Land-Cover Change Analysis and Simulation in Conakry (Guinea), Using Hybrid Cellular-Automata and Markov Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Image Processing
2.4. Image Classification
2.5. Accuracy Assessment of the Land-Cover Classification
2.6. Future Land-Cover Change Based on CA-Markov Chain
3. Results
3.1. Land-Cover Classification
3.2. Markov models and the transition probability matrices
3.3. Accuracy Assessment of the Simulated Markov Model Based on ROC
3.4. Simulated Land-Cover Map of Conakry by 2025 Based on CA-Markov Model
4. Discussion
Land-Cover Change and Demographic Dynamics in Conakry
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Reference Image | User’s Accuracy (%) | |||||
---|---|---|---|---|---|---|
Classified Image | Urban | Water | Vegetation | Bare Ground | Total Row | |
Urban | 48 | 3 | 3 | 4 | 58 | 83 |
Water | 2 | 39 | 4 | 3 | 48 | 81 |
Vegetation | 4 | 3 | 45 | 2 | 54 | 83 |
Bare ground | 6 | 3 | 1 | 30 | 40 | 75 |
Total column | 60 | 48 | 53 | 39 | 200 | |
Producer’s accuracy(%) | 80 | 81 | 85 | 77 | ||
Overall accuracy: | 0.81 | |||||
Kappa coefficient | 0.75 |
Reference Image | User’s Accuracy (%) | |||||
---|---|---|---|---|---|---|
Classified Image | Urban | Water | Vegetation | Bare Ground | Total Row | |
Urban | 46 | 3 | 5 | 5 | 59 | 78 |
Water | 3 | 38 | 2 | 3 | 46 | 83 |
Vegetation | 3 | 2 | 39 | 7 | 51 | 76 |
Bare ground | 1 | 3 | 5 | 35 | 44 | 80 |
Total column | 53 | 46 | 51 | 50 | 200 | |
Producer’s accuracy (%) | 87 | 83 | 76 | 70 | ||
Overall accuracy | 0.79 | |||||
Kappa coefficient | 0.72 |
Reference Image | User’s Accuracy (%) | |||||
---|---|---|---|---|---|---|
Classified Image | Urban | Water | Vegetation | Bare Ground | Row Total | |
Urban | 49 | 2 | 1 | 2 | 54 | 91 |
Water | 2 | 42 | 2 | 1 | 47 | 89 |
Vegetation | 3 | 2 | 45 | 2 | 52 | 87 |
Bare ground | 2 | 3 | 2 | 40 | 47 | 85 |
Total column | 56 | 49 | 50 | 45 | 200 | |
Producer’s accuracy (%) | 88 | 86 | 90 | 89 | ||
Overall accuracy | 0.88 | |||||
Kappa coefficient | 0.83 |
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Satellite | Sensor | Path/Row | Spatial Resolution | Date of Acquisition | Sources |
---|---|---|---|---|---|
Landsat 5 | TM | 202/53 | 30 m | 01/03/1986 | USGS |
Landsat 7 | ETM+ | 202/53 | 30 m | 12/19/2000 | USGS |
Landsat 8 | OLI | 202/53 | 30 m | 01/20/2016 | USGS |
Class | Description |
---|---|
Urban | Residential, commercial, industrial, transportation, utilities, communication etc. |
Water | Rivers, lakes, ponds, reservoirs, and other water bodies |
Vegetation | Mangrove forests, high vegetation, reserved forest, non-reserved forest |
Bare ground | Fallow land, bare exposed, parks, shrubs, area and transition |
Probability of Transition | |||||
---|---|---|---|---|---|
From/To | Urban | Water | Vegetation | Bare Ground | Total |
urban | 0.936 | 0.000 | 0.000 | 0.063 | 1.000 |
water | 0.016 | 0.862 | 0.108 | 0.011 | 1.000 |
vegetation | 0.326 | 0.096 | 0.459 | 0.117 | 1.000 |
bare ground | 0.127 | 0.051 | 0.000 | 0.821 | 1.000 |
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Traore, A.; Mawenda, J.; Komba, A.W. Land-Cover Change Analysis and Simulation in Conakry (Guinea), Using Hybrid Cellular-Automata and Markov Model. Urban Sci. 2018, 2, 39. https://doi.org/10.3390/urbansci2020039
Traore A, Mawenda J, Komba AW. Land-Cover Change Analysis and Simulation in Conakry (Guinea), Using Hybrid Cellular-Automata and Markov Model. Urban Science. 2018; 2(2):39. https://doi.org/10.3390/urbansci2020039
Chicago/Turabian StyleTraore, Arafan, John Mawenda, and Atupelye Weston Komba. 2018. "Land-Cover Change Analysis and Simulation in Conakry (Guinea), Using Hybrid Cellular-Automata and Markov Model" Urban Science 2, no. 2: 39. https://doi.org/10.3390/urbansci2020039
APA StyleTraore, A., Mawenda, J., & Komba, A. W. (2018). Land-Cover Change Analysis and Simulation in Conakry (Guinea), Using Hybrid Cellular-Automata and Markov Model. Urban Science, 2(2), 39. https://doi.org/10.3390/urbansci2020039