Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models
"> Figure 1
<p>Location of the study area. The 29 sectors of the study area are shown on a true-color composite of Landsat-8 OLI 2013 imagery.</p> "> Figure 2
<p>Workflow showing the main operations in the study. In the first phase, the Landsat images were classified, and the LU/LC layers were prepared. In the second phase, the changes that occurred in 1989–2000 and 2000–2013 were identified. In the third phase, the urban growth was simulated for 2013 with the CA-MC and MLP-MC methods. The simulation for 2025 was obtained by determining the method that provided the best results in the study area through a comparison of the results with the LU/LC data from 2013.</p> "> Figure 3
<p>Workflow of the simulation of urban growth. In the first phase, the urban growth simulation was performed by the CA-MC and MLP-MC methods for the year 2013. The same data were used in both simulations, and the areas with changes were calculated with the MC. Based on the validity assessment of the simulation results, the simulation for 2025 was run using MLP-MC, which provided the best results in the study area.</p> "> Figure 4
<p>The MLP architecture with the organization of layers (Reused from Ref. [<a href="#B60-remotesensing-07-05918" class="html-bibr">60</a>], Figure 8.2 with permission of Springer Science+Business Media).</p> "> Figure 5
<p>LU/LC classes for the years 1989 and 2000, and the spatial distribution of the changed areas for the period of 1989–2000.</p> "> Figure 6
<p>LU/LC classes for the years 2000 and 2013, and the spatial distribution of the changed areas for the period of 2000–2013.</p> "> Figure 7
<p>Artificial surfaces-suitability layers of Atakum. Higher values indicate higher probabilities of urban growth.</p> "> Figure 8
<p>Evidence-likelihood layers.</p> "> Figure 9
<p>Simulation results from the CA-MC and MLP-MC models and comparison of the CA-MC and MLP-MC results with the reference LU/LC 2013 layer.</p> "> Figure 10
<p>Simulation results from MLP-MC and comparison with the LU/LC 2013 layer. The figure presents a comparison between the present and probable future situations. The transition to artificial surfaces will likely destroy agricultural and forests.</p> "> Figure 11
<p>The distribution of artificial surfaces in absolute agricultural areas by year and the effects of urbanization. The figure shows that a continuous increase in the artificial surfaces has occurred in absolute agricultural areas. These new artificial surfaces may cause even more destruction of absolute agricultural areas.</p> "> Figure 12
<p>The changes in Cobanli Forest by year as a result of urbanization. The figure shows the gradual forest destruction caused by urbanization.</p> ">
Abstract
:1. Introduction
2. Study Area: Atakum
3. Data and Methodology
3.1. Preprocessing of Satellite Images and Classification
Class | Content (Features in the Study Area) |
---|---|
Artificial surfaces | Residential areas, commercial and industrial areas, roads and other construction sites |
Agricultural areas | Active farmland |
Forest and semi-natural areas | Forest, grove, active/passive green spaces |
Open spaces with little or no vegetation | Bare soil, beach, sparsely vegetated areas, sand, gravel and stone quarries |
Water bodies | Sea, dam reservoir, river |
3.2. Change Analysis
3.3. Simulation
3.3.1. CA-MC
3.3.2. MLP-MC
3.3.3. Determination of Transition Potential
Artificial surfaces-Suitability Layer
Factors | Constraint (Areas not Suitable for Urban Growth) |
---|---|
Distance to main urban centers | Areas not geologically suitable for settlement |
Distance to tram system | 20 m from riverside (flood risk boundaries of rivers) |
Distance to major roads | 50 m from sea coastline |
Distance to existing built-up surfaces | Areas covered with water |
Distance to sea coast | Current artificial surfaces |
Slopes (percent) | |
Distance to rivers | |
