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Modeling Land Use Land Cover Using Cellular Automata - Markov Chain: A Case of Belete Gera Regional Forest Priority Area, South Western Ethiopia

Received: 8 December 2022     Accepted: 13 February 2023     Published: 24 February 2023
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Abstract

Arrogant practices of land use change including expansion of agricultural land and infrastructural development are resulted to deforestation which goes to climate change. Cellular Automata (CA)-Markov chain combines the advantages of cellular automata and Markov chain analysis to simulate and predict future land use/cover trends depending on the Land Use Land Cover (LULC) changes. Spatial distribution of LULC and area changed were calculated using IDRISI software and GIS technology. Therefore, the forest land cover conversion to other LULC was evaluated to obtain rate of deforestation. Secondly, using transition probability matrices of 1999-2018, CA-Markov chain model was executed to simulate spatial distribution of land use/cover in 2018. Based on the simulated LULC map of 2018 and the actual LULC map of 2018 CA-Markov Model was validated with a kappa index of 1. As a result the kappa index of the validated result was 0.8 means it is accurate for the model. Finally, future land use/cover change of 2018-2037 and 2037-2056 were predicted using CA-Markov Chain Model. Therefore, the results revealed that decreasing of forest land and increasing of agricultural land in the study area are the major results. Specifically forest land was decreased by 52,156.71 hectares from 1980 to 2018, while agricultural land increased by 78,021.35 hectares during 1980-2018. In addition, the rate of deforestation between 1980 and 2018 was 1,372.54 hectares per year. The predicted results of 2037 year would be identified forest cover decreases by 30,204.65 hectares within future 19 years and agricultural land would be increases by 30,693.91 hectares between 2018 and 2037. The result of the study approved concerned bodies those working on the forest protection have to work better on the forest protecting and address a tough land use system.

Published in American Journal of Remote Sensing (Volume 11, Issue 1)
DOI 10.11648/j.ajrs.20231101.11
Page(s) 1-15
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

GIS, Remote Sensing, Cellular Automata, Markov Chain, Transition Matrix, Transition Probability Matrix, Transition Suitability Map

