Skip to main content

Advertisement

SpringerLink
Log in

Comparison of three hybrid models to simulate land use changes: a case study in Qeshm Island, Iran

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Land use change simulation is an important issue for its role in predicting future trends and providing implications for sustainable land management. Hybrid models have become a recognized strategy to inform decision-makers, but further attempts are needed to warrant the reliability of their projected results. In view of this, three hybrid models, including the cellular automata-Markov chain-artificial neural network, cellular automata-Markov chain-logistic regression, and Markov chain-artificial neural network, were applied to simulate land use change on the largest island in Iran, Qeshm Island. The Figure of Merit (FOM) was used to measure the modeling accuracy of the simulations, with the FOMs for the three models 6.7, 5.1, and 4.5, respectively. Consequently, the cellular automata-Markov chain-artificial neural network most precisely simulates land use change on Qeshm Island and is, thus, used to simulate land use change until 2026. The simulation shows that the incremental trend of the built-up class will continue in the coming years. Meanwhile, the areas of valuable ecosystems, such as mangroves, tend to decrease. Despite the protection plans for mangroves, these areas require more attention and conservation planning. This study demonstrates a referential example to select the proper land use models for informing planning and management in similar coastal zones.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: a review. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2016.07.007.

    Google Scholar 

  • Alilou, H., Moghaddam Nia, A., Keshtkar, H. R., Han, D., & Bray, M. (2018). A cost-effective and efficient framework to determine water quality monitoring network locations. The Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2017.12.121.

    CAS  Google Scholar 

  • Arsanjani, J. J., Helbich, M., Kainz, W., & Boloorani, A. D. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2011.12.014.

    Google Scholar 

  • Benito, P. R., Cuevas, J. A., Bravo, R., Barrio, J. M. G. D., & Zavala, M. A. (2010). Land use change in a Mediterranean metropolitan region and its periphery: assessment of conservation policies through CORINE land cover data and Markov models. Forest Systems, 19, 315–328.

    Google Scholar 

  • Bihamta Toosi, N., Soffianian, A., Fakheran, S., Pourmanafi, S., Ginzler, C., & Waser, L. (2019). Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, e00662. https://doi.org/10.1016/j.gecco.2019.e00662.

    Google Scholar 

  • Chu, L., Sun, T., Wang, T., Li, Z., & Cai, C. (2018). Evolution and prediction of landscape pattern and habitat quality based on CA-Markov and InVEST model in Hubei section of three gorges reservoir area (TGRA). Sustainability. https://doi.org/10.3390/su10113854.

    Google Scholar 

  • De Rosa, M., Knudsen, M. T., & Hermansen, J. E. (2016). A comparison of land use change models: challenges and future developments. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2015.11.097.

    Google Scholar 

  • Eastman, J. R. (2015). IDRISI TerrSet, guide to GIS and image processing, manual version 18.00. Worcester: Clark University.

    Google Scholar 

  • Ebrahimi-Sirizi, Z., & Riyahi-Bakhtiyari, A. (2013). Petroleum pollution in mangrove forests sediments from Qeshm Island and Khamir Port—Persian Gulf, Iran. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-012-2846-z.

    Google Scholar 

  • Etemadi, H., Smoak, J., & Karami, J. (2018). Land use change assessment in coastal mangrove forests of Iran utilizing satellite imagery and CA–Markov algorithms to monitor and predict future change. Environmental Earth Sciences, 77, 208. https://doi.org/10.1007/s12665-018-7392-8.

    Article  Google Scholar 

  • Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S., Nayak, S. K., Ghosh, S., Mitra, D., Ghosh, T., & Hazra, S. (2017). Application of Cellular automata and Markov-chain model in geospatial environmental modeling- a review. Remote Sensing Applications: Society and Environment. https://doi.org/10.1016/j.rsase.2017.01.005.

    Google Scholar 

  • Hamdy, O., Zhao, S., Osman, T., Salheen, M. A., & Eid, Y. Y. (2016). Applying a hybrid model of Markov chain and logistic regression to identify future urban sprawl in Abouelreesh, Aswan: a case study. Geosciences. https://doi.org/10.3390/geosciences6040043.

    Google Scholar 

  • Hu, X., & Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113(10), 2089–2102.

    Google Scholar 

  • Hu, X., Li, X., & Lu, L. (2018). Modeling the land use change in an arid oasis constrained by water resources and environmental policy change using cellular automata models. Sustainability. https://doi.org/10.3390/su10082878.

    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. https://doi.org/10.1080/01431161.2016.1259675.

    Google Scholar 

  • Iran’s Population and Housing Census. (2016). from https://amar.sci.org.ir/index_e.aspx.

  • 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. Applied Geography, 29, 435–447. https://doi.org/10.1016/j.apgeog.2008.10.002.

    Google Scholar 

  • Karimi, H., Jafarnezhad, J., Khaledi, J., & Ahmadi, P. (2018). Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran. Arabian Journal of Geosciences, 11, 592–599. https://doi.org/10.1007/s12517-018-3940-5.

