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Regional Environmental Change

, Volume 15, Issue 2, pp 277–289 | Cite as

Dynamics of landscape patterns in an inland river delta of Central Asia based on a cellular automata-Markov model

  • Geping Luo
  • Tureniguli Amuti
  • Lei Zhu
  • Bulkajyr T. Mambetov
  • Bagila Maisupova
  • Chi Zhang
Original Article

Abstract

The analysis of landscape pattern changes is of significant importance for understanding spatial ecological dynamics and maintaining sustainable development, especially in wetland ecosystems, which are experiencing indirect human disturbances in arid Central Asia. This study attempted to examine the temporal and spatial dynamics of landscape patterns and to simulate their trends in the Ili River delta of Kazakhstan through quantitative analysis and a cellular automata (CA)-Markov model. This study also sought to examine the effectiveness of using the CA-Markov model for investigating the dynamics of the wetland landscape pattern. The total wetland area, including the river, lake, marsh, and floodplain areas, and the area of sandy land have remained steady, while that of desert grassland has decreased slightly, and shrublands have increased slightly from approximately 1978 to 2007. However, the wetland and shrubland areas exhibited a trend of increasing by 18.6 and 10.3 %, respectively, from 1990 to 2007, while the desert grassland and sandy land areas presented the opposite trend, decreasing by 30.3 and 24.3 %, respectively. The landscape patterns predicted for the year 2020 using probabilistic transfer matrixes for 1990–2007 (Scenario A) and 1990–1998 (Scenario B), respectively, indicated that the predicted landscape for 2020 tends to improve based on Scenario A, but tends to degrade based on Scenario B. However, the overall Kappa coefficient of 0.754 for the 2020 predicted landscapes based on Scenarios A and B indicates that the differences in the predicted landscapes are not distinct. This research indicates that the applied CA-Markov model is effective for the simulation and prediction of spatial patterns in natural or less disturbed landscapes and is valuable for developing land management strategies and reasonably exploiting the wetland resources of the Ili River delta.

Keywords

Ili River delta Remote sensing Landscape pattern Cellular automata-Markov model Simulation 

Notes

Acknowledgments

This study was funded by the International Science and Technology Cooperation Program of China (Grant No. 2010DFA92720-9) and National Natural Science Foundation of China (Contract No. 41361140361). The authors wish to thank the anonymous reviewers for their constructive comments and suggestions for revising the manuscript.

Supplementary material

10113_2014_638_MOESM1_ESM.pdf (285 kb)
Supplementary material 1 (PDF 284 kb)

