Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model
Abstract
:1. Introduction
2. Study Area and Data Sources
2.1. Overview of the Study Area
2.2. Data and Criteria
3. Methodology
3.1. LULC Data Processing
3.2. Introduction of the Patch-Generating Land Use Simulation (PLUS) Model
3.3. Selection of Driving Factors
3.3.1. Geomorphology and Geology
3.3.2. Climatic Settings
3.3.3. Economic Development
3.3.4. Industrialization Policies
4. Results
4.1. Land Use Interpretation
4.2. Urban Expansion Simulation Based on PLUS Model
4.3. Verification of PLUS Model
4.4. Application of PLUS Model for Prediction
5. Conclusions and Discussion
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data | Criteria | Year | Description | Source | Data Format |
---|---|---|---|---|---|
Driving factor | GDP | 2005 2010 2015 2018 | 30 arc-rec spatial resolution | Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (http://www.radi.cas.cn/index_65411.html (accessed on 10 October 2021)) | GeoTIFF |
Subway | |||||
EDM | |||||
Elevation | |||||
Highway | |||||
River | |||||
Slope | |||||
Population | |||||
Soil | |||||
School | |||||
Hospital | |||||
LULC map | LULC | 2001~2019 | 500 arc-rec spatial resolution | United States Geological Survey (USGS) (https://earthexplorer.usgs.gov/ (accessed on 10 November 2021)) | GeoTIFF |
LULC map | LULC | 2005 2010 2015 2018 | 30 arc-rec spatial resolution | United States Geological Survey (USGS) (https://earthexplorer.usgs.gov/ (accessed on 10 November 2021)) | GeoTIFF |
Administrative map | Hangzhou | 2021 | A satellite image from Landsat, USGS and LAPAN | Ministry of Environment and Forestry, Indonesia (http://webgis.dephut.go.id:8080/kemenhut/index.php/id/fitur/unduhan (accessed on 10 November 2021) , https://earthexplorer.usgs.gov/ (accessed on 10 November 2021)) | KML |
Shaoxing | |||||
Huzhou | |||||
Jiaxing | |||||
Jinhua | |||||
Xuancheng | |||||
Huangshan | |||||
Quzhou |
Year | Kappa Coefficient (%) | User’s Accuracy (%) | ||||||
---|---|---|---|---|---|---|---|---|
Grass | Deciduous Forest | Cultivated Field | Urban Land | Bare Land | Waters | Evergreen Forest | ||
2005–2010 | 0.956 | 0.988 | 0.976 | 0.965 | 0.842 | 0.954 | 0.988 | 0.988 |
2010–2015 | 0.918 | 0.933 | 0.934 | 0.929 | 0.854 | 0.910 | 0.925 | 0.971 |
2015–2018 | 0.923 | 0.944 | 0.901 | 0.936 | 0.891 | 0.792 | 0.930 | 0.976 |
Year | Grass | Deciduous Forest | Cultivated Field | Urban Land | Bare Land | Waters | Evergreen Forest |
---|---|---|---|---|---|---|---|
2005–2010 | 2,620,709 | 8,157,319 | 23,216,915 | 4,817,324 | 15,459 | 2,151,630 | 44,151,632 |
Real (2010) | 2,809,602 | 8,248,160 | 23,216,938 | 4,817,326 | 20,945 | 2,349,566 | 43,688,758 |
2010–2015 | 2,784,092 | 8,278,020 | 22,533,997 | 5,478,163 | 10,133 | 2,338,896 | 43,727,994 |
Real (2015) | 2,784,850 | 8,545,279 | 22,536,173 | 5,609,103 | 22,292 | 2,340,847 | 43,312,322 |
2015–2018 | 2,668,851 | 8,566,745 | 22,075,873 | 5,916,046 | 19,137 | 2,327,583 | 43,576,631 |
Real (2018) | 2,820,751 | 8,127,297 | 22,080,545 | 6,189,235 | 19,141 | 2,328,292 | 43,580,324 |
Year | Grass | Deciduous Forest | Cultivated Field | Urban Land | Bare Land | Waters | Evergreen Forest |
---|---|---|---|---|---|---|---|
2024 | 2,888,345 | 7,412,612 | 21,330,385 | 7,156,186 | 14,780 | 2,305,684 | 44,025,773 |
2027 | 2,919,596 | 7,107,580 | 21,029,018 | 7,560,284 | 13,299 | 2,295,743 | 44,208,246 |
2030 | 2,949,129 | 6,832,732 | 20,767,392 | 7,919,104 | 12,149 | 2,286,461 | 44,366,799 |
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Xu, L.; Liu, X.; Tong, D.; Liu, Z.; Yin, L.; Zheng, W. Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model. Land 2022, 11, 652. https://doi.org/10.3390/land11050652
Xu L, Liu X, Tong D, Liu Z, Yin L, Zheng W. Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model. Land. 2022; 11(5):652. https://doi.org/10.3390/land11050652
Chicago/Turabian StyleXu, Linfeng, Xuan Liu, De Tong, Zhixin Liu, Lirong Yin, and Wenfeng Zheng. 2022. "Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model" Land 11, no. 5: 652. https://doi.org/10.3390/land11050652