1 Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran

2 Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran


The change of land use/land cover has been known as an imperative force in environmental alteration, especially in arid and semi-arid areas. This research was mainly aimed to assess the validity of two major types of land change modeling techniques via a three dimensional approach in Birjand urban watershed located in an arid climatic region of Iran. Thus, a Markovian approach based on two suitability and transition potential mappers, i.e. fuzzy analytic hierarchy process and artificial neural network-multi layer perceptron was used to simulate land use map. Validation metrics, quantity disagreement, allocation disagreement and figure of merit in a three-dimensional space were used to perform model validation. Utilizing the fuzzy-analytic hierarchy processsimulation of total landscape in the target point 2015, quantity error, the figure of merit and allocation error were 2%, 18.5% and 8%, respectively. However, Artificial neural network-multi layer perceptron simulation led to a marginal improvement in figure of merit, i.e. 3.25%.

Graphical Abstract

Performance comparison of land change modeling techniques for land use projection of arid watersheds


  • Land use alterations were mostly harmonized with the expansion of urban patches which resulted in the acreage reduction of rangelands surrounding the city of Birjand in Iran
  • Using disagreement components in a three dimensional validation approach instead of Kappa coefficient could be a better guide for validating and evaluating model errors
  • According to the validation indices, Markov simulation based on MLP approach showed a marginally higher robustness for LUCC simulation, as compared with the Fuzzy-AHP transition potential mapper.


Main Subjects

Adhikari, S.; Southworth, J., (2012). Simulating forest cover changes of Bannerghatta National Park based on a CA-Markov model: a remote sensing approach. Remote Sens. 4(10): 3215-3243 (29 pages).

Agarwal, C.; Green, G.M.; Grove, J.M.; Evans, T.P.; Schweik, C.M., (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice. Newton Square, PA: US Department of Agriculture, Forest Service, Northeastern Research Station (67 pages).

Ahmed, B.; Ahmed, R., (2012). Modeling urban land cover growth dynamics using multi‑temporal satellite images: A case study of Dhaka, Bangladesh. Int. J. Geo. Inf. 1(1): 3-31 (29 pages).

Alizadeh, M.; Ngah, I.; Shahabi, H.; Alizade, E., (2013). Evaluating AHP and WLC methods in site selection of landfill (Case study: Amol, North of Iran). J. Basic Appl. Sci. Res. 3(5): 83-88 (6 pages).

Araya, Y.H.; Cabral, P., (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sens. 2(6): 1549-1563 (15 pages).

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. Int. J. Appl. Earth. Obs & Geo-Inf. 21: 265-275 (11 pages).

Batty, M.; Couclelis, H.; Eichen, M., (1997). Urban systems as cellular automata. Environ. Plann. B. 24(2): 159-164 (6 pages).

Camacho Olmedo, M.T.; Paegelow, M.; Mas, J.F., (2013). Interest in intermediate soft-classified maps in land change model validation: suitability versus transition potential. Int. J. Geogr. Inf. Sci. 27(12): 2343-2361 (19 pages).

Chavez Jr, P.S., (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Rem. Sens. Environ. 24(3): 459-479 (21 pages).

Chavez, P.; Sides, S.C.; Anderson, J.A., (1991). Comparison of three different methods to merge multiresolution and multispectral data- Landsat TM and SPOT panchromatic. Photogramm. Eng. Rem. Sens. 57(3): 295-303 (9 pages).

Costanza, R.; Ruth, M., (1998). Using dynamic modeling to scope environmental problems and build consensus. Environ. Manage. 22(2): 183-195 (13 pages).

Couclelis, H., (1985). Cellular worlds: a framework for modeling micro—macro dynamics. Environ. Plann. A. 17(5): 585-596 (12 pages).

Coyle, G., (2004). The analytic hierarchy process (AHP). Upper Saddle River. NJ. USA. Pearson Education Open Access. Material., 1-20 (20 pages).