References

  1. Abdrasilov SA, Tulebaeva KA (1994) Dynamics of the Ili delta with consideration of fluctuations of the level Lake Balkhash. Hydrotech Constr 28:421–426CrossRefGoogle Scholar
  2. Abulaiti R (2012) Trends of precipitation over the Ili valley. Gansu Water Resour Hydropower Technol 48(7):1–4 (in Chinese)Google Scholar
  3. Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. US Geol Surv Prof Pap 964:1–36Google Scholar
  4. Baker C, Lawrence R, Montague C, Patten D (2006) Mapping wetlands and riparian areas using Landsat ETM + imagery and decision-tree-based models. Wetlands 26:465–474CrossRefGoogle Scholar
  5. Boerner REJ, DeMers MN, Simpson JW, Artigas FJ, Silva A, Berns LA (1996) Markov models of inertia and dynamic on two contiguous Ohio landscapes. Geogr Anal 28:56–66CrossRefGoogle Scholar
  6. Burgi M, Hersperger AM, Schneeberger N (2004) Driving forces of landscape change—current and new directions. Landsc Ecol 19:857–868CrossRefGoogle Scholar
  7. Caruso G, Rounsevell M, Cojocaru G (2005) Exploring a spatio-dynamic neighbourhood-based model of residential behaviour in the Brussels periurban area. Int J Geogr Inf Sci 19:103–123CrossRefGoogle Scholar
  8. Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM + , and EO-1 ALI sensors. Remote Sens Environ 113:893–903CrossRefGoogle Scholar
  9. Donker DK, Hasman A, Van Geijn HP (1993) Interpretation of low kappa values. Int J Biomed Comput 33:55–64CrossRefGoogle Scholar
  10. Eastman JR (1999) Idrisi 32: user’s guide. Clark University, WorcesterGoogle Scholar
  11. Georgescu M, Miguez-Macho G, Steyaert LT, Weaver CP (2009) Climatic effects of 30 years of landscape change over the Greater Phoenix, Arizona, region: 1. Surface energy budget changes. J Geophys Res Atmos 114:1–17Google Scholar
  12. Gong P, Niu Z, Cheng X, Zhao K, Zhou D, Guo J, Liang L, Wang X, Li D, Huang H, Wang Y, Wang K, Li W, Wang X, Ying Q, Yang Z, Ye Y, Li Z, Zhuang D, Chi Y, Zhou H, Yan J (2010) China’s wetland change (1990–2000) determined by remote sensing. Sci China Earth Sci 53:1036–1042CrossRefGoogle Scholar
  13. Guan D, Li H, Inohae T, Su W, Nagaie T, Hokao K (2011) Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol Model 222(20–22):3761–3772CrossRefGoogle Scholar
  14. Hepinstall JA, Alberti M, Marzluff JM (2008) Predicting land cover change and avian community responses in rapidly urbanizing environments. Lands Ecol 23:1257–1276CrossRefGoogle Scholar
  15. Houet T, Hubert-Moy L (2006) Modelling and projecting land-use and land-cover changes with a cellular automaton in considering landscape trajectories: an improvement for simulation of plausible future states. EARSeL eProc 5:63–76Google Scholar
  16. Hwang C, Kao Y-C, Tangdamrongsub N (2011) Preliminary analysis of lake level and water storage changes over lakes Baikal and Balkhash from satellite altimetry and gravimetry. Terr Atmos Ocean Sci 22:97–108CrossRefGoogle Scholar
  17. Iacono M, Levinson D, El-Geneidy A, Wasfi R (2012) A Markov chain model of land use change in the twin cities, 1958–2005. http://nexus.umn.edu/Papers/MarkovLU
  18. Kamusoko C, Aniya M, Adi B, Manjoro M (2009) Rural sustainability under threat in Zimbabwe-Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model[J]. Appl Geogr 29(3):435–447CrossRefGoogle Scholar
  19. Kezer K, Matsuyama H (2006) Decrease of river runoff in the Lake Balkhash basin in Central Asia. Hydrol Process 20:1407–1423CrossRefGoogle Scholar
  20. Kipshakbaev NK, Abdrasilov SA (1994) Effect of economic activity on the hydrologic regime and dynamics of the Ili delta. Hydrotech Constr 28:416–418CrossRefGoogle Scholar
  21. Lal R, Suleimenov M, Stewart BA, Hansen DO, Doraiswamy P (2007) Climate change and terrestrial carbon sequestration in Central Asia. Taylor & Francis, LondonCrossRefGoogle Scholar
  22. Loveland TR, Sohl TL, Stehman SV, Gallant AL, Sayler KL, Napton DE (2002) A strategy for estimating the rates of recent United States land-cover changes. Photogramm Eng Remote Sens 68:1091–1099Google Scholar
  23. Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28:823–870CrossRefGoogle Scholar
  24. Luo GP, Yin CY, Chen X, Xu WQ, Lu L (2010) 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 7:198–207CrossRefGoogle Scholar
  25. Mikhailov VN (2004) The impact of deltas on the mean long-term water river runoff. Water Resour 31:351–356CrossRefGoogle Scholar
  26. Mondal P, Southworth J (2010) Evaluation of conservation interventions using a cellular automata-Markov model. For Ecol Manag 260(10):1716–1725CrossRefGoogle Scholar
  27. Myint SW, Wang L (2006) Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Can J Remote Sens 32:390–404CrossRefGoogle Scholar
