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Modeling land cover change dynamic using a hybrid model approach in Qeshm Island, Southern Iran

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Abstract

Multiple factors including natural and human-induced ones lead to land cover change in the landscape. Therefore, identifying the pattern of land cover change can help inform land-use management and prevent associated issues which can affect the natural resources of the landscape. The aim of this study is to assess land cover change in the Qeshm Island in southern Iran by combining the resulting outputs of multiple modeling methods, cellular automata (CA), Markov chains, and artificial neural networks (ANN) based on land cover maps for the years 1996, 2006, and 2016 that have been extracted from satellite imagery (Landsat 5, 7, and 8). In order to evaluate the accuracy of modeling, the Kappa coefficient was calculated to be 0.8. Then, land cover changes for 2025 were predicted by a hybrid model (CA-Markov-ANN). The results indicate that the classes of built-up areas, vegetation, and mangrove forests have changed more significantly from 1996 to 2016 compared with other classes. Land cover maps generated in this study showed that built-up areas have grown significantly in recent decades due to the region’s growing population and development of ports, commercial, and industrial areas. Due to the climate change, the land area covering vegetation has decreased dramatically. The size of the mangrove forests has increased over the time period of the study (1996–2025). The findings of this study can inform land-use planning decisions by providing them with a comprehensive overview of land cover conditions in the future.

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References

  • Abd El-Kawy, O. R., Rod, J. K., Ismail, H. A., & Suliman, A. S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31, 483–494.

    Google Scholar 

  • Ali, S. M., & Mohammed, M. J. (2013). Gap-filling restoration methods for ETM+ sensor images. Iraqi Journal of Science, 54(1), 206–214.

  • Basse, R. M., Omrani, H., Charif, O., Gerber, P., & Bodis, K. (2014). Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Applied Geography, 53, 160–171.

    Google Scholar 

  • Behera, M. D., Borate, S. N., Panda, S. N., Behera, P. R., & Roy, P. S. (2012). Modelling and analyzing the watershed dynamics using cellular automata (CA)–Markov model–A geo-information based approach. Journal of Earth System Science, 121(4), 1011–1024.

    Google Scholar 

  • Bununu, Y. A. (2017). Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion. International Journal of Urban Sciences, 21(2), 217–237.

    Google Scholar 

  • Canute, H., & Lawrence, M. (2017). A Markovian and cellular automata land-use change predictive model of the Usangu catchment. International Journal of Remote Sensing, 38(1), 64–81.

    Google Scholar 

  • Carvalho, T. I., Carneiro, M. G., & Oliveira, G. M. B. (2019). Improving cellular automata scheduling through dynamics control. IJPEDS., 34(1), 115–141.

    Google Scholar 

  • Cheng, M., Jin, J., Zhang, J., Jiang, H., & Wang, R. (2018). Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau. International Journal of Remote Sensing, 39(2), 470–487.

    Google Scholar 

  • Clancy, D., Tanner, J. E., & McWilliam, S. (2010). Quantifying parameter uncertainty in a coral reef model using Metropolis-coupled Markov chain Monte Carlo. Ecological Modelling, 221, 1337–1347.

    Google Scholar 

  • Coppin, P., Jonckheere, I., Nackaerts, K., & Muys, B. (2004). Digital change detection methods in ecosystem monitoring. International Journal of Remote Sensing, 25(9), 1565–1596.

    Google Scholar 

  • Costanza, R., de Groot, R., Sutton, P., van der Ploeg, S., Anderson, S. J., Kubiszewski, I., et al. (2014). Changes in the global value of ecosystem services. Global Environmental Change, 26(1), 152e158.

    Google Scholar 

  • Dalmiya, C. P., Santhi, N., & Sathyabama, B. (2019). An enhanced back propagation method for change analysis of remote sensing images with adaptive preprocessing. European Journal of Remote Sensing, 1–12.

  • Dou, Y., Millington, J. D. A., Bicudo Da Silva, R. F., McCord, P., Viña, A., Song, Q., Yu, Q., Wu, W., Batistella, M., Emilio, M. E., & Liu, J. (2019). Land-use changes across distant places: design of a telecoupled agent-based model. Journal of Land Use Science, 14(3), 191–209.

