Analysis and prediction of land use changes: the case study of coastal areas of Gilan province

Document Type : Original Article


Department of Urban and Regional Planning, Faculty of Art and Architecture, Tarbiat Modares University, Tehran, Iran


Land use changes in coastal areas of Gilan Province in recent decades have caused problems such as forest and wetland degradation, soil erosion, biodiversity reduction, and increased environmental pollution. This region is important in terms of its unique features and human use of this environment for a variety of residential, industrial, and recreational activities. Therefore, it is necessary to be aware of the changes and factors influencing them, and predict the changes process in the future to prevent irreparable damages to the environment. The purpose of this study was to analyze the land use changes in Gilan Province during a 20-year period (1996-2016) and predict changes for the next 30 years based on the integration of the artificial neural network of multilayer perceptron and Markov chain model using the Land-change Modeler.
Material and methods:
Landsat 5 and 8 (TM) and (OLI-TIRS) satellite images were used for the years 1996, 2006 and 2016. Land cover maps were prepared for five classes in the forest, grass, agriculture, water, and residential resources using the Maximum Likelihood method. Land use changes and then modeling the transmission potential were explored using multilayer perceptron algorithm of artificial neural network using 13 independent variables and obtained 7 sub-models for modeling land use change for 2016 and then using Markov chain method, land use map for the year 2016 was predicted with a coefficient of Kappa 0.98. Finally, the land use pattern of Gilan Province was simulated for 2046.
Results and discussion:
The results obtained from the analyses of land use changes in the first period (1996-2006) indicated that residential land use with the 7702.72 hectares increased the most among other users. In these changes, agricultural land use had the largest share, where 7663 hectares of this land turned into residential areas. In the second period (2006-2016), residential land use, as in the previous period, with the annual change of 633.7 hectares, had the most significant change in this period. In the whole study period from 1996 to 2016, the residential land reached from 12157.57 hectares in 1996 to 26197.59 hectares in 2016, which agricultural lands had the largest share in the conversion of the built-up areas.
The process of land use change suggests that this trend has begun from the past and will continue in the future. So, the results of the detection of changes from the predicting land use for the next 30 years would indicate an increase in residential use and a decrease in the area of agricultural lands, forests, and grasslands. According to these results, timely and accurate evaluation of these changes lead to better decision making and planning.


