Change of Land Use/Cover in Tianjin City Based on the Markov and Cellular Automata Models
<p>Location of the study area: (<b>a</b>) location of China in the world; (<b>b</b>) location of Tianjin city in China; (<b>c</b>) Tianjin area. Source: Esri, DigitalGlobe, Geoeye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, INP, swisstopo, and the GIS User Community.</p> "> Figure 2
<p>Spatial patterns of five variables: (<b>a</b>) distance to roads; (<b>b</b>) distance to subways; (<b>c</b>) distance to the capital bus stations; (<b>d</b>) distance to CBDs; (<b>e</b>) distance to markets.</p> "> Figure 3
<p>Process of simulation in urbanization modeling.</p> "> Figure 4
<p>Land use and land cover (LULC) in 1995 (<b>a</b>), 2005 (<b>b</b>), 2015 (<b>c</b>), change map from 1995–2005 (<b>d</b>), and change map 2005–2015 (<b>e</b>).</p> "> Figure 5
<p>Actual map in 2015 (<b>a</b>), simulation map in 2015 (<b>b</b>), and error map (<b>c</b>).</p> "> Figure 6
<p>Simulation LULC in 2025 (<b>a</b>) and in 2035 (<b>b</b>), and change map 2025–2035 (<b>c</b>).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. LULC Classification
2.4. Description of Markov Model in LULC
2.5. Description of Cellular Automata Model
2.6. Model Implementation Variables
2.7. Model Validation
- The relative observed agreement among layers
- The hypothetical probability of chance agreement
2.8. Annual Urban Growth Rate
2.9. Simulation of LULC in 2025 and 2035
3. Results
3.1. LULC Mapping
3.2. Assessing the Accuracy of LULC Maps
3.3. Land Use Type Transition Matrix
3.4. Model Validation
3.5. LULC Simulation of 2025 and 2035
4. Discussion
4.1. Trend of LULC in Tianjin and China
4.2. Methodological Issues
4.3. Potential Implications of LULC Change
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Classified Data | Reference Data | Total | User’s Accuracy (%) | ||||
---|---|---|---|---|---|---|---|
Built-Up | Cropland | Grass | Forest | Water | |||
Built-up | 87 | 4 | 7 | 1 | 1 | 100 | 87 |
Cropland | 4 | 80 | 7 | 6 | 3 | 100 | 80 |
Grass | 8 | 13 | 58 | 19 | 2 | 100 | 58 |
Forest | 3 | 4 | 11 | 80 | 2 | 100 | 80 |
Water | 1 | 7 | 10 | 0 | 82 | 100 | 82 |
Total | 103 | 108 | 93 | 106 | 90 | 500 | |
Producer’s accuracy (%) | 84.47 | 74.07 | 62.37 | 75.47 | 91.11 |
Classified Data | Reference Data | Total | User’s Accuracy (%) | ||||
---|---|---|---|---|---|---|---|
Built-Up | Cropland | Grass | Forest | Water | |||
Built-up | 87 | 2 | 11 | 0 | 0 | 100 | 87 |
Cropland | 4 | 80 | 11 | 5 | 0 | 100 | 80 |
Grass | 7 | 17 | 57 | 19 | 0 | 100 | 57 |
Forest | 0 | 5 | 8 | 87 | 0 | 100 | 87 |
Water | 0 | 4 | 6 | 3 | 87 | 100 | 87 |
Total | 98 | 108 | 93 | 114 | 87 | 500 | |
Producer’s accuracy (%) | 88.78 | 74.07 | 61.29 | 76.32 | 100 |
Classified Data | Reference Data | Total | User’s Accuracy (%) | ||||
---|---|---|---|---|---|---|---|
Built-Up | Cropland | Grass | Forest | Water | |||
Built-up | 83 | 6 | 7 | 3 | 1 | 100 | 83 |
Cropland | 5 | 78 | 8 | 6 | 3 | 100 | 78 |
Grass | 9 | 15 | 59 | 17 | 0 | 100 | 59 |
Forest | 3 | 4 | 11 | 80 | 2 | 100 | 80 |
