Document Type: CASE STUDY


1 Department of Environmental Sciences, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

2 Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia

3 Department of Environmental Management, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia


Predicting land use change is an indispensable aspect in identifying the best development and management of land resources and their potential. This study used certified land-use maps of 1997, 2006, and 2015 combined with ancillary data such as road networks, water bodies and slopes, obtained from the Department of Agriculture and the Department of Surveying and Mapping in Malaysia, respectively. The prediction of future land use changes in the Selangor River basin in Malaysia was performed using the Cellular Automata Markov model. The transition probability matrices were computed using the land use conditions of the periods 1997-2006, 2006-2015, 1997-2015. The performance of the model was very good in its overall ability to simulate the actual land use map of 2015, with the index values of 0.92% and 0.97%, respectively for Kappa for no information and Kappa for grid-cell level location which indicated the reliability of the model to successfully simulate land use changes in 2024 and 2033. Based on the expected results, the future urban area will grow faster (33%) over the next two decades, leading to a decline in forest area that is expected to lose 8% of its total space during these periods. Agricultural land will increase to 4%, while water bodies will change slightly increasing to 1%, and other areas of land use will likely become reservoirs of water, topsoil or new green spaces shrinking at 30%. Given the importance of knowledge of future land use in addressing the problems of uncontrolled development on environmental quality, this study could be valuable for land use planners of the river basin largely covered by natural forest. The study however, suggests future research to integrate geospatial techniques with biophysical and socio-economic factors in simulating land use trends.

Graphical Abstract


  • The integrated CA-Markov model was applied to simulate future land use change in the river basin;
  • Urban area within the basin is expected to increase more rapidly over the next two decades;
  • Forest land would be gradually impacted by rapid urbanization and development projects;
  • The study suggests that the control policy of urban expansion within the basin and its associated environmental risks be strengthen.


Main Subjects

Abdullah, S.A.; Nakagoshi, N., (2007). Forest fragmentation and its correlation to human land use change in the state of Selangor, peninsular Malaysia. For. Ecol. Manage., 241: 39–48 (10 pages). 

Aburas, M.M.; Ho, Y.M.; Ramli, M.F.; Ash’aari, Z.H., (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. Int. J. Appl. Earth Obs. Geoinf., 52: 380–389 (10 pages).

Aburas, M.M.; Ho, Y.M.; Ramli, M.F.; Ash’aari, Z.H., (2017). Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. Int. J. Appl. Earth Obs. Geoinf., 59: 65–78 (14 pages).

Alilou, H.; Moghaddam, N.A.; Keshtkar, H., (2018). A cost-effective and efficient framework to determine water quality monitoring network locations. Sci. Total Environ., 624: 283–293 (11 pages).

Al-sharif, A.A.A.; Pradhan, B., (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arab. J. Geosci. 7, 4291–4301 (11 pages).

Araya, Y.H.; Cabral, P., (2010). Remote Sensing Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal. Remote Sens., 2: 1549–1563 (15 pages).               

Camara, M.; Jamil, N.R.; Abdullah, A.F.B., (2019a). Impact of land uses on water quality in Malaysia: a review. Ecol. Process., 8:10 (10 pages).

Camara, M.; Jamil, N.R.; Abdullah, A.F.B.; Hashim, R.B., (2019b). Spatiotemporal assessment of water quality monitoring network in a tropical river. Environ. Monit. Assess., 191:729 (14 pages).

Chetan, A.; Glen, M.; Green, J.; Morgan, G., (2002). A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice. US Department of Agriculture, Forest Service, Northeastern Research Station (61 pages).

Costanza, R.; Ruth, M., (1998). Using Dynamic Modeling to Scope Environmental Problems and Build Consensus. Environ. Manage., 22: 183–195 (13 pages).

DOA, (2019). Land use map. Department of Agricultur, Malaysia (4 pages).

Fulazzaky, M.A.; Seong, T.W.; Masirin, M.I.M., (2010). Assessment of Water Quality Status for the Selangor River in Malaysia. Water Air Soil Pollut., 205: 63–77 (15 pages).

Helen, B., (2000). Analysis of Land Use Change: Theoretical and Modeling Approaches. Regional Research Institute, West Virginia University, (152 pages).

