Coupling the Calibrated GlobalLand30 Data and Modified PLUS Model for Multi-Scenario Land Use Simulation and Landscape Ecological Risk Assessment
Abstract
:1. Introduction
2. Study Area and Data
2.1. The Study Area
2.2. Data
3. Methodology
3.1. The Modified PLUS Model
3.1.1. The PLUS Model
3.1.2. The Modified PLUS Model by Parameter Sensitivity Analysis
3.1.3. Model Validation
3.2. GlobalLand30 Data Calibration
3.3. Land Use Prediction and Multi-Scenario Design
3.4. The Landscape Ecological Risk Index
4. Results
4.1. Calibrated GLC30 Data for PLUS Simulation
4.2. Simulation after Modifying the PLUS Model
4.3. Land Use Simulation and Prediction under Multiple Scenarios
4.4. Spatiotemporal Distribution Characteristics of Landscape Ecological Risks
5. Discussion
5.1. Comparison of Driver Contributions
5.2. Strengths and Limitations
6. Conclusions
- (1)
- The impervious surface correction of GLC30 based on the NDISI significantly improved the connectivity of independent township settlements. The calibrated simulation accuracy was enhanced to greater than 0.86 based on PLUS simulation.
- (2)
- The modified PLUS model by the sensitivity analysis increased the kappa coefficient and the FoM value by more than 1.4% and 3%, respectively, and the overall accuracy reached 87.6%, effectively improving the accuracy of the PLUS simulation.
- (3)
- Based on the modified PLUS model simulation, the cultivated land in three scenarios showed a significant reduction trend, decreasing by 61%, 66.42%, and 45.5%, while the built-up land increased by 63.42%, 86.79%, and 38.9%. Only under the SSP126-EP scenario can both urban construction and the protection of cultivated land be possible.
- (4)
- According to the LER index analysis, the LER in the past 20 years has been mainly lower, moderate, or higher, and the overall level of LER has shown a downward trend. However, under SSP245-ND and SSP585-EG, the overall ecological risk shows the lowest, lower, and moderate levels.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Data | Resolution | Meaning | Data Source |
---|---|---|---|---|
Land use | Land use classification data in 2000, 2010 and 2020 | 30 m | 1 Cultivated land; 2 woodland; 3 grassland; 4 water bodies; 5 built-up land; 6 unused land | 30 m Global land cover data http://www.GlobalLandcover.com/, accessed on 1 September 2022 |
Limiting factor | Fixed rivers, reservoirs, lakes, and slopes greater than 25° in the city | 30 m | The area is off limits to development | GlobeLand30 and ASTER GDEM v3 |
Climate and environmental data | Mean annual precipitation (mm) | 30 m | The average annual precipitation at the location corresponding to the pixel | Resources and Environmental Science and Data Center, CAS [36] http://www.resdc.cn/, accessed on 1 September 2022 |
Mean annual temperature (°C) | 30 m | The average annual temperature at the location corresponding to the pixel | ||
Elevation (m) | 30 m | Topographic elevation condition | ASTER GDEM v3 https://earthdata.nasa.gov/, accessed on 1 September 2022 | |
Slope (°) | 30 m | Topographic slope condition | ||
Social economy data | GDP (10,000 yuan/km2) | 30 m | The GDP value of each pixel location | Resources and Environmental Science and Data Center, CAS [37] http://www.resdc.cn/, accessed on 1 September 2022 |
The number of people/persons | 30 m | The number of people in each pixel’s location | WorldPop https://www.worldpop.org/, accessed on 1 September 2022 | |
The distance to the main road (m), the primary road (m), the secondary road (m), the tertiary road (m), the motorway road (m) and the rail road (m) | 30 m | The nearest Euclidean distance from the pixel geometric center to the road | OpenStreetMap https://www.openstreetmap.org/, accessed on 1 September 2022 |
Type | Year | Overall | Kappa | |
---|---|---|---|---|
GLC30 | 2010 | 0.814 | 0.782 | 0.153 |
2020 | 0.825 | 0.751 | 0.179 | |
GLC30-NDISI | 2010 | 0.86 | 0.797 | 0.155 |
2020 | 0.862 | 0.8 | 0.18 |
Type | Year | Overall | Kappa | |
---|---|---|---|---|
Traditional | 2010 | 0.86 | 0.797 | 0.155 |
2020 | 0.862 | 0.8 | 0.18 | |
After correction | 2010 | 0.867 | 0.826 | 0.224 |
2020 | 0.876 | 0.814 | 0.21 |
Category | 2020 | 2040 | 2060 | 2080 | 2100 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
EG | EP | ND | EG | EP | ND | EG | EP | ND | EG | EP | ND | ||
Lowest | 11.47 | 11.77 | 11.47 | 11.58 | 19.96 | 11.63 | 14.95 | 25.82 | 12.34 | 25.87 | 30.49 | 14.15 | 21.41 |
Lower | 31.98 | 30.06 | 31.94 | 30.22 | 34.82 | 29.62 | 33.26 | 34.78 | 29.97 | 34.80 | 33.42 | 31.09 | 36.22 |
Moderate | 23.21 | 24.40 | 22.99 | 24.20 | 24.08 | 24.38 | 24.89 | 21.93 | 25.11 | 21.82 | 20.26 | 25.64 | 23.28 |
Higher | 20.76 | 21.36 | 20.81 | 21.22 | 15.73 | 21.50 | 19.37 | 12.91 | 21.29 | 12.96 | 11.68 | 20.67 | 14.17 |
Highest | 12.57 | 12.41 | 12.80 | 12.77 | 5.40 | 12.87 | 7.53 | 4.56 | 11.29 | 4.56 | 4.14 | 8.45 | 4.92 |
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Wang, Z.; Guo, M.; Zhang, D.; Chen, R.; **, C.; Yang, H. Coupling the Calibrated GlobalLand30 Data and Modified PLUS Model for Multi-Scenario Land Use Simulation and Landscape Ecological Risk Assessment. Remote Sens. 2023, 15, 5186. https://doi.org/10.3390/rs15215186
Wang Z, Guo M, Zhang D, Chen R, ** C, Yang H. Coupling the Calibrated GlobalLand30 Data and Modified PLUS Model for Multi-Scenario Land Use Simulation and Landscape Ecological Risk Assessment. Remote Sensing. 2023; 15(21):5186. https://doi.org/10.3390/rs15215186
Chicago/Turabian StyleWang, Zongmin, Mengdan Guo, Dong Zhang, Ruqi Chen, Chaofan **, and Haibo Yang. 2023. "Coupling the Calibrated GlobalLand30 Data and Modified PLUS Model for Multi-Scenario Land Use Simulation and Landscape Ecological Risk Assessment" Remote Sensing 15, no. 21: 5186. https://doi.org/10.3390/rs15215186