Water | 2 | 7 | 7 | 0 | 84 | 100 | 84 |
Total | 102 | 110 | 92 | 106 | 90 | 500 | |
Producer’s accuracy (%) | 81.37 | 70.91 | 64.13 | 75.47 | 93.33 |
Appendix B
LULC in 1995 | LULC in 2005 | ||||
---|---|---|---|---|---|
Built-up | Cropland | Grass | Forest | Water | |
Built-up | 83.22 | 14.55 | 1.60 | 0.31 | 0.33 |
Cropland | 12.98 | 84.59 | 0.84 | 0.10 | 1.50 |
Grass | 4.81 | 9.10 | 75.60 | 1.48 | 9.01 |
Forest | 1.15 | 16.46 | 1.03 | 79.60 | 1.76 |
Water | 0.80 | 23.02 | 0.88 | 1.56 | 73.74 |
LULC in 2005 | LULC in 2015 | ||||
---|---|---|---|---|---|
Built-up | Cropland | Grass | Forest | Water | |
Built-up | 81.44 | 15.50 | 0.32 | 0.77 | 1.98 |
Cropland | 14.72 | 82.48 | 0.34 | 0.88 | 1.58 |
Grass | 1.03 | 38.54 | 56.79 | 2.84 | 0.79 |
Forest | 2.74 | 17.22 | 0.19 | 78.62 | 1.23 |
Water | 1.23 | 11.40 | 0.22 | 1.89 | 85.25 |
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Data Type | Date Acquired (Day/Month/Year) | Season | Path | Row |
---|---|---|---|---|
Landsat 5 TM | 19 November 1995 | Autumn | 123 | 32 |
19 November 1995 | Autumn | 123 | 33 | |
28 November 1995 | Autumn | 122 | 32 | |
28 November 1995 | Autumn | 122 | 33 | |
Landsat 5 TM | 22 October 2005 | Autumn | 122 | 32 |
29 October 2005 | Autumn | 123 | 33 | |
14 November 2005 | Autumn | 123 | 32 | |
23 November 2005 | Autumn | 122 | 32 | |
Landsat 8 OLI | 5 December 2015 | Winter | 122 | 32 |
5 December 2015 | Winter | 122 | 33 | |
28 December 2015 | Winter | 123 | 32 | |
28 December 2015 | Winter | 123 | 33 |
LULC Class | 1995 | 2005 | 2015 | |||
---|---|---|---|---|---|---|
Area (km2) | % | Area (km2) | % | Area (km2) | % | |
Built-up | 1537.92 | 13.52 | 2021.7532 | 17.78 | 2747.11 | 24.15 |
Cropland | 7632.16 | 67.1 | 7267.6775 | 63.90 | 6652.69 | 58.49 |
Grass | 247.84 | 2.17 | 320.0716 | 2.81 | 216.76 | 1.91 |
Forest | 451.76 | 3.97 | 355.5 | 3.13 | 394.7 | 3.47 |
Water | 1504.8 | 13.23 | 1408.6578 | 12.39 | 1362.48 | 11.98 |
Total | 11374 | 100 | 11374 | 100 | 11374 | 100 |
LULC Type | Area Change (km2) | Annual LULC Change Rate (%) | ||
---|---|---|---|---|
1995~2005 | 2005~2015 | 1995~2005 | 2005~2015 | |
Built-up | 483.83 | 725.36 | 3.15 | 3.59 |
Cropland | −364.48 | −614.99 | −0.48 | −0.85 |
Grass | 72.23 | −103.31 | 2.91 | −3.23 |
Forest | −96.26 | 39.20 | −2.13 | −0.06 |
Water | −96.14 | −46.18 | −0.64 | 0.00 |
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Wang, R.; Murayama, Y. Change of Land Use/Cover in Tianjin City Based on the Markov and Cellular Automata Models. ISPRS Int. J. Geo-Inf. 2017, 6, 150. https://doi.org/10.3390/ijgi6050150
Wang R, Murayama Y. Change of Land Use/Cover in Tianjin City Based on the Markov and Cellular Automata Models. ISPRS International Journal of Geo-Information. 2017; 6(5):150. https://doi.org/10.3390/ijgi6050150
Chicago/Turabian StyleWang, Ruci, and Yuji Murayama. 2017. "Change of Land Use/Cover in Tianjin City Based on the Markov and Cellular Automata Models" ISPRS International Journal of Geo-Information 6, no. 5: 150. https://doi.org/10.3390/ijgi6050150