Kautz, R.; Stys, B.; Kawula, R., (2007). Agricultural and Natural Resource Sciences Florida vegetation 2003 and land use change between. Florida Sci., 70(1) 12–23 (12 Pages).

Keshtkar, H.; Voigt, W., (2016). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Model. Earth Syst. Environ., 2(10), (13 pages).

Kusin, F.M.; Muhammad, S.N.; Zahar, M.S.M.; Madzin, Z., (2016). Integrated River Basin Management: incorporating the use of abandoned mining pool and implication on water quality status. Desalin. Water Treat., 57: 29126–29136 (11 pages).

Liping, C.; Yujun, S.; Saeed, S., (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS One, 13: e0200493 (11 pages).

Ma, C.; Zhang, G.Y.; Zhang, X.C.; Zhao, Y.J.; Li, H.Y.; (2012). Application of Markov model in wetland change dynamics in Tianjin Coastal Area, China. Procedia Environ. Sci. 13, 252–262 (11 Pages).

Nurhidayu, S.; Faizalhakim, M.; Shafuan, A., (2016). Long-term sediment pattern of the Selangor River Basin, Malaysia impacted by land-use and climate changes. E-proceedings of the 36th IAHR World Congress, (4 pages).

Ong, D.J., (1991). Land use in the Selangor River: A study on the application and strategy for integrated river basin management, WWF Malaysia, Petaling Jaya (55 pages).

Panandiker, A.P.; Gude, S.; Venkatesh, B., (2019). Examining Temporal Change and Prediction of Future Land Use Using Geospatial Approach: A Case Study of Talpona River Watershed in Goa, India. J. Indian Assoc. Environ. Manage., 39: 25–29 (5 pages).

Pontius, R.G., (2000). Quantification error versus location error in comparison of categorical maps. Photogramm. Eng. Remote Sensing (6 pages).

Rimal, B.; Zhang, L.; Keshtkar, H., (2017), Monitoring and modelling of spatiotemporal urban expansion and land-use/land-cover change using integrated Markov chain cellular automata model. Int. J. Geo-Information 6: 288 (21 pages).

Rodrigues, V.; Estrany, J.; Ranzini, M., (2018). Effects of land use and seasonality on stream water quality in a small tropical catchment: The headwater of Córrego Água Limpa, São Paulo (Brazil). Sci. Total Environ., 622–623: 1553–1561 (9 pages).

Ronald, R.; Rindfuss, S.J.; Walsh, B. L.; Turner, I.I.; Jefferson, F., (2004). Developing a science of land change: Challenges and methodological issues. National Academy of Sciences of the United States of America, (6 pages).

Sakai, N.; Alsaad, Z.; Thuong, N.T., (2017). Source profiling of arsenic and heavy metals in the Selangor River basin and their maternal and cord blood levels in Selangor State, Malaysia. Chemosphere, 184: 857–865 (9 pages).

Sang, L.; Zhang, C.; Yang, J., (2011). Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Math Comput Model 54:938–943 (6 Pages).

Singh, S.K.; Laari, P.B.; Mustak, S., (2017). Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India Geocarto. Int., 1–21 (21 pages).

Subedi, P.; Subedi, K.; Thapa, B., (2013). Application of a Hybrid Cellular Automaton-Markov (CA-Markov) Model in Land-Use Change Prediction: A Case Study of Saddle Creek Drainage Basin, Florida Appl. Ecol. Environ. Sci., 1: 126–132 (7 pages).

Van den, B.F.; Gupta, J.; Hordijk, M., (2018). Megacities and rivers: Scalar mismatches between urban water management and river basin management. J. Hydrol., 573: 1067-1074 (8 pages)

Verburg, P.H.; Kok, K.; Pontius, R.G.; Veldkamp, A., (2012). Modeling Land-Use and Land-Cover Change. Springer Berlin Heidelberg, Berlin, Heidelberg. 117–135 (19 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: 309–324 (16 pages)

Weng, Q., (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J. Environ. Manage., 64:273–284 (12 Pages).

Yu, D.; Shi, P.; Liu, Y.; Xun, B., (2013). Detecting land use-water quality relationships from the viewpoint of ecological restoration in an urban area. Ecol. Eng., 53: 205–216 (12 pages)

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