Optimization and Simulation of Mountain City Land Use Based on MOP-PLUS Model: A Case Study of Caijia Cluster, Chongqing
Abstract
:1. Introduction
2. Study Area and Data Used
2.1. Study Area
2.2. Data Sources and Preprocessing
3. Methodology
3.1. MOP Model
3.1.1. Development Scenario Setting
3.1.2. Decision Variables and Constraints
3.1.3. Objective Function Construction
3.2. PLUS Model
3.2.1. Overview of the PLUS Model
3.2.2. Rules and Parameter Settings
3.2.3. Accuracy Validation
4. Results
4.1. Accuracy Validation Results
4.2. Land Use Structure Optimization Results
4.2.1. Economic Priority Scenario
4.2.2. Ecological Priority Scenario
4.2.3. Balanced Development Scenario
4.3. Spatial Layout Optimization Results
4.3.1. Economic Priority Scenario
4.3.2. Ecological Priority Scenario
4.3.3. Balanced Development Scenario
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Category | Data/Material | Resolution | Source | Data Usage | |
---|---|---|---|---|---|
Socio-economic data | Economic statistical data | Chongqing Statistics Bureau | Calculation of economic efficiency factor | ||
Density of population | 1 km | Resource and Environment Science and Data Center | Socio-economic drivers | ||
GDP | 1 km | ||||
Chongqing Land Use Master Planning (2006–2020) | Chongqing Planning and Natural Resources Bureau | Reference material | |||
Territorial Spatial Planning of Chongqing Municipality (2021–2035) | |||||
Current Land Use Classification (GB/T21010-2017) | |||||
Land use/cover data | Landsat 8 | LC08_L2SP_128039_20150708 | 30 m | GEE | PLUS model input |
LC08_L2SP_128039_20160710 | |||||
LC08_L2SP_128039_20170915 | |||||
LC08_L2SP_128039_20180902 | |||||
Fundamental geographic data | DEM | 30 m | Geospatial Data Cloud | Natural factor drivers | |
Soil types | 1 km | Resource and Environment Science and Data Center | |||
Transportation networks | Open Street Map | Traffic accessibility drivers | |||
Railway stations | Chongqing Geographic Information Center | ||||
Administrative division | Relevant analysis | ||||
Other data | NPP | 1 km | Resource and Environment Science and Data Center | Modification of the ecological efficiency factor | |
NDVI | 1 km |
Economic Sectors | The Secondary and Tertiary Sectors | Forestry | Farming | Husbandry | Tea Gardens and Orchards | Fishery | |
---|---|---|---|---|---|---|---|
Year | |||||||
2015 | 2,417,527.74 | 573.76 | 12,560.73 | 8846.65 | 774.47 | 4695.26 | |
2016 | 2,680,109.26 | 676.52 | 14,493.15 | 7465.13 | 906.15 | 5185.99 | |
2017 | 2,868,781.64 | 1010.50 | 14,309.11 | 7324.87 | 1204.15 | 6985.34 | |
2018 | 2,866,303.04 | 1224.59 | 15,551.38 | 7481.78 | 1325.44 | 7324.61 |
Constraint | Formula | Description |
---|---|---|
Total area | , S = 8770.69 (ha) | The total area of the study area should remain unchanged. |
Planning population in 2035 | Based on the urban and farmland population density and total population in Chongqing from 2015 to 2018, the values in 2035 are projected using the GF model as follows: = 75.36, = 4.36 (persons per hectare), = 487,787. By 2035, the total population is not to be larger than 487,787. | |
Ecological environment | With ecological environment protection as a constraint, it is ensured that the areas of forest, cropland, grassland, and shrubland are within an appropriate range . The value 5718.28 represents the total area of , , , and in 2018. | |
Flexible planning | The elasticity coefficient is set within the interval [0.75, 1.25]. It was determined based on the proportions of cropland, forest, grassland, and shrubland as specified in the “Land Use Planning”, taking into account historical trends and the length of the forecasting period. | |
Policies | According to “Spatial Planning”, the urban and rural construction land is projected to increase by 17.64% in 2035, with a stable increase in the area of shrubland. Therefore, the area of construction land should be between 2136.72 and 2513.63 (ha), with the shrubland area not being less than 173.26 (ha). | |