References
[1] Liping, C., S. Yujun, and S. Saeed, Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques. 2018.
[2] Jeremy, D. K., A survey of the use of CA and CA-like models for simulating a population of biological cells. 2011.
[3] Ozah, A. P., Dami, and F. A. Adesina, A deterministic cellular automata model for simulating rural land use dynamics. Journal of Earth Science and Enginering, 2012 (2 (1)).
[4] Rahaman, S. A., et al., Land use/land cover changes in semi-arid mountain landscape in Southern India: A geoinformatics based Markov chain approach. International Archives of Photogrammetry Remote Sensing and Spatial Information Science, 2017.
[5] Mondel, M. S., et al., Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. The Egyptian Journal of Remote Sensing and Space Science, 2016 (19 (2)): p. 259-272.
[6] He, D., et al., An integrated CA-markov model for dynamic simulation of land use change in Lake Dianchi watershed. Acta Scientiarum Naturalium Universitatis Pekinensis, 2014 (50 (6)).
[7] Danano, K. A., A. T. Legesse, and D. Likisa, Monitoring Deforestation in South Western Ethiopia Using Geospatial Technologies. Journal of Remote Sensing and GIS, 2018 (7): p. 229.
[8] Yasusuki Todo, A. R., Impact of Farmer field schools on Agricultural income and skills. 2011, JIC Research Institute.
[9] Foody, G. M., Status of land cover classification accuracy assessment. 2002: p. 185-201.
[10] Hua, A. K., Application of CA-Markov Model and Land Use Land Cover Change in Malacca River Watershed, Malaysia. 2017.
[11] Weng, Q., Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modeling. Journal of Environmental Management, 2002: p. 273-284.
[12] Araya, Y. H., Urban land use change analysis and modeling: a case study of Setubal and Sesimbra. Unpublished Thesis Institute for Geoinformatics University of Munster, 2009.
[13] Damjan, S., Discrete Time Markov Chains With Interval Probabilities. International journal of Approximate Reasoning, 2009. 50.
[14] Chen, L., S. Yujun, and S. Saeed, Monitoring and Predicting Land Use Land Cover Change Using Remote Sensing and GIS Techniques. 2018.
[15] Ma, C., et al., Application of Markov model in wetland change dynamics in Tianjin Coastal Area. Journal of Environmental Science, (13 (62)).
[16] Yang, Q., X. Li, and X. Shi, Cellular automata for simulating land use changes based on support vector machines. Computer and Geosciences, 2008 (34 (6)).
[17] Chen, E. A., Monitoring and Predicting Land use Land Cover Change Using Remote Sensing and GIS Techniques. 2018.
[18] Li, Y. C. and C. Y. He, Scenario simulation and forecast of land use/cover in northern China. 2008 (53): p. 1401-1412.
[19] Guan, D., et al., Modeling Urban Land Use Change by the Integration of CA and Markov Model. Journal of Ecological Modeling. Journal of Ecological Modeling, 2011.
[20] Pontius Jr, R. G. and H. Chen, Land change modeling with GEOMOD. Clark University, 2006 (11).
[21] Eastman, J. R., IDRISI Andes, Guide to GIS and Image Processing. 2006.
[22] Subedi, P., K. Subedi, and B. Thapa, Application of a hybrid cellular automaton–Markov (CA-Markov) model in land-use change prediction: a case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences, 2013 (1 (6)): p. 126-132.
[23] Hoffer, R. M., Biological and physical considerations in applying computer-aided analysis techniques to remote sensor data. Remote sensing. Quantitative Approach, 1978.
[24] Myint, S. W. and L. Wang, Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing, 2006 (32 (6)): p. 390-404.
[25] ESCAP, Manual on geographic information System, for Planners and Decision makers. United Nation. 1996.
[26] Geist, H. J. and E. F. Lambin, Proximate Causes and Underlying Driving Forces of Tropical Deforestation Tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. Bio Science, 2002 (52 (2)): p. 143-150.
[27] Arsanjani, J. J., et al., Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 2013: p. 21, 265-275.
[28] Antonio, C. A., Pleas, plights and environment. Springer Netherlands. 1991. 11. 2.
[29] FAO, Global forest resources assessment, country report, Ethiopia. 2010.
Cite This Article
  • APA Style

    Wendafiraw Abdisa Gemmechis, Abiyot Legesse Tura. (2023). Modeling Land Use Land Cover Using Cellular Automata - Markov Chain: A Case of Belete Gera Regional Forest Priority Area, South Western Ethiopia. American Journal of Remote Sensing, 11(1), 1-15. https://doi.org/10.11648/j.ajrs.20231101.11

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    ACS Style

    Wendafiraw Abdisa Gemmechis; Abiyot Legesse Tura. Modeling Land Use Land Cover Using Cellular Automata - Markov Chain: A Case of Belete Gera Regional Forest Priority Area, South Western Ethiopia. Am. J. Remote Sens. 2023, 11(1), 1-15. doi: 10.11648/j.ajrs.20231101.11

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    AMA Style

    Wendafiraw Abdisa Gemmechis, Abiyot Legesse Tura. Modeling Land Use Land Cover Using Cellular Automata - Markov Chain: A Case of Belete Gera Regional Forest Priority Area, South Western Ethiopia. Am J Remote Sens. 2023;11(1):1-15. doi: 10.11648/j.ajrs.20231101.11