    Article  Google Scholar 

  • Kazemzadeh-Zow, A., Shahraki, S. Z., Salvati, L., & Samani, N. N. (2017). A spatial zoning approach to calibrate and validate urban growth models. International Journal of Geographical Information Science. https://doi.org/10.1080/13658816.2016.1236927.

    Google Scholar 

  • Keshtkar, H., & Voigt, W. (2016). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment, 2, 2–13. https://doi.org/10.1007/s40808-015-0068-4.

    Article  Google Scholar 

  • Khoorani, A., Bineiaz, M., & Amiri, H. R. (2015). Mangrove forest area changes due to climatic changes (case study: forest between the port and the Khamir island). Journal of Aquatic Ecology, 5(2), 100–111 (In Persian with English abstract).

    Google Scholar 

  • Kokabi, M., Yousefzadi, M., Razaghi, M., & Feghhi, M. A. (2016). Zonation patterns, composition and diversity of macroalgal communities in the eastern coasts of Qeshm Island, Persian Gulf, Iran. Marine Biodiversity Records. https://doi.org/10.1186/s41200-016-0096-4.

  • Koomen, E., Stillwell, J., Bakema, A., & Scholten, H. J. (2007). Modelling land use change: progress and applications. GeoJournal. https://doi.org/10.1007/978-1-4020-5648-2.

    Google Scholar 

  • Kourosh Niya, A., Jinliang, H., Kazemzadeh-Zow, A., & Naimi, B. (2019). An adding/deleting approach to improve land change modeling: a case study in Qeshm Island, Iran. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-019-4504-z.

  • Li, X., Chen, Y., Liu, X., Xu, X., & Chen, G. (2017). Experiences and issues of using cellular automata for assisting urban and regional planning in China. International Journal of Geographical Information Science. https://doi.org/10.1080/13658816.2017.1301457.

    Google Scholar 

  • Liu, D., Zheng, X., Zhang, C., & Wang, H. (2017). A new temporal–spatial dynamics method of simulating land use change. Ecological Modeling. https://doi.org/10.1016/j.ecolmodel.2017.02.005.

    Google Scholar 

  • Mafi-Gholami, D., Zenner, E. K., Jaafari, A., & Ward, R. D. (2019). Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran. The Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2018.11.462.

    CAS  Google Scholar 

  • Memarian, H., Kumar Balasundram, S., Talib, J. B., Sung, C. T. B., Sood, A. M., & Abbaspour, K. (2012). Validation of CA-Markov for simulation of land use and cover change in the Langat Basin, Malaysia. Journal of Geographic Information System. https://doi.org/10.4236/jgis.2012.46059.

    Google Scholar 

  • Mirza, R., Moeinaddini, M., Pourebrahim, S., & Zahed, M. A. (2019). Contamination, ecological risk and source identification of metals by multivariate analysis in surface sediments of the khouran Straits, the Persian Gulf. Marine Pollution Bullten. https://doi.org/10.1016/j.marpolbul.2019.06.028.

    CAS  Google Scholar 

  • Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning. https://doi.org/10.1016/j.landurbplan.2010.10.001.

    Google Scholar 

  • Mustafa, Y. T. (2020). Multi-temporal satellite data for land use/cover (LULC) change detection in Zakho, Kurdistan Region-Iraq. In A. Al-Quraishi & A. Negm (Eds.), Environmental remote sensing and GIS in Iraq. Cham: Springer Water. https://doi.org/10.1007/978-3-030-21344-2_7.

    Chapter  Google Scholar 

  • Mustafa, A., Rienow, A., Saadi, I., Cools, M., & Teller, J. (2018). Comparing support vector machines with logistic regression for calibrating cellular automata land use change models. European Journal of Remote Sensing, 51, 391–401. https://doi.org/10.1080/22797254.2018.1442179.

    Article  Google Scholar 

  • Newman, G., Lee, J., & Berke, P. (2016). Using the land transformation model to forecast vacant land. Journal of Land Use Science. https://doi.org/10.1080/1747423X.2016.1162861.

    Google Scholar 

  • Olmedo, M. T. C., Pontius Jr., R. G., Paegelow, M., & Mas, J. F. (2015). Comparison of simulation models in terms of quantity and allocation of land change. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2015.03.003.

    Google Scholar 

  • Pickard, B., Gray, J., & Meentemeyer, R. (2017). Comparing quantity, allocation and configuration accuracy of multiple land change models. Land. https://doi.org/10.3390/land6030052.

    Google Scholar 

  • Pontius Jr., 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), 239–248.

    Google Scholar 

  • Pontius Jr., R. G., & Millones, M. (2011). Death to kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407-4429. https://doi.org/10.1080/01431161.2011.552923.

    Google Scholar 

  • Pourahmad, A., Hosseini, A., Pourahmad, A., Zoghi, M., & Sadat, M. (2018). Tourist value assessment of Geotourism and environmental capabilities in Qeshm Island-Iran. Geoheritage., 10, 687–706. https://doi.org/10.1007/s12371-017-0273-9.