  28. Niu Z, Gong P, Cheng X, Guo J, Wang L, Huang H, Shen S, Wu Y, Wang X, Wang X, Ying Q, Liang L, Zhang L, Wang L, Yao Q, Yang Z, Guo Z, Dai Y (2009) Geographical characteristics of China’s wetlands derived from remotely sensed data. Sci China, Ser D Earth Sci 52:723–738CrossRefGoogle Scholar
  29. Pontius GR, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19:243–265CrossRefGoogle Scholar
  30. Propastin PA (2008) Simple model for monitoring Balkhash Lake water levels and Ili River discharges: application of remote sensing. Lakes Reserv Res Manag 13:77–81CrossRefGoogle Scholar
  31. Schneider LC, Pontius RG (2001) Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85:83–94CrossRefGoogle Scholar
  32. Shafizadeh MH, Helbich M (2013) Spatiotemporal urbanization processes in the megacity of Mumbai, India: a Markov chains-cellular automata urban growth model. Appl Geogr 40:140–149CrossRefGoogle Scholar
  33. Sivanpillai R, Latchininsk AV, Driese KL, Kambulin VE (2006) Mapping locust habitats in River Ili Delta, Kazakhstan, using Landsat imagery. Agric Ecosyst Environ 117:128–134CrossRefGoogle Scholar
  34. Starodubtsev VM, Truskavetskiy SR (2011) Desertification processes in the Ili River delta under anthropogenic pressure. Water Resour 38:253–256CrossRefGoogle Scholar
  35. Stevens D, Dragicevic S (2007) A GIS-based irregular cellular automata model of land-use change. Environ Plan B Plan Design 34:708–724CrossRefGoogle Scholar
  36. Stevens D, Dragicevic S, Rothley K (2007) iCity: a GIS-CA modelling tool for urban planning and decision making. Environ Model Softw 22:761–773CrossRefGoogle Scholar
  37. Sun H, Chen Y, Li W, Li F, Chen Y, Hao X, Yang Y (2010) Variation and abrupt change of climate in Ili River Basin, Xinjiang. J Geogr Sci 20:652–666CrossRefGoogle Scholar
  38. Tacis Central Asia Action Programme 2006 (2010a) Development and improvement of policy instruments for environmental protection, Republic of Kazakhstan, EuropeAid/127636/C/SER/KZ, Ili-Balkhash LEAP: Task 3 report—hydrology, pp 1–34Google Scholar
  39. Tacis Central Asia Action Programme 2006 (2010b) Development and improvement of policy instruments for environmental protection, Republic of Kazakhstan, EuropeAid/127636/C/SER/KZ, Result 3–1: Ili-Balkhash LEAP, pp 1–56Google Scholar
  40. Veldkamp A, Lambin EF (2001) Predicting land-use change. Agric Ecosyst Environ 85:1–6CrossRefGoogle Scholar
  41. Verburg PH, Soepboer W, Limpiada R, Espaldon MVO, Sharifa M, Veldkamp A (2002) Land use change modelling at the regional scale: the CLUE-S model. Environ Manag 30:391–405CrossRefGoogle Scholar
  42. Verburg PH, de Nijs TCM, van Eck JR, Visser H, de Jong K (2004a) A method to analyse neighbourhood characteristics of land use patterns. Comput Environ Urban Syst 28:667–690CrossRefGoogle Scholar
  43. Verburg PH, Schot P, Dijst M, Veldkamp A (2004b) Land use change modelling: current practice and research priorities. GeoJournal 61:309–324CrossRefGoogle Scholar
  44. Verburg PH, Eickhout B, van Meijl H (2008) A multi-scale, multi-model approach for analyzing the future dynamics of European land use. Ann Reg Sci 42:57–77CrossRefGoogle Scholar
  45. Wang JY, Lu JX (2009) Hydrological and ecological impacts of water resources development in the Ili River Basin. J Nat Resour 24(7):1299–1310 (in Chinese)Google Scholar
  46. Weng QH (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J Environ Manag 64:273–284CrossRefGoogle Scholar
  47. White R, Engelen G (2000) High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput Environ Urban Syst 24:383–400CrossRefGoogle Scholar
  48. Xie L, Long A, Mi D, Wang J (2011) Study on ecological water consumption in delta downstream of Ili River. J Glaciol Geocryol 33(6):1330–1340Google Scholar
  49. Yeh AGO, Li X (2003) Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning. Photogramm Eng Remote Sens 69:1043–1052CrossRefGoogle Scholar
  50. Yeh AGO, Li X (2006) Errors and uncertainties in urban cellular automata. Comput Environ Urban Syst 30:10–28CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Geping Luo
    • 1
  • Tureniguli Amuti
    • 1
    • 2
  • Lei Zhu
    • 3
  • Bulkajyr T. Mambetov
    • 4
  • Bagila Maisupova
    • 4
  • Chi Zhang
    • 1
  1. 1.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesÜrümqiChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina
  3. 3.College of Prataculture and Environment ScienceXinjiang Agricultural UniversityÜrümqiChina
  4. 4.Almaty Branch of Kazakh Scientific Research Institute of ForestryMinistries of AgricultureAlmatyRepublic of Kazakhstan

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