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  • @article{10.11648/j.ajrs.20231101.11,
      author = {Wendafiraw Abdisa Gemmechis and Abiyot Legesse Tura},
      title = {Modeling Land Use Land Cover Using Cellular Automata - Markov Chain: A Case of Belete Gera Regional Forest Priority Area, South Western Ethiopia},
      journal = {American Journal of Remote Sensing},
      volume = {11},
      number = {1},
      pages = {1-15},
      doi = {10.11648/j.ajrs.20231101.11},
      url = {https://doi.org/10.11648/j.ajrs.20231101.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20231101.11},
      abstract = {Arrogant practices of land use change including expansion of agricultural land and infrastructural development are resulted to deforestation which goes to climate change. Cellular Automata (CA)-Markov chain combines the advantages of cellular automata and Markov chain analysis to simulate and predict future land use/cover trends depending on the Land Use Land Cover (LULC) changes. Spatial distribution of LULC and area changed were calculated using IDRISI software and GIS technology. Therefore, the forest land cover conversion to other LULC was evaluated to obtain rate of deforestation. Secondly, using transition probability matrices of 1999-2018, CA-Markov chain model was executed to simulate spatial distribution of land use/cover in 2018. Based on the simulated LULC map of 2018 and the actual LULC map of 2018 CA-Markov Model was validated with a kappa index of 1. As a result the kappa index of the validated result was 0.8 means it is accurate for the model. Finally, future land use/cover change of 2018-2037 and 2037-2056 were predicted using CA-Markov Chain Model. Therefore, the results revealed that decreasing of forest land and increasing of agricultural land in the study area are the major results. Specifically forest land was decreased by 52,156.71 hectares from 1980 to 2018, while agricultural land increased by 78,021.35 hectares during 1980-2018. In addition, the rate of deforestation between 1980 and 2018 was 1,372.54 hectares per year. The predicted results of 2037 year would be identified forest cover decreases by 30,204.65 hectares within future 19 years and agricultural land would be increases by 30,693.91 hectares between 2018 and 2037. The result of the study approved concerned bodies those working on the forest protection have to work better on the forest protecting and address a tough land use system.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Modeling Land Use Land Cover Using Cellular Automata - Markov Chain: A Case of Belete Gera Regional Forest Priority Area, South Western Ethiopia
    AU  - Wendafiraw Abdisa Gemmechis
    AU  - Abiyot Legesse Tura
    Y1  - 2023/02/24
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajrs.20231101.11
    DO  - 10.11648/j.ajrs.20231101.11
    T2  - American Journal of Remote Sensing
    JF  - American Journal of Remote Sensing
    JO  - American Journal of Remote Sensing
    SP  - 1
    EP  - 15
    PB  - Science Publishing Group
    SN  - 2328-580X
    UR  - https://doi.org/10.11648/j.ajrs.20231101.11
    AB  - Arrogant practices of land use change including expansion of agricultural land and infrastructural development are resulted to deforestation which goes to climate change. Cellular Automata (CA)-Markov chain combines the advantages of cellular automata and Markov chain analysis to simulate and predict future land use/cover trends depending on the Land Use Land Cover (LULC) changes. Spatial distribution of LULC and area changed were calculated using IDRISI software and GIS technology. Therefore, the forest land cover conversion to other LULC was evaluated to obtain rate of deforestation. Secondly, using transition probability matrices of 1999-2018, CA-Markov chain model was executed to simulate spatial distribution of land use/cover in 2018. Based on the simulated LULC map of 2018 and the actual LULC map of 2018 CA-Markov Model was validated with a kappa index of 1. As a result the kappa index of the validated result was 0.8 means it is accurate for the model. Finally, future land use/cover change of 2018-2037 and 2037-2056 were predicted using CA-Markov Chain Model. Therefore, the results revealed that decreasing of forest land and increasing of agricultural land in the study area are the major results. Specifically forest land was decreased by 52,156.71 hectares from 1980 to 2018, while agricultural land increased by 78,021.35 hectares during 1980-2018. In addition, the rate of deforestation between 1980 and 2018 was 1,372.54 hectares per year. The predicted results of 2037 year would be identified forest cover decreases by 30,204.65 hectares within future 19 years and agricultural land would be increases by 30,693.91 hectares between 2018 and 2037. The result of the study approved concerned bodies those working on the forest protection have to work better on the forest protecting and address a tough land use system.
    VL  - 11
    IS  - 1
    ER  - 

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Author Information
  • Department of Geography and Environmental Studies, College of Social Science and Humanities, Bule Hora University, Bule Hora, Ethiopia

  • Department of Geography and Environmental Studies, College of Social Science and Humanities, Dilla University, Dilla, Ethiopia

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