Predicting and detecting the trend of temporal and spatial changes of land use using land change modeler

Document Type : Research Paper


1 Department of Geography, University of Jiroft, Kerman, Iran.

2 Department of ecological engineering, Faculty of Natural Resources, University of Jiroft, Kerman, Iran.


The purpose of this study is to investigate land use changes in the past and predict future land use using land change modeler in Halil River watershed. The detection of land use changes was performed using Landsat satellite images (L5-TM-1991, L7- ETM+-2003 and L8-OLI-2020). Transition potential modeling was done using MLP neural network method and eight variables including altitude, slope, aspect, distance to road, distance to river, distance to agricultural lands, distance to urban and Normalized Difference Vegetation Index (NDVI). Finally, the Markov chain was used to predict future land use changes. Investigating the calibration periods using kappa statistics showed that the period of 1991-2020 had the highest accuracy to predict land use for 2041. The results of land use changes indicated that during the calibration period, among the six categories namely rangeland, agricultural land, residential land, barren land, rock and orchard, the highest increase and the highest decrease in area was related to agricultural lands and rangelands by 293.7 and 382.6 km2, respectively. Also, the area of barren lands, orchard and residential lands has increased and rocky lands have remained unchanged. The degradation of rangelands has been more in line with the conversion of these lands into agricultural, orchard and residential lands. Also, the prediction of future land use map (2041) using land change modeler showed that , the area of rangelands will decrease by 201.1 km2 and the area of agricultural lands, residential lands, orchards and barren lands will increase by 158.01, 22.38, 20.2 and 0.53 km2, respectively.