    Google Scholar 

  • Du, G., JooShin, K., Yuan, L., & Managi, S. (2018). A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area. International Journal of Geographical Information Science, 32(4), 757–782.

    Google Scholar 

  • Feng, Y., & Tong, X. (2018). Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules. GIScience & Remote Sensing, 55(5), 678–698.

    Google Scholar 

  • Feng, Y., Yang, Q., Hong, Z., & Cui, L. (2018). Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models. Geocarto International, 33(5), 470–488.

    Google Scholar 

  • Flores-Casas, R., & Ortega-Huerta, M. A. (2019). Modelling land cover changes in the tropical dry forest surrounding the Chamela-Cuixmala biosphere reserve, Mexico. International Journal of Remote Sensing, 40(18), 6948–6974.

    Google Scholar 

  • Geoghegan, J., Villar, S. C., Klepeis, P., Mendoza, P. M., Ogneva-Himmelberger, Y., Chowdhury, R. R., et al. (2001). Modeling tropical deforestation in the southern Yucatan peninsular region: comparing survey and satellite data. Agriculture, Ecosystems & Environment, 85(1), 25–46.

    Google Scholar 

  • Grigorescu, I., Kucsicsa, G., Popovici, E. A., Mitrică, B., Mocanu, I., & Dumitraşcu, M. (2019). Modelling land use/cover change to assess future urban sprawl in Romania. Geocarto International, 1–19.

  • Guodong, D., Kong, J. S., Liang, Y., & Shunsuke, M. (2018). A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area. International Journal of Geographical Information Science, 32(4), 757–782.

    Google Scholar 

  • Hashem, N., & Balakrishnan, P. (2015). Change analysis of land use/land cover and modelling urban growth in Greater Doha. Qatar. Annals of GIS, 21(3), 233–247.

    Google Scholar 

  • Hauser, L. T., Vu, G. N., Nguyen, B. A., Dade, E., Nguyen, H. M., Nguyen, T. T. Q., et al. (2017). Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004–2013). Applied Geography, 86, 197–207.

    Google Scholar 

  • Hossain, M. S., Bujang, J. S., Zakaria, M. H., & Hashim, M. (2015). Assessment of Landsat 7 Scan Line Corrector-off data gap-filling methods for seagrass distribution mapping. International Journal of Remote Sensing, 36(4), 1188–1215.

    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.

    Google Scholar 

  • Iranian Statistical Center. (2016). Population data of Qeshm Island. Retrieved 1.10.2018 from https://www.amar.org.ir/

  • Jagarnath, M., Thambiran, T., & Gebreslasie, M. (2019). Modelling urban land change processes and patterns for climate change planning in the Durban metropolitan area, South Africa. Journal of Land Use Science, 14(1), 81–109.

    Google Scholar 

  • Jensen, J. R. (2015). Introductory digital image processing: a remote sensing perspective (3rd ed.). Upper Saddle River, NJ: Prentice-Hall.

    Google Scholar 

  • Jokar, A. J., Helbich, M., Kainz, W., & Darvishi, A. B. (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.

    Google Scholar 

  • Katana, S. J. S., Ucakuwun, E. K., & Munyao, T. M. (2013). Detection and prediction of land cover changes in upper Athi River catchment, Kenya: a strategy towards monitoring environmental changes. Greener Journal of Environmental Management and Public Safety, 2(4), 146–157.

    Google Scholar 

  • Kazemzadeh-Zow, A., Zanganeh-Shahraki, S., Salvati, L., & Samani, N. N. (2017). A spatial zoning approach to calibrate and validate urban growth models. International Journal of Geographical Information Science, 31(4), 763–782.

    Google Scholar 

  • Ke, X., Zheng, W., Zhou, T., & Liu, X. (2017). A CA-based land system change model: LANDSCAPE. International Journal of Geographical Information Science, 31(9), 1798–1817.