Land development utilization rate | ≤ 309.85 | The land development utilization rate () in 2035 should not be lower than that in 2018; the bare land area should be greater than 309.85 (ha). is calculated using the following formula: = ((S − )/S) × 100%. |
Decision variable non-negative | The area of each land use type should not be less than 0. |
Category | Driver |
---|---|
Natural factor | Elevation |
Slope | |
Soil Types | |
Social factor | GDP |
Population density | |
Traffic accessibility factor | Distance to Train Station |
Distance to Highway | |
Distance to Railway | |
Distance to Main Road | |
Distance to Primary Road | |
Distance to Secondary Road | |
Distance to Tertiary Road |
Land Type | Total Pixel | Correctly Simulated Pixels | Accuracy (%) |
---|---|---|---|
Impervious surface | 20,648 | 15,863 | 76.83 |
Forest | 12,694 | 10,161 | 80.05 |
Cropland | 44,360 | 35,204 | 79.36 |
Grassland | 999 | 887 | 88.79 |
Shrubland | 1627 | 1346 | 82.73 |
Water | 14,457 | 13,507 | 93.43 |
Bare land | 2987 | 2092 | 70.04 |
Variable Symbol | Variable Name | Status in 2018 | Different Development Scenarios in 2035 | |||||
---|---|---|---|---|---|---|---|---|
Econ. Prior. Scenario | Ecol. Prior. Scenario | BD Scenario | ||||||
Area (ha) | Area (ha) | Change from 2018 (ha) | Area (ha) | Change from 2018 (ha) | Area (ha) | Change from 2018 (ha) | ||
Impervious surface | 2136.72 | 2513.63 | 376.91 | 2202.81 | 66.09 | 2401.83 | 265.11 | |
Forest | 856.36 | 685.71 | 170.65 | 933.43 | 77.07 | 793.44 | 62.92 | |
Cropland | 4601.00 | 4574.88 | 26.12 | 4536.90 | 64.10 | 4561.83 | 39.17 | |
Grassland | 87.66 | 88.56 | 0.90 | 84.33 | 3.33 | 74.79 | 12.87 | |
Shrubland | 173.26 | 224.36 | 51.10 | 216.58 | 43.32 | 237.61 | 64.35 | |
Water | 605.84 | 598.23 | 7.61 | 624.24 | 18.40 | 608.22 | 2.38 | |
Bare land | 309.85 | 85.32 | 224.53 | 171.36 | 138.49 | 92.97 | 216.88 | |
Economic benefit (104 CNY) | 3.47 × 106 | 4.06 × 106 | 5.90 × 105 | 3.50 × 106 | 1.15 × 105 | 3.89 × 106 | 4.2 × 105 | |
Ecological benefit (104 CNY) | 7.23 × 103 | 6.91 × 103 | −321.58 | 7.46 × 103 | 233.17 | 7.16 × 103 | −71.56 |
Administrative Division | Impervious Surface Area in 2018 (ha) | Different Development Scenarios in 2035 | |||||
---|---|---|---|---|---|---|---|
Econ. Prior. Scenario | Ecol. Prior. Scenario | BD Scenario | |||||
Area (ha) | Change from 2018 (ha) | Area (ha) | Change from 2018 (ha) | Area (ha) | Change from 2018 (ha) | ||
Shijialiang Town | 217.53 | 264.96 | 47.43 | 289.98 | 72.45 | 277.92 | 60.39 |
Caijiagang Subdistrict | 1408.14 | 1624.68 | 216.54 | 1428.75 | 20.61 | 1522.53 | 114.39 |
Tongjiaxi Town | 511.05 | 623.99 | 112.94 | 484.08 | 26.97 | 601.38 | 90.33 |
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Zhong, Y.; Zhang, X.; Yang, Y.; Xue, M. Optimization and Simulation of Mountain City Land Use Based on MOP-PLUS Model: A Case Study of Caijia Cluster, Chongqing. ISPRS Int. J. Geo-Inf. 2023, 12, 451. https://doi.org/10.3390/ijgi12110451
Zhong Y, Zhang X, Yang Y, Xue M. Optimization and Simulation of Mountain City Land Use Based on MOP-PLUS Model: A Case Study of Caijia Cluster, Chongqing. ISPRS International Journal of Geo-Information. 2023; 12(11):451. https://doi.org/10.3390/ijgi12110451
Chicago/Turabian StyleZhong, Yuqing, Xiaoxiang Zhang, Yanfei Yang, and Minghui Xue. 2023. "Optimization and Simulation of Mountain City Land Use Based on MOP-PLUS Model: A Case Study of Caijia Cluster, Chongqing" ISPRS International Journal of Geo-Information 12, no. 11: 451. https://doi.org/10.3390/ijgi12110451
APA StyleZhong, Y., Zhang, X., Yang, Y., & Xue, M. (2023). Optimization and Simulation of Mountain City Land Use Based on MOP-PLUS Model: A Case Study of Caijia Cluster, Chongqing. ISPRS International Journal of Geo-Information, 12(11), 451. https://doi.org/10.3390/ijgi12110451