair: gravitational-wave signatures of dark matter. The Astrophysical Journal, 774(1), 48. https://doi.org/10.1088/0004-637X/774/1/48
  27. Mendoza, M. E. (2011). Analyzing land cover and land-use change processes at watershed level: A multitemporal study in the Lake Cuitzeo Watershed, Mexico (1975-2003). Applied Geography, 31, 1 237-250. https://doi.org/10.1016/j.apgeog.2010.05.010
  28. Mohammad, A., Worku, H., (2020). Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Climate, (31), 100545. https://doi.org/10.1016/j.uclim.2019.100545
  29. Mohammadyari, F., Mirsanjari, M. M., Suziedelyte Visockiene, J., & Zarandian, A. (2020). Evaluation of change in land usage and land cover in Karaj, Iran. Environmental engineering. Paper presented at 11th International Conference. Vilnius Gediminas Technical University. Lithuania. DOI: 3846/enviro.2020.649
  30. Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 3 247–259. https:// doi.org/ 10.1016/ j.isprsjprs. 2010.11.001
  31. Mozumder, Ch., & Tripathi, N.K. (2014). Geospatial scenario-based modeling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network. International Journal of Applied Earth Observation and Geoinformation, 32, 92-104. https://doi.org/10.1016/j.jag.2014.03.002
  32. Nahuelhual, L., Carmona, A., Lara, A., Echeverría, C., & González, M.E. (2012). Land-cover change to forest plantations: Proximate Causes and implications for the landscape in south-central Chile. Landscape and Urban Planning, 107, 1 12-20. https://doi.org/10.1016/j.landurbplan.2012.04.006
  33. Oñate-Valdivieso, F., & Sendra, J. B. (2010). Application of GIS and Remote Sensing Techniques in Generation of Land Use Scenarios for Hydrological Modeling. Journal of Hydrology 395, (3-4), 256-263. https://doi.org/10.1016/j.jhydrol.2010.10.033
  34. Oommen, T. (2008). An objective analysis of Support Vector Machine-based classification for remote sensing. Mathematical Geosciences, 40, 3 409–424. https://doi.org/10.1007/s11004-008-9156-6
  35. Perez-Vega, A., Mas, J., & Ligmann-Zielinska, A. (2012). Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modelling & Software, 29, 11-23. https://doi.org/10.1016/j.envsoft.2011.09.011
  36. Pijanowski, B. C., Brown, D. G., Shellito, B. A., & Manik, G. A. (2002). Using Neural Networks and GIS to Forecast Land Use Changes: a Land Transformation Model. Journal Computers Environment Urban Systems, 26, 6 553-575. https://doi.org/10.1016/S0198-9715(01)00015-1
  37. 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. Environmental Modelling & Software, 51, 250-268. https://doi.org/10.1016/j.envsoft.2013.09.015
  38. Pontius Jr, R. G., & Spencer, J. (2005). Uncertainty in extrapolations of predictive land-change models. Environment and Planning B: Planning and design, 32(2), 211-230. https://doi.org/10.1068%2Fb31152
  39. Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3-4), 938-943. https://doi.org/10.1016/j.mcm.2010.11.019
  40. Schulz, J. J., Cayuela, L., Echeverria, C., Salas, J., & Rey, Benayas, J.M. (2010). Monitoring Land Cover Change of the Dryland Forest Landscape of Central Chile (1975 - 2008), Applied Geography, 30, 436-447. https://doi.org/10.1016/j.apgeog.2009.12.003
  41. Shahidul Islam, M., & Ahmed, R. (2011). Land-use change prediction in Dhaka city using GIS-aided Markov Chain modeling. Life Earth Science, 6, 81-89. https://doi.org/ 3329/jles.v6i0.9726
  42. Silva, L. P., Xavier, A., Silva, R. M., Santos, G. (2020). Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil. Global Ecology and Conservation, 21, 2 1-13. https://doi.org/10.1016/j.gecco.2019-00811.
  43. Sun, X., Crittenden, J. C., Li, F., Lu, Z., & Dou, X. (2018). Urban expansion simulation and the Spatio-temporal changes of ecosystem services, a case study in Atlanta Metropolitan area, USA. Science of the Total Environment, 622–623, 974–987. https://doi.org/10.1016/j.scitotenv.2017.12.062
  44. Veldkamp, A., & Lambin, E. F. (2001). Predicting land-use change.
    Agriculture, Ecosystems & Environment    , 85(1–3), 1-6. https://doi.org/10.1016/S0167-8809(01)00199-2.
  45. Wu, Q., Li, H. Q., Wang, J., Paulussen, Y., He, M., Wang, B., Wang, H., & Wang, Z. (2006). Monitoring and predicting land-use change in Beijing. Landscape and Urban Plan. 78, 4 322-333. https:// doi.org/ 10.1016/ j.landurbplan.2005.10.002
  46. Yang, X., & Lo, C. P. (2002). Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23(9), 1775-1798. https://doi.org/10.1080/01431160110075802
  47. Yang, X., Zheng, X. Q. & Chen, R. (2014). A land-use change model: Integrating landscape pattern indexes and Markov-CA. Ecological Modelling, 283, 10 1-7. https://doi.org/10.1016/ j.ecolmodel.2014.03.011
  48. Zhang, X., & Shu, C. W. (2010). On positivity-preserving high order discontinuous Galerkin schemes for compressible Euler equations on rectangular meshes. Journal of Computational Physics, 229(23), 8918-8934. https://doi.org/10.1016/j.jcp.2010.08.016
Send comment about this article
CAPTCHA Image

Files

History

  • Receive Date: 09 February 2021
  • Revise Date: 03 June 2021
  • Accept Date: 23 June 2021

Share

How to cite

Statistics