    Google Scholar 

  • Kolb, M., Jean-François Mas, J. F., & Leopoldo Galicia, L. (2013). Evaluating drivers of land-use change and transition potential models in a complex landscape in Southern Mexico. International Journal of Geographical Information Science, 27(9), 1804–1827.

    Google Scholar 

  • Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., et al. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change, 11(4), 261–269.

    Google Scholar 

  • Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16, 323–343.

    Google Scholar 

  • Li, S., Peng, M., Wu, C., Feng, X., & Wu, Y. (2015). Optimal selection of GCPs from Global Land Survey 2005 for precision geometric correction of Landsat-8 imagery. European Journal of Remote Sensing, 48(1), 303–318.

    Google Scholar 

  • Li, J., Oyana, T. J., & Mukwaya, P. I. (2016a). An examination of historical and future land use changes in Uganda using change detection methods and agent-based modelling. African Geographical Review, 35(3), 247–271.

    Google Scholar 

  • Li, H., Wang, X., Shen, H., Yuan, Q., & Zhang, L. (2016b). An efficient multi-resolution variational Retinex scheme for the radiometric correction of airborne remote sensing images. International Journal of Remote Sensing, 37(5), 1154–1172.

    Google Scholar 

  • Lin, J., Weihao, W., & W. (2019). Investigating the land use characteristics of urban integration based on remote sensing data: experience from Guangzhou and Foshan. Geocarto International, 34(14), 1608–1620.

    Google Scholar 

  • Liu, X. P., Li, X., Yeh, A. G. O., & Tao, J. (2007). Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science China Earth Sciences, 50, 1578–1588.

    Google Scholar 

  • Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2407.

    Google Scholar 

  • Lu, D., Li, G., & Moran, E. (2014). Current situation and needs of change detection techniques. IJIDF., 5(1), 13–38.

    Google Scholar 

  • Memarian, H., Balasundram, S. K., 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, 4(6), 542–554.

    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, 99(2), 141–153.

    Google Scholar 

  • Mozumder, C., & Tripathi, N. K. (2014). Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network. International Journal of Applied Earth Observation and Geoinformation., 32, 92–104.

    Google Scholar 

  • Munroe, D. K., Croissant, C., & York, A. M. (2005). Land use policy and landscape fragmentation in an urbanizing region: assessing the impact of zoning. Applied Geography, 25(2), 121–141.

    Google Scholar 

  • Nguyen, H.-H., McAlpine, C., Pullar, D., Johansen, K., & Duke, N. C. (2013). The relationship of spatial-temporal changes in fringe mangrove extent and adjacent land-use: case study of Kien Giang coast, Vietnam. Ocean & Coastal Management, 76, 12e22.

    Google Scholar 

  • Nkya, S. E., Hagai, M., & Kashaigili, J. J. (2017). Land cover change impacts on beef cattle productivity under changing climate: case of Ilemela and Magu districts, Tanzania. East African Agricultural and Forestry Journal, 82(2), 188–200.

    Google Scholar 

  • Osman, T., Shaw, D., & Kenawy, E. (2018). An integrated land use change model to simulate and predict the future of greater Cairo metropolitan region. Journal of Land Use Science, 13(6), 565–584.

    Google Scholar 

  • Paegelow, M., Camacho, M. T., Mas, J. F., Houet, T., & Gilmore, R. (2013). Land change modelling: moving beyond projections. International Journal of Geographical Information Science., 27(9), 1691–1695.

    Google Scholar 

  • Paolini, L., Grings, F., Sobrino, J. A., Jiménez Muñoz, J. C., & Karszenbaum, H. (2006). Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies. International Journal of Remote Sensing, 27(4), 685–704.

    Google Scholar 

  • Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., & Plourde, J. (2014). A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environmental Modelling and Software, 51, 250–268.

    Google Scholar 

  • Qiu, B. W., & Chen, C. C. (2008). Land use change simulation model based on MCDM and CA and its application. Acta Geographica Sinica, 63, 165–174.

    Google Scholar 

  • Richards, D. R., & Friess, D. A. (2015). Rates and drivers of mangrove deforestation in Southeast Asia, 2000-2012. Proceedings of the National Academy of Sciences, 113(2), 344–349. 

