Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Arid Land Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • Bacon J. 2016. The most polluted city is? Hint: It’s not in China. USA Today. [2016-12-19]. https://doi.org/www.usatoday.com/story/news/world/2016/12/19/most-polluted-city-is-not-in-china/95606914/#.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bhatta B. 2010. Analysis of Urban Growth and Sprawl from Remote Sensing Data. Berlin: Springer-Verlag Berlin Heidelberg, 172.

    Book  Google Scholar 

  • Brémaud P. 2013. Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues. New York: Springer Science & Business Media, 445.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Eastman J R. 2012. IDRISI Selva. Worcester: Clark University, 354.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Foley J A, DeFries R, Asner G P, et al. 2005. Global consequences of land use. Science, 309(5734): 570–574.

    Article  Google Scholar 

  • Foltz R C. 2002. Iran’s water crisis: cultural, political, and ethical dimensions. Journal of Agricultural & Environmental Ethics, 15(4): 357–380.

    Article  Google Scholar 

  • 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 

  • Herman J R, Bergen J R, Peleg S, et al. 2000. Method and apparatus for mosaic image construction: Google Patents. [2000-06-13]. https://doi.org/www.google.com/patents/US6075905.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • October 2004. Nairobi: African Association of Remote Sensing of the Environment.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Lillesand T, Kiefer R W, Chipman J. 2014. Remote Sensing and Image Interpretation. New York: John Wiley & Sons, 721.

    Google Scholar 

  • 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 

  • Lunetta R S, Lyon J G. 2004. Remote sensing and GIS accuracy assessment. Florida: CRC Press, 394.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • McGarigal K, Marks B J. 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. General Technical Report. PNW-GTR-351. Portland, USA.

    Book  Google Scholar 

  • Metternicht G I., Zinck J A. 2003. Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85(1): 1–20.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Module F. 2009. Atmospheric Correction Module: QUAC and FLAASH User’s Guide (ver. 4). Boulder: Harris Geospatial Co., 44.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Rozema J, Flowers T. 2008. Crops for a salinized world. Science, 322(5907): 1478–1480.

    Article  Google Scholar 

  • Saaty T L. 2008. Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1): 83–98.

    Article  Google Scholar 

  • Shahbaz M, Ashraf M. 2013. Improving salinity tolerance in cereals. Critical Reviews in Plant Sciences, 32(4): 237–249.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Tutorial E-Z. 2010. ENVI user guide. Colorado Springs, CO: ITT, 590.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Department of Natural Resources, Isfahan University of Technology, Isfahan, 8415683111, Iran

    Mohammad Shafiezadeh, Hossein Moradi & Sima Fakheran

Authors
  1. Mohammad Shafiezadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Moradi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sima Fakheran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossein Moradi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shafiezadeh, M., Moradi, H. & Fakheran, S. Evaluating and modeling the spatiotemporal pattern of regional-scale salinized land expansion in highly sensitive shoreline landscape of southeastern Iran. J. Arid Land 10, 946–958 (2018). https://doi.org/10.1007/s40333-018-0104-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40333-018-0104-0

Keywords

Search

Navigation