Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Data Materials and Preprocessing
3. Method
3.1. Land Use Simulation Using a CA–Markov Model
3.1.1. Cellular Automata
3.1.2. Markov Chain
3.1.3. CA–Markov Model
3.2. Model Validation and Accuracy Assessment
3.3. Multi-Scenario Simulation
3.4. LUCC Modeling and Analysis
3.4.1. Land Use Conversion Modeling
3.4.2. LUCC Effect Analysis
4. Results and Analysis
4.1. Land System Changes from 2005 to 2015
4.2. Simulation Validation
4.3. Multiple Scenario Simulations for Future Land Use Pattern
4.4. Future Land System Dynamics under Multiple Scenarios
4.5. The Effect of Land System Changes under Multiple Scenarios
4.5.1. Urban Expansion Analysis
4.5.2. Grain Yield Estimation
4.5.3. Ecology Quality Evaluation
5. Discussion
5.1. Scenario Preference and Suitability for Regional Sustainability
5.2. Future Land Use Optimization and Policy Formulation
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Materials | Data Type | Original Resolution | Data Resource |
---|---|---|---|
Land use/cover data (2005/2010/2015) | Land use/cover data | 30 m | https://www.resdc.cn/DOI/doi.aspx?DOIid=54, accessed on 7 May 2022 |
Administrative vector map (2015) | Vector map | --- | Resource and Environmental Science Data Platform (https://www.resdc.cn/DOI/DOI.aspx?DOIid=120, accessed on 11 June 2022) |
Socio-economic factors | Distance from highway | 30 m | OpenStreetMap (https://www.openstreetmap.org/, accessed on 11 June 2022) |
Distance from trunk road | |||
Distance from primary road | |||
Distance from secondary road | |||
Distance from railway | |||
Distance from important towns | 30 m | http://lbsyun.baidu.com/, accessed on 11 June 2022 | |
Natural factors | Annual Temperature | 30 arc-s | WorldClim v2.0 (http://www.worldclim.org/, accessed on 11 June 2022) |
Elevation | 30 m | NASA SRTM1 v3.0 | |
Slope | 30 m |
2015 | Cropland | Forest | Grassland | Water | Build-Up Land | Unused Land | Initial Total | Gross Loss | |
---|---|---|---|---|---|---|---|---|---|
2005 | |||||||||
Cropland | 11,306.1 | 300.84 | 17.51 | 342.91 | 1071.02 | 0.15 | 13,038.53 | 1732.43 | |
Forest | 314.51 | 28,707.72 | 191 | 86.76 | 566.9 | 0.25 | 29,867.14 | 1159.42 | |
Grassland | 19.37 | 63.62 | 944.63 | 12.24 | 44.57 | 0.06 | 1084.49 | 139.86 | |
Water | 562.14 | 59.62 | 10.31 | 2982.11 | 335.82 | 0.3 | 3950.3 | 968.19 | |
Build-up land | 263.23 | 208.52 | 12.66 | 96 | 5562.94 | 0.06 | 6143.41 | 580.47 | |
Unused land | 2.41 | 0.77 | 0.08 | 0.39 | 3.89 | 5.62 | 13.16 | 7.54 | |
Final total | 12,467.76 | 29,341.09 | 1176.19 | 3520.41 | 7585.14 | 6.44 | 54,097.03 | ||
Net change | −570.77 | −526.05 | 91.7 | −429.89 | 1441.73 | −6.72 |
Land Use Type | PA (%) | UA (%) | OA (%) | Kappa | FOM (%) | T |
---|---|---|---|---|---|---|
Cropland | 89.19 | 89.45 | 90.27 | 0.8495 | 89.14 | 587,686 |
Forest | 90.41 | 98.11 | ||||
Grassland | 78.88 | 85.65 | ||||
Water | 99.95 | 81.30 | ||||
Build-up land | 88.79 | 73.14 | ||||
Unused land | 77.33 | 87.83 |
Time | Scenario | Cropland | Forest | Grassland | Water | Build-Up Land | Unused Land |
---|---|---|---|---|---|---|---|
2030 | UD | 11,891.39 | 26,189.76 | 2123.79 | 3550.04 | 10,337.74 | 4.31 |
CP | 13,082.65 | 26,276.64 | 2132.02 | 3549.01 | 9052.39 | 4.32 | |
ES | 10,856.46 | 28,659.44 | 2156.83 | 3572.24 | 8847.73 | 4.33 | |
2050 | UD | 11,772.97 | 22,460.17 | 2311.55 | 3358.87 | 14,191.34 | 2.13 |
CP | 14,102.63 | 22,797.79 | 2342.44 | 3365.04 | 11,486.78 | 2.35 | |
ES | 9669.38 | 27,859.89 | 2457.66 | 3525.93 | 10,581.83 | 2.34 | |
2070 | UD | 11,967.37 | 18,154 | 2341.51 | 3130.48 | 18,502.74 | 0.93 |
CP | 15,377.63 | 18,661.33 | 2236 | 3032.34 | 14,788.58 | 1.15 | |
ES | 8945.99 | 25,923.37 | 2558.55 | 3520.76 | 13,146.51 | 1.85 |
Scenario | 2030 | 2050 | 2070 |
---|---|---|---|
Urban development | 6,242,664.75 | 6,180,489 | 6,282,528 |
Cropland protection | 6,868,076.25 | 7,403,555.25 | 8,072,893.5 |
Ecology security | 5,699,326.5 | 5,076,109.5 | 4,696,324.5 |
Scenario | 2030 | 2050 | 2070 |
---|---|---|---|
Urban development | 5.3261 | 4.7496 | 4.2650 |
Cropland protection | 5.3821 | 4.8486 | 4.3248 |
Ecology security | 5.7017 | 5.5221 | 5.1725 |
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Yang, C.; Zhai, H.; Fu, M.; Zheng, Q.; Fan, D. Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model. Remote Sens. 2024, 16, 1512. https://doi.org/10.3390/rs16091512
Yang C, Zhai H, Fu M, Zheng Q, Fan D. Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model. Remote Sensing. 2024; 16(9):1512. https://doi.org/10.3390/rs16091512
Chicago/Turabian StyleYang, Chao, Han Zhai, Meijuan Fu, Que Zheng, and Dasheng Fan. 2024. "Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model" Remote Sensing 16, no. 9: 1512. https://doi.org/10.3390/rs16091512
APA StyleYang, C., Zhai, H., Fu, M., Zheng, Q., & Fan, D. (2024). Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model. Remote Sensing, 16(9), 1512. https://doi.org/10.3390/rs16091512