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), 2024. Published by Science Publishing Group |
GIS, Remote Sensing, Cellular Automata, Markov Chain, Transition Matrix, Transition Probability Matrix, Transition Suitability Map
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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
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
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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Download@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}
}
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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