  • Afifi, M. E. (2020). Modeling land use changes using Markov chain model and LCM model. Journal of Applied researches in Geographical Sciences, 20(56),141-158.
  • Ahmadlou, M. and Delavar, M. R. (2015). Multiple Land Use Change Modeling Using Multivariate Adaptive Regression Spline and Geospatial Information System. Journal of Geomatics Science and Technology, 5(2), 131-146.
  • Akbari, E., Zangane Asadi, M. A. and Taghavi, B. (2016). Change detection land use and land cover regional Neyshabour using Different methods of statistical training theory Document Type: Research Paper. Geographical Planning of Space Quarterly Journal, 6(20), 35-50.
  • Anand, J., Gosain, A. K. and Khosa, R. (2018). Prediction of land use changes based on Land Change Modeler and attribution of changes in the water balance of Ganga basin to land use change using the SWAT model. Science of the Total Environment, 644, 503-519.
  • Azimi Sardari, M. R., Bazrafshan, O., Panagopoulos, T. and Sardooi, E. R. (2019). Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed. Sustainability, 11(12), 3353.
  • Azizi Ghalati, S., Rangzan, K., Taghizadeh, A., Ahmadi, Sh. (2014). LCM Logistic regression modelling of land-use changes in Kouhmare Sorkhi, Fars Province. Iranian Journal of Forest and Poplar Research, 22(4), 585-596.
  • Eastman, J. R. (2006). IDRISI Andes guide to GIS and image processing. Clark University, Worcester, 328.
  • Eastman, J. R. (2016). IDRISI Terrset Manual. Clark Labs, Clark University, Worcester, MA, , Provided as a PDF with the IDRISI Terrset software package.
  • Eastman, J. R., Van Fossen, M. E. and Solarzano, L. A. (2012). Transition potential modeling for land cover change. In: Maguire, D., Good Child, M., Batty, M. (Eds.), GIS, Spatial Analysis and Modeling. ESRI Press, Redlands, California.
  • Ferchichi, A., Boulila, W. and Farah, I. R. (2018). Reducing uncertainties in land cover change models using sensitivity analysis. Knowledge and Information Systems, 55(3), 719-740.
  • Ghabaei Sough, M., Mosaedi, A., Hesam, M., Hezarjaribi, A. (2010). Evaluation Effect of Input Parameters Preprocessing in Artificial Neural Networks (Anns) by Using Stepwise Regression and Gamma Test Techniques for Fast Estimation of Daily Evapotranspiration. Journal of Water and Soil, 24(3), 610-624.
  • Gholamalifard, M., Joorabian Shooshtari, Sh., Hosseini Kahnuj, S. H., Mirzaei, M. (2013). Land Cover Change Modeling of Coastal Areas of Mazandaran Province Using LCM in a GIS Environment. Journal of environmental studies, 38 (4), 109-124
  • Gómez, C., White, J. C. and Wulder, M. A. (2011). Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation. Science of the total environment. 115(7), 1665–1679
  • Gontier, M., Mörtberg, U. and Balfors, B. (2009). Comparing GIS-based habitat models for applications in EIA and SEA. Environmental Impact Assessment Review, 30(1), 8-18.
  • Gross, J. E., Nemani, R. R., Turner, W. and Melton, F. (2006). Remote sensing for the national parks. Park Science, 24(1), 30-36.
  • Gupta, R. and Sharma, L. K. (2020). Efficacy of Spatial Land Change Modeler as a forecasting indicator for anthropogenic change dynamics over five decades: A case study of Shoolpaneshwar Wildlife Sanctuary, Gujarat, India. Ecological Indicators, 112, 106171.
  • Hathout, S. (2002). The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. Journal of Environmental Management, (66), 229-238.
  • Hayatzadeh, M., Ekhtesasi, M., Malekinezhad. H., Fathzadeh, A. and Azimzadeh, H. (2016). Simulation of Future Land Use Map of the Catchment Area, with the Integration of Cellular Automata and Markov Chain Models Based on Selection of the Best Classification Algorithm (A Case Study of Fakhrabad Basin of Mehriz, Yazd). Environmental Erosion Research, 6(4), 1-22.
  • Ildermi, A., Nouri, H., Naderi, M., Aghabeigi, S., Zaini Wand, H. (2017). Forecasting Land Use Change Using Markov Chain Model and CA Markov (Case Study: Green Watershed). Watershed Management Research, 8 (16), 232-240.
  • Leta, M. K., Demissie, T. A. and Tränckner, J. (2021). Modeling and Prediction of Land Use Land Cover Change Dynamics Based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia. Sustainability, 13(7), 3740.
  • Linkie, M., Smith, R.J. and Leader-Williams, N. (2004). Mapping and predicting deforestation patterns in the lowlands of Sumatra. Biodiversity and Conservation, 13 (10), 1809-1818.
  • Lu, D., Mausel, P., Brondizio, E. and Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365-2401.
  • Mas, J. F., Kolb, M., Paegelow, M., Olmedo, M. T. C. and Houet, T. (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, 51, 94-111.
  • McConnell, W. J., Sweeney, S. P. and Mulley, B. (2004). Physical and social access to land: spatio-temporal patterns of agricultural expansion in Madagascar. Agriculture, Ecosystems & Environment, 101(2-3), 171-184.
  • Mertens, B. and Lambin, E. F. (1997). Spatial modelling of deforestation in southern Cameroon: spatial disaggregation of diverse deforestation processes. Applied Geography, 17(2), 143-162.
  • 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.
  • Mohammad Yousefi, M., Pajoohesh, M. and Honarbakhsh, A. (2020). Modeling Trends Land Use Changes Local by Using LCM Model Based on Artificial Neural Networks and Markov Chain Analysis (Case Study: BeheshtAbad Watershed). Journal of Watershed Management Research, 11(21), 129-142.
  • Mohammadyary, F., Purkhabbaz, H., Aghdar, H. and Tavakoly, M. (2019). Predicted trends in land use city Behbahan years 2014 to 2028 Using LCM model. Geographic Space, 19 (65), 37-56.
  • Mufubi, A., S. Yudi and H. Effendi. 2016. Land use/land cover change detection in an urban watershed: a case study of upper Citarum Watershed, West Java Province, Indonesia. Procedia Environmental Sciences, 33(2016): 654-660
  • Parma, R., Maleknia, R., Shataee, Sh. and Naghavi, H. (2017). Land Cover Change Modeling based on Artificial Neural Networks and transmission potential method in LCM (Case Study: Forests Gilan-e Gharb, Kermanshah Province). Journal of town and country planning, 9(1), 129-151.
  • Pérez-Vega, A., Mas, J. F., Ligmann-Zielinska, A. (2012) Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environ Model Softw, 29(1), 11–23.
  • Rahnama, M., Ajza shokouhi M. and Ata. B. (2017). Detection of land use/land cover changes in Gonbade Kavus City using remote sensing. Scientific-Research Quarterly of Geographical Data (SEPEHR), 26(103), 148-160.
  • Salehi Sedeh, R., Sharifi, M. (2006). Application of neural networks in predicting river flow in Kardeh Paired Watershed. The 2nd Conference on Water Resources Management. Ferdowsi University of Mashhad, Mashhad, pp. 1-9.
  • Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10, 989-1003.
  • Singh, N. and Punia, M. (2018). Geospatial Approach for Land Use/Land Cover Change Prediction: A case study of Bhagirathi Basin, Uttarakhand, INDIA. cosp, 42, A3-1.
  • Sundara Kumar, K., Udaya Bhaskar, P. and Padmakumari, K. (2015). Application of land change modeler for prediction of future land use land cover (a case study: of Vijayawada city). International Conference on Science, Technology and Management, 2571-2581
  • Tso, B. and Mather, P. (2009). M. Classification Methods for Remotely Sensed Data, Chapter 2-3.
  • Václavík, T. and Rogan, J. (2009). Identifying trends in land use/land cover changes in the context of post-socialist transformation in central Europe. GIS Science and Remote Sensing, 49(1), 1-32.
  • Vafaei, S., Darvishsefat, A. A. and Pir Bavaghar, M. (2013). Monitoring and predicting land use changes using LCM module (Case study: Marivan region). Iranian Journal of Forest, 5(3), 323-336.
  • Wu, Q., Li, H. Q., Wang, R. S., Paulussen, J., He, Y., Wang, M., ... & Wang, Z. (2006). Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and urban planning, 78(4), 322-333.
Volume 74, Issue 3
December 2021
Pages 483-500
  • Receive Date: 23 July 2021
  • Revise Date: 25 October 2021
  • Accept Date: 17 October 2021
  • First Publish Date: 22 November 2021