Land use Changes Modelling Using Logistic Regression and Markov Chain in the Haraz Watershed

Document Type : Research Paper



Assessment of land use spatio-temporal changes presents the perfect data for managers to elaborate plans. Land use change modeling is one of the methods used by planers to manage land use changes. The present study aims to predict land use changes using logistic regression and Markov chain model in Haraz watershed. Land use maps of the study area were prepared from Landsat images (L5-TM-1988, L7-ETM+ -2000 and -L8-OLI-2013). For this purpose, image classification method and maximum likelihood algorithm was used in ENVI 4.8 software. Then transition potential modeling was performed using Land Change Modeler (LCM) and Logistic Regression (LR). In order to prediction of land use for 2025, maps of the calibration periods of 1988-2000, 2000-2013 and 1988-2013 using a Markov chain model and hard prediction were used. The results of the calibration periods using the GEOMOD method and its parameters (N(n), N(m), H(m), M(m), K(m), P(m) and P(p)) and kappa coefficients showed that period of 1988-2013 with highest accuracy was selected to predict 2025 land use map. The results of the land use changes showed that over the period 1988-2013, the rate of decreasing in forest, grassland and irrigated land was 4.20, 5.09 and 0.63 percent, respectively. Also during the period residential areas, orchard and bare land, increased 1.28, 2.20 and 6.62 percent, respectively. Most of the changes in this period was transition of forest and rangeland to orchards, residential area and bare land with 8836.4, 5165.1 and 426598.4 ha, respectively. Also the results of land use modeling for 2025 revealed that the area of forest and rangelands will be decrease 2978.18 and 6367.41 ha, respectively and irrigated land, residential area, orchards and bare land will be increase 391.86, 29.38, 1453.42 and 7214.94 ha, respectively.


Alansi, A.W., M.S.M., Amin, G., Abdul Halim, H.Z.M., Shafri, A.M., Thamer, A.R.M., Waleed, W., Aimrun, and M.H., Ezrin, 2009. The Effect of Development and Land Use Change on Rainfall-Runoff and Runoff-Sediment Relationships under Humid Tropical Condition: Case Study of Bernam Watershed Malaysia.  European Journal of Scientific Research, 31 (1): 88-105.
Babaei Aghdam, F., and H., Ebrahimzadeh, 2012. Land use modeling change and of agricultural land and Bareland to urban area in the Ardabil city using CLUE_S model. Journal of geghraphy and Development, 26: 21-34. (In Persian)
Darabi, H., K., Shahedi, K., Solaimani, and M., Miryaghoubzadeh, 2014. Prioritization of subwatersheds based on flooding conditions using hydrological model, multivariate analysis and remote sensing technique. Water and Environment Journal28 (3): 382-392.
Donner, A., and N., Klar, 1996. The statistical analysis of kappa statistics in multiple samples. Journal of Clinical Epidemiology, 49: 1053-1058.
Dunne, T., and L.B., Leopold, 1978.  Water in environmental planning.  W. H. Freeman and Co New York Pub, 818 pp.  
FAO. 1981. State of the world’s forest.  Food and Agriculture Organization of the United Nations.  Rome, 144 pp.
Fatemi, S.B., and Y., Rezaei, 2010. Principles of remote sensing. Tehran. T: Azadeh, Tehran. 257 pages. (In Persian)
Foody, G.M., 2000. Mapping Land Cover from Remotely Sensed Data with a Softened Ghats (India), Sensors, 8: 6132-6153.
Gholamali fard, M., Sh., Shooshtari, S.H., Hosseini Kanooj, and M., Mirzaei, 2013. Modeling of land use changes using GIS and LCM in the coastal province, Mohit shenasi, 4: 109-124. (In Persian)
Joorabian, Sh., E., Esmaeili Sari, M., Hosseini, and M., Gholamali fard, 2013. Logistic Regression and Markov Chain to predict the land use change in the East of Mazandaran. Journal of Natural Resources – Environment, 66: 351-361 (in Persian).
Kamyab, H., A., Salman Mahiny, M., Hosseini, and M., Gholamalifard, 2013. Adopt a data-driven approach using logistic regression to model the urban growth in Gorgan, Journal of Environmental Studies, 36: 89-96. (In Persian)
Kim, O.S., 2010. An Assessment of Deforestation Models for Reducing Emissions from Deforestation and Forest Degradation (REDD), Transactions in GIS, 14: 631-654.
Lambin, E.F., 1997. Modelling and monitoring land-cover change processes in tropical regions, Progress in Physical Geography, 21: 375–393.
Li, X., and A.,Yeh, 2002.  Neural-network-based cellular automata for simulating multiple land use changes using GIS.  International Journal of Geographical Information Science, 16 (4): 323-343.
Lourdesa, L., Z., Karinac, L., Pedrob, M., Héctora, and M., Néstorc, 2011. A dynamic simulation model of land cover in the Dulce Creek Basin, ArgentinaA dynamic simulation model of land cover in the Dulce Creek Basin, Argentina, Procedia Environmental Sciences, 7: 194–199.
Lu, D., P., Mausel, E., Brondizio, and E., Moran, 2004. Change detection techniques, mapping of the Twin Cities (Minnesota) Metropolitan Area with Multi-seasonal Landsat of plausible future states, EARSeL proceedings, 5 (1): 63-76.
Olaniyi, A.O., A.M., Abdullah, M.F., Ramli, and M.S., Alias, 2012. Assessment of drivers of coastal land use change in Malaysia. Journal of Ocean & Coastal Management, 67: 113-123.
Onate-vadiieso, F., and J.B., sendra, 2010. Aplication of GIS and Remote sensing technequs in generation of landuse scenario for hidrological modeling. Journal of Hydrology, 395 (4): 256-264.
Park, S.J., N., Giesen, and P., Vlek, 2005. Optimal Spatial Scale for Land Use Change Modelling: A Case Study in a Savanna Landscape in Northern Ghana.  Journal of the Korean Geographical Society, 40 (2): 221-241.
Parker, D.C., S.M., Manson, and M.J., Deadman, 2003. Multi agent systems for the simulation of land use and land cover change: a Review, Annals of the Association of American Geographers, 43: 314-337.
Paudel, S., and F., Yuan, 2012. Assessing landscape changes and dynamics using patch analysis and GIS modeling. International Journal of Applied Earth Observation and Geoinformation, 16: 66–76.
Perez-Vega, A., J., Mas, and A., Ligmann-Zielinska, 2012. Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modelling & Software, 29 (1): 11-23.
Shattaei, Sh., and R., Bagheri, 2010. Decreasin of forest area modeling using the logistic regression (Case Study: Chehl-chay Watershed, Golestan province). Journal of forest, 3: 243-252. (In Persian)
Wanga, S.Q., X.Q., Zhenga, and X.B., Zangb, 2012. Accuracy assessments of land use change simulation based on Markov-cellular automata model. ­Procedia Environmental Sciences, 13: 1238-1245.
Wu, Q., LiR.S., WangJ., PaulussenY., He, and L. Wa, 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78 (4): 322-333.
Yuan, F., M.E., Bauer, N.J., Heinert, and G.R., Holden, 2005. Multi-level Land Cover Mapping of the Twin Cities (Minnesota) Metropolitan Area with Multi-seasonal Landsat TM/ETM+ Data. Geocarto International, 20 (2): 5–13.
Zare-Garizi, A., Sh., Vahed bordi, A., Sadodin, and S.E., Mahini, 2011. Using logistic regression in the spatial patterns modeling of vegetation change. Fazaye Geghraphiaei, 37: 56-68. (In Persian)