Because major roads and existing built-up surfaces vary by year, the distance to existing built-up surfaces and the distance to major roads were calculated in both the 2013 simulation and 2025 simulation. The constraints layer (due to artificial surface changes) for the 2013 and 2025 simulations were also arranged. The situation in 2000 was taken into account for the 2013 simulation, and the situation in 2013 was taken into account for the 2025 simulation. |
Factors | Function | Explanation of Control Points | |
---|---|---|---|
Distance to main urban centers | Linear | 0 m highest suitability | |
0–3 km decreasing suitability | |||
>3 km no suitability | |||
Distance to tram system | J-shaped | 0–100 m highest suitability | |
100 m–1.5 km decreasing suitability | |||
>1.5 km no suitability | |||
Distance to major roads | J-shaped | 0–100 m highest suitability | |
100 m–1.5 km decreasing suitability | |||
>1.5 km no suitability | |||
Distance to existing built-up surfaces | Linear | 0 m highest suitability | |
0–1 km decreasing suitability | |||
>1 km no suitability | |||
Distance to sea coast | J-shaped | 0–50 m no suitability | |
50 m–2 km decreasing suitability | |||
>2 km no suitability | |||
Slopes (percent) | Sigmoid | 0% highest suitability | |
0%–15% decreasing suitability | |||
>15% no suitability | |||
Distance to rivers | Sigmoid | 0–20 m no suitability | |
20 m–0.5 km increasing suitability | |||
>0.5 km highest suitability |
Testing the Selected Driving Factors
Validation of Simulation Results
4. Results
1989 | 2000 | 2013 | ||||
---|---|---|---|---|---|---|
Class | Producer | User | Producer | User | Producer | User |
Artificial surfaces | 87.6% | 84.4% | 86.5% | 84.8% | 88.7% | 88.0% |
Agricultural areas | 85.3% | 86.0% | 88.7% | 88.0% | 87.9% | 86.8% |
Forest and semi-natural areas | 88.5% | 89.6% | 85.9% | 87.6% | 89.8% | 92.0% |
Open spaces with little or no vegetation | 85.5% | 85.2% | 86.1% | 86.4% | 89.5% | 88.4% |
Water bodies | 96.9% | 98.8% | 98.4% | 98.8% | 98.4% | 99.2% |
Overall accuracy | 88.8% | 89.1% | 90.9% | |||
Kappa | 0.86 | 0.86 | 0.89 |
4.1. LU/LC Layers and Accuracy Assessment
4.2. Change Analysis
4.3. Simulation
4.3.1. LU/LC Transition Analysis
Year: 2000 ha (%) | ||||||
Year: 1989 ha (%) | Artificial Surfaces | Agricultural Areas | Forest and Semi-Natural Areas | Open Spaces with Little or No Vegetation | Water Bodies | Total |
Artificial surfaces | 507.4 (5.01%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 507.4 (5.01%) |
Agricultural areas | 388.4 (3.83%) | 6361.3 (62.80%) | 97.9 (0.97%) | 2.7 (0.03%) | 0.1 (0.001%) | 6850.4 (67.63%) |
Forest and semi-natural areas | 5.0 (0.05%) | 0.1 (0.001%) | 958.8 (9.47%) | 89.9 (0.89%) | 0 (0%) | 1053.8 (10.40%) |
Open spaces with little or no vegetation | 78.3 (0.77%) | 0.3 (0.003%) | 14.0 (0.14%) | 605.9 (5.98%) | 0.5 (0.005%) | 699.0 (6.90%) |
Water bodies | 0 (0%) | 0 (0%) | 0 (0%) | 3.8 (0.04%) | 1014.6 (10.02%) | 1018.4 (10.05%) |
Total | 979.1 (9.67%) | 6361.7 (62.81%) | 1070.7 (10.57%) | 702.3 (6.93%) | 1015.2 (10.02%) | 10,129.00(100%) |
Year: 2013 ha (%) | ||||||
Year: 2000 ha (%) | Artificial surfaces | Agricultural areas | Forest and semi-natural areas | Open spaces with little or no vegetation | Water bodies | Total |
Artificial surfaces | 979.1 (9.67%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 979.1 (9.67%) |
Agricultural areas | 608.3 (6.01%) | 5724.5 (56.52%) | 28.8 (0.28%) | 0 (0%) | 0 (0%) | 6361.6 (62.81%) |
Forest and semi-natural areas | 5.2 (0.05%) | 32.3 (0.32%) | 1011.8 (9.99%) | 21.4 (0.21%) | 0 (0%) | 1070.7 (10.57%) |
Open spaces with little or no vegetation | 89.3 (0.88%) | 16.1 (0.16%) | 0 (0%) | 596.9 (5.89%) | 0 (0%) | 702.3 (6.93%) |
Water bodies | 0 (0%) | 0 (0%) | 0 (0%) | 28.1 (0.28%) | 987.2 (9.74%) | 1015.3 (10.02%) |
Total | 1681.9 (16.61%) | 5772.9 (56.99%) | 1040.6 (10.27%) | 646.4 (6.38%) | 987.2 (9.74%) | 10,129.00 (100%) |
4.3.2. Composition of Transition Potential Layers
