A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Preparation
2.3. A Two-Layer SD-ANN-CA Model Framework Construction
2.4. Estimation of Simulation Accuracy
- -
- Overall Error-accuracy (OA)
- -
- The kappa coefficient
2.5. Selection of Key Driving Factors
3. Results
3.1. Simulation and Results of the SD Model Layer
3.1.1. Identification of Meso-Level Feedback Constraints
3.1.2. Identification of Macro-Level Demand Constraint
3.2. Simulation of the ANN-CA Model Layer under Constraints
3.2.1. Artificial Neural Network Training under Meso-Level Constraints
3.2.2. Setting of CA Parameters Following ANN Rules and Spatial Constraints
3.3. Output Simulation Results under Macro-Level Demand Constraints
3.4. Land Use Prediction for the Years 2028 and 2038 in the Study Area
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Data | Time | Description | Source | Format/Resolution |
---|---|---|---|---|---|
Geospatial information data | Remote sensing images | 1998 2008 2018 | Three phases of graphic data regarding land use status through interpretation of remote sensing images. | Geospatial data cloud (http://www.gscloud.cn/search) [51] | Raster, 30 m |
DEM | 2018 | Slope and aspect were acquired through the 3D analysis of DEM, which are used as the model’s constraints. | Geospatial data cloud (http://www.gscloud.cn/search) [51] | Raster, 30 m | |
Road map | 2020 | The arterial road map was acquired as a constraint of the model. | Open Street Map (https://www.openstreetmap.org/) [52] | Shapefile (line) | |
River chart | 2020 | The major river chart was acquired as a constraint of the model. | Open Street Map (https://www.openstreetmap.org/) [52] | Shapefile (line) | |
Residential areas | 2020 | Major cities and towns were acquired as rated constraints of the model. | Open Street Map (https://www.openstreetmap.org/) [52] | Shapefile (point) | |
Natural reserve | 2020 | Used as the constraint of the model. | The World Database on Protected Areas (WDPA) [53] | Shapefile (polygon) | |
Administrative map | 2020 | Running boundary of the model. | BIGEMAP software ver. 30.0.9.14 [54] | Shapefile (polygon) | |
Numerical data | Population data | 1998–2018 | Input data of the SD model. Include information on urban and rural population numbers, change rates, and carrying capacities. | Statistical Yearbook of Ya’an City [55] Statistical Yearbook of Sichuan Province [56] | |
Industrial output value | 1998–2018 | Input data of the SD model. Encompass the value-added of primary, secondary, and tertiary industries along with their growth rates, as well as land value-added information | Statistical Yearbook of Ya’an City [55] Statistical Yearbook of Sichuan Province [56] | ||
Agricultural and grain data | 1998–2018 | Input data of the SD model. Comprise production quantities, per capita consumption levels, and demand figures for grains and livestock meat products. | Statistical Yearbook of Ya’an City [55] Statistical Yearbook of Sichuan Province [56] | ||
Housing and construction information | 1998–2018 | Input data of the SD model. Contain urbanization rates, housing areas, housing demands, land usage areas, etc. | China National Land and Resources Statistical Yearbook [57] China Urban Construction Statistical Yearbook [58] |
Classification of Driving Factors | Number of Variables Considered | Variable Names |
---|---|---|
Socioeconomic factors | 69 |
|
Spatial factors | 6 | Elevation, slope, aspect, distance to main roads, distance to rivers, and distance to administrative centers |
Output Variables | The Year 2018 | The Year 2028 | The Year 2038 |
---|---|---|---|
Area of forest land (ha) | 537,953 | 570,353 | 603,948 |
Area of grassland (ha) | 40,279 | 39,382 | 39,699 |
Area of cultivated land (ha) | 55,090 | 50,661 | 51,809 |
Area of construction land (ha) | 11,447 | 16,488 | 22,806 |
Cultivated Land | Forest Land | Grassland | Water Surface | Construction Land | Other Land | |
---|---|---|---|---|---|---|
Cultivated land | 1 | 1 | 1 | 1 | 1 | 0 |
Forest land | 1 | 1 | 1 | 1 | 1 | 1 |
Grassland | 1 | 1 | 1 | 1 | 1 | 1 |
Water surface | 0 | 0 | 0 | 1 | 1 | 0 |
Construction land | 0 | 0 | 0 | 0 | 1 | 0 |
Other land | 0 | 0 | 0 | 0 | 0 | 1 |
Cultivated Land | Forest Land | Grassland | Water Surface | Construction Land | Other Land | |
---|---|---|---|---|---|---|
Actual number of cells | 185,868 | 1,403,516 | 233,992 | 11,520 | 18,986 | 3159 |
Simulation number of cells SD-ANN-CA model (ha) | 190,987 | 1,404,623 | 234,522 | 10,934 | 18,951 | 3145 |
Error-accuracy of SD-ANN-CA model | 2.75% | 0.08% | 0.23% | 5.08% | 0.18% | 0.44% |
Kappa coefficient of SD-ANN-CA model | 0.931 | 0.998 | 0.994 | 0.873 | 0.996 | 0.989 |
Error-accuracy of ANN-CA model [49] | 3.25% | 3.57% | 4.35% | 7.34% | 3.78% | 5.56% |
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Zhao, J.; Zhu, X.; Zhang, F.; Gao, L. A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China. Land 2024, 13, 714. https://doi.org/10.3390/land13050714
Zhao J, Zhu X, Zhang F, Gao L. A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China. Land. 2024; 13(5):714. https://doi.org/10.3390/land13050714
Chicago/Turabian StyleZhao, Jingyao, Xiaofan Zhu, Fan Zhang, and Lei Gao. 2024. "A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China" Land 13, no. 5: 714. https://doi.org/10.3390/land13050714
APA StyleZhao, J., Zhu, X., Zhang, F., & Gao, L. (2024). A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China. Land, 13(5), 714. https://doi.org/10.3390/land13050714