Donyavi, R.; Ahmadizadeh, S.; Memarian, H., (2014). Frontage features environmental analysis in evaluating the future development of the city of Birjand. In the 2nd National Conference on Environmental Research, Hamedan, Iran (In Persian); (26 pages).

Eastman, J.R., (2009). Idrisi taiga manual. Clark Lab. Clark University. Worcester, USA (333 Pages).

Eastman, J.R., (2014). Idrisi TerrSet 18.00. Clark University, Worcester, MA, USA (392 Pages).

Engelen, G.; Geertman, S.; Smits, P.; Wessels, C.; (1999). Dynamic GIS and strategic physical planning support: a practical application. In Geographical information and planning. Springer, Berlin, Heidelberg. 87-111 (25 pages).

Geographical Sciences Committee, (2014). Advancing land change modeling: opportunities and research requirements. National Academies Press (152 pages).

Graupe, D., (2013). Principles of artificial neural networks. 3rd Ed, Advanced Series in Circuits and Systems, World Scientific Publishing Co (364 pages).

Guillem, E.E.; Murray-Rust, D.; Robinson, D.T.; Barnes, A.; Rounsevell, M.D.A., (2015). Modelling farmer decision-making to anticipate tradeoffs between provisioning ecosystem services and biodiversity. Agr. Syst. 137: 12-23 (12 pages).

Hamilton, S.H.; ElSawah, S.; Guillaume, J.H.; Jakeman, A.J.; Pierce, S.A.; (2015). Integrated assessment and modelling: overview and synthesis of salient dimensions. Environ. Model. Softw. 64: 215-229 (15 pages).

Heistermann, M.; Müller, C.; Ronneberger, K., (2006). Land in sight? Achievements, deficits and potentials of continental to global scale land-use modeling. Agr. Ecosyst. Environ. 114(2): 141-158 (18 pages).

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. Appl. Geogr. 29(3): 435-447 (13 pages).

Kim, M.; Gilley, J.E., (2008). Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas. Comput. Electron. Agric. 64(2): 268-275 (8 pages).

Kuo, J.T.; Hsieh, M.H.; Lung, W.S.; She, N., (2007). Using artificial neural network for reservoir eutrophication prediction. Ecol. Model. 200(1): 171-177 (7 pages).

Liebetrau, A.M., (1983). Association between variables. In: Measures of Association. SAGE Publication, California, USA (152 pages).

Luo, G.; Yin, C.; Chen, X.; Xu, W.; Lu, L., (2010). Combining system dynamic model and CLUE-S model to improve land use scenario analyses at regional scale: A case study of Sangong watershed in Xinjiang, China. Ecol. Complex., 7(2): 198-207 (10 pages).

Markov, A.A., (1971). Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain, The Notes of the Imperial Academy of Sciences of St. Petersburg, VIII Series, Physio-Mathematical College XXII (152 pages).

Mas, J.F.; Kolb, M.; Paegelow, M.; Olmedo, M.T.C.; Houet, T., (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environ. Model. Softw., 51: 94-111 (18 pages).

Mas, J.F.; Paegelow, M.; De Jong, B.; Masera, O.; Guerrero, G.; Follador, M.; Olguin, M.; Diaz, J.R.; Castillo, M.A; Garcia, T., (2007). Modelling tropical deforestation: a comparison of approaches. In 32rd symposium on remote sensing of environment. (3 pages).

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. J. Geogr. Inf. Syst., 4(6): 542-554 (13 pages).

Memarian, H.; Balasundram, S.K., (2012). Comparison between multi-layer perceptron and radial basis function networks for sediment load estimation in a tropical watershed. J. Water. Res. Prot. 4(10): 870-876 (7 pages).

Memarian, H.; Balasundram, S.K.; Talib, J.B.; Teh Boon Sung, C.; Mohd Sood, A.; Abbaspour, K.C., (2013a). KINEROS2 application for land use/cover change impact analysis at the Hulu Langat Basin, Malaysia. Water Environ. J. 27(4): 549-560 (12 pages).

