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Cellular automata and Markov Chain (CA_Markov) model-based predictions of future land use and land cover scenarios (2015–2033) in Raya, northern Ethiopia

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Abstract

Little is known about the future land use and land cover (LULC) type in some parts of Ethiopia, but not in the study area. This study aims to predict and analyze the future scenarios of LULC (2015–2033) using cellular automata and Markov Chain model (CA_Markov) by taking into consideration the physical and socio-economic drivers of LULC dynamics. The historical LULC change data of 1984–1995, 1995–2015, and 1984–2015 were used as a baseline. Both transition rules and transition area matrix for the period 1984–1995, 1995–2015, and 1984–2015 were produced quantitatively using the Markov chain model. After that, the physical and socio-economic factors were standardized using fuzzy and then Multi-Criteria Evaluation (MCE) was used to produce the transition suitability image. The CA_Markov model was then applied as a standard contiguity filter of 5 × 5 to predict the 2033 LULC condition using the TerrSet Geospatial Modeling and Monitoring System software. The result indicated that forestland are predicted to increase by 108 sq km (44.5%), shrub/bush lands 710 sq km (20%), built-up area 286.2 sq km (48.3%), and grasslands 31 sq km (15%), respectively. However, significant reductions (losses) in a water body (Wb) 5.2 sq km (11.2%), croplands (Cl) 78.9 sq km (1.3%), barren lands (Bl) 800 sq km (27.4%), and floodplain area (Fp) 251.68 sq km (33.7%), respectively. Furthermore, the Pearson correlation result between the historical and predicted LULC type indicated that there are positive, strongly correlated, and are statistically significant relationships (r = 0.981, p = 0.000). The increase in forest land and reduction in barren and flood plain may benefit the study area. However, the decrease in the water body may contribute to the severity of drought in the area. This study may help to use as useful information to foster better decisions and improve policies in land use within the framework of sustainable land use planning system.

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Acknowledgements

This research was funded by Mekelle University under Grant number CRPO/ICS/PhD/001/09 and the Open Society Foundation–Africa Climate Change Adaptation Initiative (OSF–ACCAI). The lead author is thankful for the PhD scholarship given by the Intra-Africa-Transdisciplinary Training for Resource Efficiency and Climate Change Adaptation in Africa (TreccAfrica II) project. The authors would also like to thank the National Aeronautics and Space Administration (NASA), United States Geological Survey (USGS) for the provision of Landsat imagery.

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  1. Department of Environmental Science, University of Botswana, Private Bag UB 0704, Gaborone, Botswana

    Eskinder Gidey, Oagile Dikinya, Reuben Sebego & Eagilwe Segosebe

  2. Land Resource Management and Environmental Protection, Mekelle University, P.O. Box 231, Mekelle, Ethiopia

    Eskinder Gidey & Amanuel Zenebe

  3. Institute of Climate and Society, Mekelle University, P.O. Box 231, Mekelle, Ethiopia

    Eskinder Gidey & Amanuel Zenebe

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  1. Eskinder Gidey
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  2. Oagile Dikinya
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Correspondence to Eskinder Gidey.

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Gidey, E., Dikinya, O., Sebego, R. et al. Cellular automata and Markov Chain (CA_Markov) model-based predictions of future land use and land cover scenarios (2015–2033) in Raya, northern Ethiopia. Model. Earth Syst. Environ. 3, 1245–1262 (2017). https://doi.org/10.1007/s40808-017-0397-6

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