Journal of Arid Land

, Volume 10, Issue 6, pp 946–958 | Cite as

Evaluating and modeling the spatiotemporal pattern of regional-scale salinized land expansion in highly sensitive shoreline landscape of southeastern Iran

  • Mohammad Shafiezadeh
  • Hossein MoradiEmail author
  • Sima Fakheran


Taking an area of about 2.3×104 km2 of southeastern Iran, this study aims to detect and predict regional-scale salt-affected lands. Three sets of Landsat images, each set containing 4 images for 1986, 2000, and 2015 were acquired as the main source of data. Radiometric, atmospheric and cutline blending methods were used to improve the quality of images and help better classify salinized land areas under the support vector machine method. A set of landscape metrics was also employed to detect the spatial pattern of salinized land expansion from 1986 to 2015. Four factors including distance to sea, distance to sea water channels, slope, and elevation were identified as the main contributing factors to land salinization. These factors were then integrated using the multi-criteria evaluation (MCE) procedure to generate land sensitivity map to salinization and also to calibrate the cellular-automata (CA) Markov chain (CA-Markov) model for simulation of salt-affected lands up to 2030, 2040 and 2050. The results of this study showed a dramatic dispersive expansion of salinized land from 7.7 % to 12.7% of the total study area from 1986 to 2015. The majority of areas prone to salinization and the highest sensitivity of land to salinization was found to be in the southeastern parts of the region. The result of the MCE-informed CA-Markov model revealed that 20.3% of the study area is likely to be converted to salinized lands by 2050. The findings of this research provided a view of the magnitude and direction of salinized land expansion in a past-to-future time period which should be considered in future land development strategies.


soil salinization remote sensing CA-Markov salt land expansion southeastern Iran 


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The authors are pleased to thank the companionship of personnel of the Chabahar Department of Environment, especially Mr. Ashrafali HOSSEINI in providing valuable data and facilitating field surveys and Mr. Ali ASGARIAN for improving the English writing.