Memarian, H.; Balasundram, S.K.; Khosla, R., (2013b). Comparison between pixel-and object-based image classification of a tropical landscape using Système Pour l’Observation de la Terre-5 imagery. J. Appl. Remote. Sens. 7(1): 073512 (14 pages).

Memarian, H.; Balasundram, S.K.; Tajbakhsh, M., (2013c). An expert integrative approach for sediment load simulation in a tropical watershed. J. Integr. Environ. Sci. 10(3-4): 161-178 (18 pages).

Memarian, H.; Balasundram, S.K.; Abbaspour, K.C.; Talib, J.B.; Boon Sung, C.T.; Sood, A.M., (2014). SWAT-based hydrological modelling of tropical land-use scenarios. Hyd. Sci. J. 59(10): 1808-1829 (22 pages).

Memarian, H.; Balasundram, S.K.; Abbaspour, K.C.; Talib, J.B.; Sung, C.T.B.; Sood, A.M., (2015). Integration of analytic hierarchy process and weighted goal programming for land use optimization at the watershed scale. Turkish J. Eng. Environ. Sci. 38(2): 139-158 (20 pages).

Miller, S.L.; Childers, D., (2004). Markov Processes, In: Probability and Random Processes. Academic Press, Burlington. 323-367 (45 pages).

Mishra, V.N.; Rai, P.K.; 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 (17 pages).

Omrani, H.; Charif, O.; Gerber, P.; Bódis, K.; Basse, R.M., (2012). Simulation of land use changes using cellular automata and artificial neural network. Technical report. CEPS/INSTEAD working paper (24 pages).

Paegelow, M.; Olmedo, M.T.C., (2005). Possibilities and limits of prospective GIS land cover modelling—a compared case study: Garrotxes (France) and Alta Alpujarra Granadina (Spain). Int. J. Geogr. Inf. Sci. 19(6): 697-722 (26 pages).

Parker, D.C.; Manson, S.M.; Janssen, M.A.; Hoffmann, M.J.; Deadman, P., (2003). Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann. Assoc. Am. Geogr. 93(2): 314-337 (24 pages).

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 (13 pages).

Poelmans, L.; Van Rompaey, A., (2010). Complexity and performance of urban expansion models. Computers, Environ. Urban Syst. 34(1): 17-27 (11 pages).

Pontius Jr, R.G.; Chen, H., (2006). Land change modeling with GEOMOD. Clark University (11 pages).

Pontius Jr, R.G.; Millones, M., (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote. Sens. 32(15): 4407-4429 (23 pages).

Pontius Jr, R.G.; Neeti, N., (2010). Uncertainty in the difference between maps of future land change scenarios. Sustain. Sci. 5(1): 39-50 (12 pages).

Pontius Jr, R.G.; Boersma, W.; Castella, J.C.; Clarke, K.; de Nijs, T.; Dietzel, C.; Duan, Z.; Fotsing, E.; Goldstein, N.; Kok, K.; Koomen, E., (2008). Comparing the input, output, and validation maps for several models of land change. Ann. Regional. Sci. 42(1): 11-37 (27 pages).

Pontius Jr, R.G.; Peethambaram, S.; Castella, J.C., (2011). Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam. Ann. Assoc. Am. Geogr. 101(1): 45-62 (18 pages).

Pontius, G.R.; Malanson, J., (2005). Comparison of the structure and accuracy of two land change models. Int. J. Geogr. Inf. Sci. 19(2): 243-265 (23 pages).

Pontius, R.G.; Schneider, L.C., (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agr. Ecosyst. Environ. 85(1): 239-248 (10 pages).

Principe, J.C.; Lefebvre, W.C.; Lynn, G.; Fancourt, C.; Wooten, D., (2015). Neuro-solutions-documentation, the manual and on-line help. Version 5.05. NeuroDimension (1198 pages).

Qiang, Y.; Lam, N.S., (2015). Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata. Environ. Monit. Assess. 187(3): 1-16 (16 pages).