Transition Probability Matrix (2013) | Transition Areas Matrix (2013) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LU/LC1 | LU/LC2 | LU/LC3 | LU/LC4 | LU/LC5 | Total | LU/LC1 | LU/LC2 | LU/LC3 | LU/LC4 | LU/LC5 | Total | ||
LU/LC1 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 979.1 | 0.0 | 0.00 | 0.0 | 0.0 | 979.1 | |
LU/LC2 | 0.0668 | 0.9160 | 0.0168 | 0.0004 | 0.0000 | 1.0000 | 425.0 | 5827.0 | 107.0 | 2.6 | 0.1 | 6361.7 | |
LU/LC3 | 0.0055 | 0.0001 | 0.8938 | 0.1006 | 0.0000 | 1.0000 | 5.9 | 0.1 | 957.0 | 107.7 | 0.0 | 1070.7 | |
LU/LC4 | 0.1320 | 0.0005 | 0.0237 | 0.8431 | 0.0008 | 1.0000 | 92.7 | 0.4 | 16.7 | 592.0 | 0.5 | 702.3 | |
LU/LC5 | 0.0000 | 0.0000 | 0.0000 | 0.0044 | 0.9956 | 1.0000 | 0.00 | 0.00 | 0.0 | 4.4 | 1010.8 | 1015.2 | |
Total | 1502.7 | 5827.5 | 1080.7 | 706.7 | 1011.4 | 10,129.0 | |||||||
Transition Probability Matrix (2025) | Transition Areas Matrix (2025) | ||||||||||||
LU/LC1 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | Total | 1681.9 | 0.0 | 0.0 | 0.0 | 0.0 | 1681.9 | |
LU/LC2 | 0.0886 | 0.9072 | 0.0042 | 0.0000 | 0.0000 | 1.0000 | 511.7 | 5236.9 | 24.3 | 0.0 | 0.0 | 5772.9 | |
LU/LC3 | 0.0043 | 0.0280 | 0.9491 | 0.0186 | 0.0000 | 1.0000 | 4.4 | 29.2 | 987.6 | 19.4 | 0.0 | 1040.6 | |
LU/LC4 | 0.1180 | 0.0214 | 0.0000 | 0.8605 | 0.0000 | 1.0000 | 76.3 | 13.9 | 0.0 | 556.2 | 0.0 | 646.4 | |
LU/LC5 | 0.0000 | 0.0000 | 0.0000 | 0.0256 | 0.9744 | 1.0000 | 0.0 | 0.0 | 0.0 | 25.2 | 962.0 | 987.2 | |
Total | 2274.3 | 5280.0 | 1011.9 | 600.8 | 962.0 | 10,129.0 |
Criteria | C1 | C2 | C3 | C4 | C5 | C6 | C7 | Weights |
---|---|---|---|---|---|---|---|---|
C1 | 1 | 1 | 2 | 2 | 3 | 4 | 5 | 0.255 |
C2 | 1 | 1 | 2 | 2 | 3 | 4 | 5 | 0.255 |
C3 | 0.50 | 0.5 | 1 | 1 | 2 | 3 | 4 | 0.150 |
C4 | 0.50 | 0.5 | 1 | 1 | 2 | 3 | 4 | 0.150 |
C5 | 0.33 | 0.33 | 0.50 | 0.50 | 1 | 2 | 3 | 0.091 |
C6 | 0.25 | 0.25 | 0.33 | 0.33 | 0.50 | 1 | 3 | 0.062 |
C7 | 0.20 | 0.20 | 0.25 | 0.25 | 0.33 | 0.33 | 1 | 0.037 |
CR=0.02 | Σ = 1.000 |
Variable | Artificial surfaces | Agricultural areas | Forest and semi-natural areas | Open spaces with little or no vegetation | Water bodies | |
---|---|---|---|---|---|---|
2013 | ||||||
Artificial surfaces suitability | 0.5740 | 0.5627 | 0.6333 | 0.3955 | 0.1572 | |
Evidence likelihood | 0.7920 | 0.3444 | 0.8988 | 0.8914 | 0.8607 | |
2025 | ||||||
Artificial surfaces suitability | 0.4847 | 0.6582 | 0.6879 | 0.4309 | 0.1759 | |
Evidence likelihood | 0.4988 | 0.2754 | 0.8659 | 0.4933 | 0.8783 |
4.3.3. 2013 Simulations Using the CA-MC and MLP-MC Methods and Validation of the Models
Agreement | CA-MC | MLP-MC |
---|---|---|
Kno | 0.86 | 0.89 |
Klocation | 0.85 | 0.88 |
KlocationStrata | 0.85 | 0.88 |
Kstandard (Overall kappa) | 0.83 | 0.85 |
Cramer’s V | 0.84 | 0.86 |
KIA | ||
---|---|---|
Class | CA-MC | MLP-MC |
Artificial surfaces | 0.68 | 0.70 |
Agricultural areas | 0.85 | 0.87 |
Forest and semi-natural areas | 0.86 | 0.90 |
Open spaces with little or no vegetation | 0.80 | 0.82 |
Water bodies | 1.00 | 1.00 |
4.4. 2025 Simulation Using the MLP-MC Method
Class Transition | Area (ha (%)) |
---|---|
Artificial surfaces to artificial surfaces | 1681.9 (16.61%) |
Agricultural areas to artificial surfaces | 511.7 (5.05%) |
Forest and semi-natural areas to artificial surfaces | 4.4 (0.04%) |
Open spaces with little or no vegetation to artificial surfaces | 76.3 (0.75%) |
Water bodies to artificial surfaces | - |
Total artificial surfaces expected in 2025 | 2274.3 (22.45%) |
Total study area | 10,129.0 (100%) |
5. Discussion
6. Conclusions
Acknowledgements
Conflicts of Interest
References
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Ozturk, D. Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models. Remote Sens. 2015, 7, 5918-5950. https://doi.org/10.3390/rs70505918
Ozturk D. Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models. Remote Sensing. 2015; 7(5):5918-5950. https://doi.org/10.3390/rs70505918
Chicago/Turabian StyleOzturk, Derya. 2015. "Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models" Remote Sensing 7, no. 5: 5918-5950. https://doi.org/10.3390/rs70505918