    Article  Google Scholar 

  • Rimal, B., Zhang, L., Keshtkar, H. R., Haack, B., Rijal, S., & Zhang, P. (2018). Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and Markov chain. ISPRS International Journal of Geo-Information. https://doi.org/10.3390/ijgi7040154.

    Google Scholar 

  • 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. https://doi.org/10.1016/j.mcm.2010.11.019.

    Google Scholar 

  • Shadman-Roodposhti, M., Aryal, J., & Bryan, B. A. (2019). A novel algorithm for calculating transition potential in cellular automata models of land use/cover change. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2018.10.006.

    Google Scholar 

  • Shafizadeh-Moghadam, H., Asghari, A., Taleai, M., Helbich, M., & Tayyebi, A. (2017). Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata. GIScience & Remote Sensing. https://doi.org/10.1080/15481603.2017.1309125.

    Google Scholar 

  • Silva, H. J. F., Gonçalves, W. A., & Bezerra, B. G. (2019). Comparative analyzes and use of evapotranspiration obtained through remote sensing to identify deforested areas in the Amazon. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2019.01.015.

    Google Scholar 

  • Sun, B., & Robinson, D. T. (2018). Comparisons of statistical approaches for Modelling land use change. Land. https://doi.org/10.3390/land7040144.

    Google Scholar 

  • Tajbakhsh, S. M., Memarian, H., Moradi, K., & Afshar, A. H. A. (2018). Performance comparison of land change modeling techniques for land use projection of arid watersheds. Global Journal of Environmental Science and Management. https://doi.org/10.22034/gjesm.2018.03.002.

  • Varga, O. G., Pontius Jr., R. G., Singh, S. K., & Szabó, S. (2019). Intensity analysis and the Figure of Merit’s components for assessment of a cellular automata – Markov simulation model. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2019.01.057.

    Google Scholar 

  • Verburg, P., Schot, P., Dijst, M., & Veldkamp, A. (2004). Land use change modeling: current practice and research priorities. GeoJournal., 61, 309–324. https://doi.org/10.1007/s10708-004-4946-y.

    Article  Google Scholar 

  • Xu, T., Gao, J., & Coco, G. (2019). Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata. International Journal of Geographical Information Science. https://doi.org/10.1080/13658816.2019.1600701.

    Google Scholar 

  • Yan, R., Cai, Y., Li, C., Wang, X., & Liu, Q. (2019). Hydrological responses to climate and land use changes in a watershed of the Loess Plateau, China. Sustainability. https://doi.org/10.3390/su11051443.

    Google Scholar 

  • Yang, X., Zheng, X. Q., & Lv, L. N. (2012). A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling. https://doi.org/10.1016/j.ecolmodel.2012.03.011.

    Google Scholar 

  • Yao, Y., Li, J., Zhang, X., Duan, P., Li, S., & Xu, Q. (2017). Investigation on the expansion of urban construction land use based on the CART-CA model. ISPRS International Journal of Geo-Information, 6(5), 149. https://doi.org/10.3390/ijgi6050149.

    Google Scholar 

  • Yazdi, A., & Dabiri, R. (2018). Investigating the Geotourism phenomena in eroded land of Iran, Qeshm Island; Revista Publicando. 5 No 16. (2). 2018, 35–94. ISSN 1390-9304.

  • Yirsaw, E., Wu, W., Shi, X., Temesgen, H., & Bekele, B. (2017). Land use/land cover change modeling and the prediction of subsequent changes in ecosystem service values in a coastal area of China, the Su-Xi-Chang Region. Sustainability. https://doi.org/10.3390/su9071204.

    Google Scholar 

  • Yuan, H., Van Der Wiele, C. F., & Khorram, S. (2009). An automated artificial neural network system for land use/land cover classification from Landsat TM imagery. Remote Sensing, 1, 243–265.

    Google Scholar 

Download references

Acknowledgments

Anonymous reviewers supplied constructive feedback that helped to improve this manuscript.

Funding

This research was supported by the Chinese Government Marine Scholarship (Grant No. 2016SOA016).

Author information

Authors and Affiliations

  1. Coastal & Ocean Management Institute, Xiamen University, Xiamen, 361102, Fujian, China

    Ali Kourosh Niya & Jinliang Huang

  2. Department of RS and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

    Ali Kazemzadeh-Zow

  3. Department of Environmental Science, University of Zakho, Zakho, Kurdistan Region, Iraq

    Hazhir Karimi

  4. Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran

    Hamidreza Keshtkar

  5. Department of Geosciences and Geography, University of Helsinki, Helsinki, 00014, Finland

    Babak Naimi

Authors
  1. Ali Kourosh Niya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinliang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Kazemzadeh-Zow
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hazhir Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hamidreza Keshtkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Babak Naimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinliang Huang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kourosh Niya, A., Huang, J., Kazemzadeh-Zow, A. et al. Comparison of three hybrid models to simulate land use changes: a case study in Qeshm Island, Iran. Environ Monit Assess 192, 302 (2020). https://doi.org/10.1007/s10661-020-08274-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-020-08274-6

Keywords

Search

Navigation