  1. Azizi Ghalati, S., Rangzan, K., Taghizadeh., A and Ahmadi, S.H.,2015. LCM Logistic regression modeling of land-use changes in Kouhmare Sorkhi, Fars province. Iranian Journal of Forest and Poplar Research.22(4),585-596[In Persian with English abstract]
  2. Bakr, N., Wendorf, D. C., Bahnassy, M. H, Marei, S. M., and El-Badawi, M.M., 2010.Monitoring land cover changes in a newly reclaimed area of Egypt using Multitemporal Landsat data. Applied Geography, 30(4), 592-605.
  3. Bao, G. Y., Huang, H., Gao, Y. N., and Wang, D. B.,2017. Study on driving mechanisms of land use change in the coastal area of Jiangsu, China. Journal of Coastal Research, 79(sp1), 104-108.
  4. Dadashpoor, H., Azizi, P., & Moghadasi, M. (2019). Analyzing spatial patterns, driving forces and predicting future growth scenarios for supporting sustainable urban growth: Evidence from Tabriz metropolitan area, Iran. Sustainable Cities and Society, 47, 101502.
  5. Dadashpoor, H., Azizi, P., & Moghadasi, M. (2019). Land use change, urbanization, and change in landscape pattern in a metropolitan area. Science of The Total Environment, 655, 707-719.
  6. Dadashpoor, H., Nateghi, M., 2017. Simulating spatial patterns of urban growth using GIS-based Sleuth model: A case study of the Eastern corridor of Tehran metropolitan region, Iran, Environment, Development and Sustainability, 19(2), 527-547.
  7. Dadashpoor, H. and Salarian, F., 2018. Urban sprawl on natural lands: analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran. Environment, Development, and Sustainability, 1-22.
  8. Dadashpoor, H., Kheirodin R., Yaghobkhani M., Chamani B.,2015. Modeling Tehran land use changes by using The Moland model. Journal of Regional Planning .4(16), 49- 64 [In Persian with English abstract]
  9. Eastman, J. R. 2015. TerrSet Tutorial. Clark Labs, Clark University: Worcester, MA, United States
  10. Falahatkar, S., Hosseini, S.M ., Salman Mahiny, A.R and Ayoubi, S ., 2016. Prediction of land use/ cover change by using LCM model. Environmental Researches. 7(13),163-174 [In Persian with English abstract]
  11. Gholamalifard, M ., Mirzayi, M and Joorabian Shooshtari, S.H., 2014. Land use change modeling using artificial neural network and Markov chain (Case study: Middle Coastal of Bushehr Province). Journal of RS and GIS for Natural Resources.5(1), 61-74[In Persian with English abstract]
  12. Halmy, M. W. A., Gessler, P. E., Hicke, J. A., and Salem, B. B., 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63: 101-112.
  13. Han, H., Yang, C., and Song, J., 2015. Scenario simulation and the prediction of land use and land cover change in Beijing, China. Sustainability, 7(4) : 4260-4279
  14. Joorabian Shooshtari, S.H., 2012. Modeling land use change changes in the Neka watershed using LCM in Gis environment. MSc. Graduated. Thesis. College of Natural Resources, Tarbiat Modares University.Iran.
  15. Khoi, D. D., and Murayama, Y. (2010). Forecasting areas vulnerable to forest conversion in the Tam Dao National Park Region, Vietnam. Remote sensing, 2(5): 1249-1272
  16. Lambin, E. F., and Geist, H. J., 2006. Land-use and land-cover change: local processes and global impacts. Springer Science & Business Media.
  17. Mishra, V. N., Rai, P. K., and Mohan, K., 2014. Prediction of land use changes based on land change modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute" Jovan Cvijic", SASA, 64(1): 111-127.
  18. Mousavi Malek. A., Hellathi Nasserian, H., Vaezipour., H.A and Asadi., R., 2013. Assessing the land use of Chabahar Bay coast with a sustainable development approach in Rs/GIS environment. In Proceedings The first national gathering for the development of coastal waters and the Islamic Republic of Iran's naval authority, 16th-18th February, Iran. p.8.
  19. Shi, L., Liu, F., Zhang, Z., Zhao, X., Liu, B., Xu, J and Hu, S., 2015. Spatial differences of coastal urban expansion in China from the 1970s to 2013. Chinese geographical science, 25(4):389-403.
  20. Stephenne, N., and Lambin, E. F. 2004. Scenarios of land-use change in Sudano-Sahelian countries of Africa to better understand driving forces. GeoJournal, 61(4): 365-379.
  21. Taheri, M ., Gholamalifard, M ., Riahi Bakhtiari, A and Rahimoghli, S.H.,2014. Land Cover Changes Modeling of Tabriz Township Using Artificial Neural Network and Markov Chain. Physical Geography Research Quarterly.45(4),87-121[In Persian with English abstract]
  22. Tope-Ajayi, O. O., Adedeji, O. H., Adeofun, C. O., and Awokola, S. O. 2013. Land Use Change Assessment, Prediction Using Remote Sensing, and GIS Aided Markov Chain Modelling at Eleyele Wetland Area, Nigeria. Journal of Settlements and Spatial Planning, 7(1): 51-63
  23. Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., and Mastura, S. S. 2002. Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30(3): 391-405
  24. Yousefi, M., and Ashrafi, A. 2016. Urban growth modeling in Bojnurd by using remote sensing data (Based on neural network and Markov modeling changes of land). Journal of Regional Planning .6(21),179-192[In Persian with English abstract].