  1. Arsanjani J J, Helbich M, Kainz W, et al. 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.CrossRefGoogle Scholar
  2. Bacon J. 2016. The most polluted city is? Hint: It’s not in China. USA Today. [2016-12-19]. Scholar
  3. Benedek C, Szirányi T. 2009. Change detection in optical aerial images by a multilayer conditional mixed Markov model. IEEE Transactions on Geoscience and Remote Sensing, 47(10): 3416–3430.CrossRefGoogle Scholar
  4. Bhatta B. 2010. Analysis of Urban Growth and Sprawl from Remote Sensing Data. Berlin: Springer-Verlag Berlin Heidelberg, 172.CrossRefGoogle Scholar
  5. Brémaud P. 2013. Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues. New York: Springer Science & Business Media, 445.Google Scholar
  6. 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, 24(2): 247–261.CrossRefGoogle Scholar
  7. Cooley T, Anderson G P, Felde G W, et al. 2002. FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. In: IEEE, IEEE International Geoscience and Remote Sensing Symposium. Toronto: IEEE.CrossRefGoogle Scholar
  8. DeFries R S, Hansen M C, Townshend J R G, et al. 2000. A new global 1km dataset of percentage tree cover derived from remote sensing. Global Change Biology, 6(2): 247–254.CrossRefGoogle Scholar
  9. Eastman J R. 2012. IDRISI Selva. Worcester: Clark University, 354.Google Scholar
  10. El-Hallaq M A, Habboub M O. 2015. Using Cellular Automata-Markov Analysis and Multi Criteria Evaluation for Predicting the Shape of the Dead Sea. Advances in Remote Sensing, 4(1): 83.CrossRefGoogle Scholar
  11. Foley J A, DeFries R, Asner G P, et al. 2005. Global consequences of land use. Science, 309(5734): 570–574.CrossRefGoogle Scholar
  12. Foltz R C. 2002. Iran’s water crisis: cultural, political, and ethical dimensions. Journal of Agricultural & Environmental Ethics, 15(4): 357–380.CrossRefGoogle Scholar
  13. Hanin M, Ebel C, Ngom M, et al. 2016. New Insights on plant salt tolerance mechanisms and their potential use for breeding. Frontiers in Plant Science, 7.Google Scholar
  14. Herman J R, Bergen J R, Peleg S, et al. 2000. Method and apparatus for mosaic image construction: Google Patents. [2000-06-13]. Scholar
  15. Houghton R A, Nassikas A A. 2017. Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochemical Cycles, 31(3): 456–470.CrossRefGoogle Scholar
  16. Hurskainen P, Pellikka P. 2004. Change detection of informal settlements using multi-temporal aerial photographs–the case of Voi, SE-Kenya. In: Proceedings of the 5th African Association of Remote Sensing of the Environment Conference, 18–2.Google Scholar
  17. October 2004. Nairobi: African Association of Remote Sensing of the Environment.Google Scholar
  18. Hyandye C, Martz L W. 2017. A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. International Journal of Remote Sensing, 38(1): 64–81.CrossRefGoogle Scholar
  19. Jamil A, Riaz S, Ashraf M, et al. 2011. Gene expression profiling of plants under salt stress. Critical Reviews in Plant Sciences, 30(5): 435–458.CrossRefGoogle Scholar
  20. Kaufman Y J, Wald A E, Remer L A, et al. 1997. The MODIS 2.1-/spl mu/m channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1286–1298.CrossRefGoogle Scholar
  21. Kim D-H, Sexton J O, Noojipady P, et al. 2014. Global, Landsat-based forest-cover change from 1990 to 2000. Remote Sensing of Environment, 155: 178–193.CrossRefGoogle Scholar
  22. Lambin E F, Geist H J. 2008. Land-Use and Land-Cover Change: Local Processes and Global Impacts. Berlin: Springer Science & Business Media, 221.Google Scholar
  23. Lillesand T, Kiefer R W, Chipman J. 2014. Remote Sensing and Image Interpretation. New York: John Wiley & Sons, 721.Google Scholar
  24. Lin Z, Zhou D, Liu L. 2006. Regional-Scale Assessment and Simulation of Land Salinization Using Cellular Automata-Markov Model. In: ASABE/CSBE North Central Intersectional Meeting. Michigan: American Society of Agricultural and Biological Engineers, RRV12110.Google Scholar
  25. Lunetta R S, Lyon J G. 2004. Remote sensing and GIS accuracy assessment. Florida: CRC Press, 394.CrossRefGoogle Scholar
  26. Mahiny A S, Clarke K C. 2012. Guiding SLEUTH land-use/land-cover change modeling using multicriteria evaluation: towards dynamic sustainable land-use planning. Environment and Planning B, 39(5): 925–944.CrossRefGoogle Scholar
  27. McDowell N G, Coops N C, Beck P S, et al. 2015. Global satellite monitoring of climate-induced vegetation disturbances. Trends in Plant Science, 20(2): 114–123.CrossRefGoogle Scholar
  28. McGarigal K, Marks B J. 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. General Technical Report. PNW-GTR-351. Portland, USA.CrossRefGoogle Scholar
  29. Metternicht G I., Zinck J A. 2003. Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85(1): 1–20.CrossRefGoogle Scholar
  30. Meyfroidt P, Lambin E F, Erb K-H, et al. 2013. Globalization of land use: distant drivers of land change and geographic displacement of land use. Current Opinion in Environmental Sustainability, 5(5): 438–444.CrossRefGoogle Scholar
  31. Module F. 2009. Atmospheric Correction Module: QUAC and FLAASH User’s Guide (ver. 4). Boulder: Harris Geospatial Co., 44.Google Scholar
  32. Moradi H. 2016. Identification of Environmental Resources and Spatial zoning of Makran Coastal Area, Southeastern Iran. In: Landuse & Land Cover Change Report (1st ed.). Department of Environment, Iran.Google Scholar
  33. 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.CrossRefGoogle Scholar
  34. Palmate S S, Pandey A, Mishra S K. 2017. Modelling spatiotemporal land dynamics for a trans-boundary river basin using integrated Cellular Automata and Markov Chain approach. Applied Geography, 82: 11–23.CrossRefGoogle Scholar
  35. Pontius R G, Schneider L C. 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1–3): 239–248.CrossRefGoogle Scholar
  36. Roy D P, Wulder M A, Loveland T R, et al. 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145: 154–172.CrossRefGoogle Scholar
  37. Rozema J, Flowers T. 2008. Crops for a salinized world. Science, 322(5907): 1478–1480.CrossRefGoogle Scholar
  38. Saaty T L. 2008. Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1): 83–98.CrossRefGoogle Scholar
  39. Shahbaz M, Ashraf M. 2013. Improving salinity tolerance in cereals. Critical Reviews in Plant Sciences, 32(4): 237–249.CrossRefGoogle Scholar
  40. Shalaby A, Tateishi R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1): 28–41.CrossRefGoogle Scholar
  41. Shrivastava P, Kumar R. 2015. Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi Journal of Biological Sciences, 22(2): 123–131.CrossRefGoogle Scholar
  42. Thenkabail P S, Biradar C M, Noojipady P, et al. 2009. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. International Journal of Remote Sensing, 30(14): 3679–3733.CrossRefGoogle Scholar
  43. Tutorial E-Z. 2010. ENVI user guide. Colorado Springs, CO: ITT, 590.Google Scholar
  44. Wu K Y, Ye X Y, Qi Z F, et al. 2013. Impacts of land use/land cover change and socioeconomic development on regional ecosystem services: The case of fast-growing Hangzhou metropolitan area, China. Cities, 31: 276–284.CrossRefGoogle Scholar
  45. Wu W, Mhaimeed A S, Al-Shafie W M, et al. 2014. Mapping soil salinity changes using remote sensing in Central Iraq. Geoderma Regional, 2–3: 21–31.CrossRefGoogle Scholar
  46. Xu J, Grumbine R E. 2014. Building ecosystem resilience for climate change adaptation in the Asian highlands. Wiley Interdisciplinary Reviews: Climate Change, 5(6): 709–718.Google Scholar
  47. Zahed M A, Rouhani F, Mohajeri S, et al. 2010. An overview of Iranian mangrove ecosystems, northern part of the Persian Gulf and Oman Sea. Acta Ecologica Sinica, 30(4): 240–244.CrossRefGoogle Scholar
  48. Zhou D, Lin Z, Liu L. 2012. Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis. Science of the Total Environment, 439: 260–274.CrossRefGoogle Scholar
  49. Zhu Z, Woodcock C E, Holden C, et al. 2015. Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time. Remote Sensing of Environment, 162: 67–83.CrossRefGoogle Scholar

Copyright information

© Xinjiang Institute of Ecology and Geography, the Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mohammad Shafiezadeh
    • 1
  • Hossein Moradi
    • 1
    Email author
  • Sima Fakheran
    • 1
  1. 1.Department of Natural ResourcesIsfahan University of TechnologyIsfahanIran

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