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.
Similar content being viewed by others
References
Abdrasilov SA, Tulebaeva KA (1994) Dynamics of the Ili delta with consideration of fluctuations of the level Lake Balkhash. Hydrotech Constr 28:421–426
Abulaiti R (2012) Trends of precipitation over the Ili valley. Gansu Water Resour Hydropower Technol 48(7):1–4 (in Chinese)
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 964:1–36
Baker C, Lawrence R, Montague C, Patten D (2006) Mapping wetlands and riparian areas using Landsat ETM + imagery and decision-tree-based models. Wetlands 26:465–474
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 28:56–66
Burgi M, Hersperger AM, Schneeberger N (2004) Driving forces of landscape change—current and new directions. Landsc Ecol 19:857–868
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 19:103–123
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 113:893–903
Donker DK, Hasman A, Van Geijn HP (1993) Interpretation of low kappa values. Int J Biomed Comput 33:55–64
Eastman JR (1999) Idrisi 32: user’s guide. Clark University, Worcester
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 114:1–17
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 53:1036–1042
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):3761–3772
Hepinstall JA, Alberti M, Marzluff JM (2008) Predicting land cover change and avian community responses in rapidly urbanizing environments. Lands Ecol 23:1257–1276
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 5:63–76
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 22:97–108
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
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):435–447
Kezer K, Matsuyama H (2006) Decrease of river runoff in the Lake Balkhash basin in Central Asia. Hydrol Process 20:1407–1423
Kipshakbaev NK, Abdrasilov SA (1994) Effect of economic activity on the hydrologic regime and dynamics of the Ili delta. Hydrotech Constr 28:416–418
Lal R, Suleimenov M, Stewart BA, Hansen DO, Doraiswamy P (2007) Climate change and terrestrial carbon sequestration in Central Asia. Taylor & Francis, London
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 68:1091–1099
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28:823–870
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 7:198–207
Mikhailov VN (2004) The impact of deltas on the mean long-term water river runoff. Water Resour 31:351–356
Mondal P, Southworth J (2010) Evaluation of conservation interventions using a cellular automata-Markov model. For Ecol Manag 260(10):1716–1725
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 32:390–404
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 52:723–738
Pontius GR, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19:243–265
Propastin PA (2008) Simple model for monitoring Balkhash Lake water levels and Ili River discharges: application of remote sensing. Lakes Reserv Res Manag 13:77–81
Schneider LC, Pontius RG (2001) Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85:83–94
Shafizadeh MH, Helbich M (2013) Spatiotemporal urbanization processes in the megacity of Mumbai, India: a Markov chains-cellular automata urban growth model. Appl Geogr 40:140–149
Sivanpillai R, Latchininsk AV, Driese KL, Kambulin VE (2006) Mapping locust habitats in River Ili Delta, Kazakhstan, using Landsat imagery. Agric Ecosyst Environ 117:128–134
Starodubtsev VM, Truskavetskiy SR (2011) Desertification processes in the Ili River delta under anthropogenic pressure. Water Resour 38:253–256
Stevens D, Dragicevic S (2007) A GIS-based irregular cellular automata model of land-use change. Environ Plan B Plan Design 34:708–724
Stevens D, Dragicevic S, Rothley K (2007) iCity: a GIS-CA modelling tool for urban planning and decision making. Environ Model Softw 22:761–773
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 20:652–666
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 1–34
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 1–56
Veldkamp A, Lambin EF (2001) Predicting land-use change. Agric Ecosyst Environ 85:1–6
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 30:391–405
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 28:667–690
Verburg PH, Schot P, Dijst M, Veldkamp A (2004b) Land use change modelling: current practice and research priorities. GeoJournal 61:309–324
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 42:57–77
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)
Weng QH (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J Environ Manag 64:273–284
White R, Engelen G (2000) High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput Environ Urban Syst 24:383–400
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):1330–1340
Yeh AGO, Li X (2003) Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning. Photogramm Eng Remote Sens 69:1043–1052
Yeh AGO, Li X (2006) Errors and uncertainties in urban cellular automata. Comput Environ Urban Syst 30:10–28
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Editor: Juan Ignacio Lopez Moreno.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Luo, G., Amuti, T., Zhu, L. et al. Dynamics of landscape patterns in an inland river delta of Central Asia based on a cellular automata-Markov model. Reg Environ Change 15, 277–289 (2015). https://doi.org/10.1007/s10113-014-0638-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10113-014-0638-4