Rocchini, D.; Foody, G.M.; Nagendra, H.; Ricotta, C.; Anand, M.; He, K.S.; Amici, V.; Kleinschmit, B.; Förster, M.; Schmidtlein, S; Feilhauer, H., (2013). Uncertainty in ecosystem mapping by remote sensing. Comput. Geosci., 50: 128-135 (8 pages).

Rounsevell, M.D.; Pedroli, B.; Erb, K.H.; Gramberger, M.; Busck, A.G.; Haberl, H.; Kristensen, S.; Kuemmerle, T.; Lavorel, S.; Lindner, M; Lotze-Campen, H., (2012). Challenges for land system science. Land Use Policy. 29(4): 899-910 (12 pages).

Roy, H.G.; Fox, D.M.; Emsellem, K., (2014). Predicting land cover change in a Mediterranean catchment at different time scales. In International Conference on Computational Science and Its Applications, Springer International Publishing. 315-330 (16 pages).

Rumelhart, E.; McClelland, J.L.; the PDP Research Group., (1986). Parallel distributed processing: explorations in the microstructure of cognition, Vol. 1: Foundations, MIT Press, Cambridge (567 pages).

Saaty, T.L., (2008). Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1): 83-98 (16 pages).

Sadidy, J.; Firouzabadi, P.Z.; Entezari, A., (2009). The use of Radarsat and Landsat Image Fusion algorithms and different supervised classification methods to improve land use map accuracy: Case study: Sari Plain, Iran. Department of Geography, Tarbiat Moallem Sabzevar University (7 pages).

Schreinemachers, P.; Berger, T., (2011). An agent-based simulation model of human–environment interactions in agricultural systems. Environ. Model. Softw. 26(7): 845-859 (15 pages).

Tajbakhsh, M.; Memarian, H.; Shahrokhi, Y., (2016). Analyzing and modeling urban sprawl and land use changes in a developing city using a CA-Markovian approach. Global J. Environ. Sci. Manage. 2(4): 397-410 (14 pages).

Tape, T.G., (2006). Interpreting diagnostic tests. University of Nebraska Medical Center (15 pages).

Tewolde, M.G.; Cabral, P., (2011). Urban sprawl analysis and modeling in Asmara, Eritrea. Remote Sens. 3(10): 2148-2165 (18 pages).

Thomas, H.; Laurence, H.M., (2006). Modeling and projecting land-use and land-cover changes with a cellular automaton in considering landscape trajectories: An improvement for simulation of plausible future states. EARSeL eProc. 5: 63-76 (14 pages).

Torrens, P.M., (2006). Geosimulation and its application to urban growth modeling. In complex artificial environments, Springer Berlin Heidelberg, 119-136 (18 pages).

Van Vliet, J.; Hagen-Zanker, A.; Engelen, G.; Hurkens, J.; Vanhout, R.; Uljee, I., (2009). Map comparison kit 3: User Manual. Maastricht: Research Institute for Knowledge Systems (88 pages).

Velayati, S.; Tavasoli, S., (1993). Water resources and issues in Khorasan province. Astan Qods Razavi, Mashhad. (In Persian); (278 pages).

Verburg, P.H.; Schot, P.P.; Dijst, M.J.; Veldkamp, A., (2004). Land use change modelling: current practice and research priorities. Geo. J. 61(4): 309-324 (16 pages).

Verburg, P.H.; Soepboer, W.; Veldkamp, A.; Limpiada, R.; Espaldon, V.; Mastura, S.S., (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ. Manage. 30(3): 391-405 (15 pages).

Zadeh, L.A., (1965). Fuzzy sets. Inf. Control. 8(3): 338-353 (16 pages).

Zarei, A.; Mirsayar, S.M.; Vosoogh, A., (2010). Evaluation of environmental capability of arid and semi-arid regions using geographic information system (Case study: Birjand watershed). J. Environ. Stud. 52: 35-42 (In Persian); (8 pages).

Letters to Editor

GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.