  • Rumora, L., Miler, M., & Medak, D. (2019). Contemporary comparative assessment of atmospheric correction influence on radiometric indices between Sentinel-2A and Landsat 8 imagery. Geocarto International, 1–15. 

  • Sano, E. E., Ferreira, L. G., Asner, G. P., Steinke, E. T., & E. T. (2007). Spatial and temporal probabilities of obtaining cloud-free Landsat images over the Brazilian tropical savanna. International Journal of Remote Sensing, 28(12), 2739–2752.

    Google Scholar 

  • Shimizu, K., Ota, T., Mizoue, N., & Yoshida, S. (2018). Assessments of preprocessing methods for Landsat time series images of mountainous forests in the tropics. Journal of Forest Research, 23(3), 139–148.

    Google Scholar 

  • Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.

    Google Scholar 

  • Singh, S. K., Basommi Laari, P., Mustak, S. K., Srivastava, P. K., & Szabó, S. (2018). Modelling of land use land cover change using earth observation data-sets of tons River Basin, Madhya Pradesh, India. Geocarto International, 33(11), 1202–1222.

    Google Scholar 

  • Tolnai, M., János György Nagy, J. G., & Bakó, G. (2016). Spatiotemporal distribution of Landsat imagery of Europe using cloud cover-weighted metadata. Journal of Maps, 12(5), 1084–1088.

    Google Scholar 

  • Valdez, M., Chen, C. F., Chiang, S. H., Chang, K. T., Lin, Y. W., Chen, Y. F., & Yu-Chi Chou, Y. C. (2019). Illegal land use change assessment using GIS and remote sensing to support sustainable land management strategies in Taiwan. Geocarto International, 34(2), 133–148.

    Google Scholar 

  • Wu, H., Li, Z., Clarke, K. C., Shi, W., Fang, L., Lin, A., & Zhou, J. (2019). Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. International Journal of Geographical Information Science, 33(5), 1040–1061.

    Google Scholar 

  • Wyman, M. S., & Stein, T. V. (2010). Modeling social and land-use/land-cover change data to assess drivers of smallholder deforestation in Belize. Applied Geography, 30(3), 329–342.

    Google Scholar 

  • Yan, Y., Hua, W., Liu, X., Cui, Z., & Diao, D. (2019). Spatial–spectral preprocessing for spectral unmixing. International Journal of Remote Sensing, 40(4), 1357–1373.

    Google Scholar 

  • Yang, Q. S., & Li, X. (2007). Integration of multi-agent systems with cellular automata for simulating urban land expansion. Scientia Geographica Sinica, 27, 542–548.

    Google Scholar 

  • Yang, Q., Li, X., & Shi, X. (2008). Cellular automata for simulating land use changes based on support vector machines. Computational Geosciences, 34(6), 592–602.

    Google Scholar 

  • Yang, X., Zheng, X., & Lv, L. N. A. (2012). Spatio-temporal model of land use change based on ant colony optimization,Markov chain and cellular automata. Ecological Modelling, 233, 11–19.

    Google Scholar 

  • Ye, Y., Su, Y., Zhang, H. O., Liu, K., & Wu, Q. (2015). Construction of an ecological resistance surface model and its application in urban expansion simulations. Journal of Geographical Sciences, 25(2), 211–224.

    Google Scholar 

  • Yin, G., Mariethoz, G., Sun, Y., & McCabe, M. F. (2017). A comparison of gap-filling approaches for Landsat-7 satellite data. International Journal of Remote Sensing, 38(23), 6653–6679.

    Google Scholar 

  • Zhai, Y., Yao, Y., Guan, Q., Liang, X., Li, X., Pan, Y., et al. (2020). Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata. International Journal of Geographical Information Science, 1–25. 

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Tajbakhsh, A., Karimi, A. & Zhang, A. Modeling land cover change dynamic using a hybrid model approach in Qeshm Island, Southern Iran. Environ Monit Assess 192, 303 (2020). https://doi.org/10.1007/s10661-020-08270-w

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