Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China
Abstract
:1. Introduction
- (1)
- How can the effects of various land uses on the development of carbon stock patterns be taken into account, in order to then evaluate the spatial connection of carbon stocks in coastal urban agglomerations?
- (2)
- How can the influencing factors for the growth of each land use type contribute?
- (3)
- How can the interaction between low-carbon ecological and economic development be balanced and well-coordinated?
2. Materials and Methods
2.1. Study Area
2.2. Research Methods
2.2.1. A Carbon Stock Calculation Method using the InVEST Model
2.2.2. A Grid-Based Approach to Spatial Correlation Analysis
2.2.3. A Patch-Generating Land Use Simulation (PLUS)-Based Approach to Sprawl Research
2.3. Data Source and Data Preprocessing
2.3.1. Land Use Data
2.3.2. Carbon Pool Data
2.3.3. Data and Parameter Settings for the PLUS Model
3. Results
3.1. Features of Land Use Change in the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration
3.2. Analysis of the Evolution of Spatial and Temporal Patterns of Carbon Stocks in the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration
3.3. Spatial Correlation of Carbon Stocks in the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration
3.4. Carbon Stock Projections for Land Use in the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration under Different Development Scenarios
4. Discussion
4.1. Interpretation of Research
4.2. Contributions and Limitations
4.3. Proposals for Future Development
5. Conclusions
- (1)
- Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration as an example, we studied land use from 2000 to 2020 under two scenarios in which the area of cultivated land, woodland, and grassland continues to decrease, and the area of watershed and construction land continues to increase until 2060. Under the urban development priority scenario, more woodland is converted to water and unused land, while reservoirs and ponds are built to provide quality urban living, and the area of hard-to-use land, such as sandy and bare rock areas, increases. An ecological development scenario with greater mitigation of forest degradation should be prioritized to improve the ecological resilience of the study area, in terms of increasing vegetation cover, improving landscape patterns, and increasing species diversity. Cultivated land, woodland, and grassland areas are heavily used in the process of urbanization, and the change in land use type in the process of ecological degradation negatively affects the overall carbon stock of the regional environment.
- (2)
- The study found that the distance to trunk roads was the most important potential driver of land use change in cultivated land; land use change in forests was mainly influenced by slope, population size, and GDP factors; and land use change in grassland was more influenced by elevation and slope. The analysis of the spatial heterogeneity of carbon stock changes under different scenarios showed that a massive conversion of woodland to building land and of watershed and unused land were the main drivers of carbon stock decline; under the ecological priority scenario, the conversion of cropland, woodland, and grassland to building land was the main driver of carbon stock decline.
- (3)
- The spatial correlation analysis of carbon stocks in the Xiamen–Zhangzhou–Quanzhou urban agglomeration shows that the hot spots are mainly located in the northern part of Quanzhou City, the northwestern part of Xiamen City, and the western part of Zhangzhou City. The cold spots of carbon storage are concentrated in the eastern bay of Quanzhou City, Xiamen Island and its surroundings, and the southeastern port area of Zhangzhou City, which are the main development areas of the three cities. Overall, the hot spot areas of the study area have a predominantly westerly ecological barrier complemented by the seashore forest, while the cold spot areas are concentrated along the estuary and bay.
- (4)
- A comparison of the carbon stock patterns of the two scenarios for the Xiamen–Zhangzhou–Quanzhou urban agglomeration in 2060 shows that, under the urban development priority scenario, the difference between the predicted carbon storage values is 12,134,862.88 Mg. The low-carbon-stock areas are concentrated in the eastern bay of Quanzhou city, Xiamen Island and its surroundings, and the middle of Zhangzhou city; in the ecological development priority scenario, Zhangpu County, Hua’an County, Yunxiao County, and Dehua County are the concentrated areas with high carbon storage value. Dehua, Yongchun, Hua’an, and Nanjing counties are important ecological safety barriers for maintaining ecosystem functions in the region; Huli and Fengze counties have significant carbon losses; and Zhangpu county is a potential area for maintaining carbon storage in the urban agglomeration in the future.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Land Use Type | Carbon Density (Mg/ha) | ||||
---|---|---|---|---|---|
Cabove | Cbelow | Csoil | Cdead | Ctot | |
Cultivated land | 11.80 | 4.34 | 106.87 | 2.20 | 125.21 |
Woodland | 132.42 | 51.97 | 120.68 | 14.73 | 319.80 |
Grassland | 6.31 | 4.84 | 111.81 | 3.48 | 126.44 |
Water | 8.43 | 4.10 | 6.50 | 0 | 19.03 |
Construction land | 1.37 | 0.31 | 1.42 | 0 | 3.10 |
Unused land | 0.36 | 0.53 | 21.95 | 0 | 22.84 |
Sea | 0 | 0 | 0 | 0 | 0 |
Type | Data | Meaning | Source |
---|---|---|---|
Land use | Land use data | Land use classification | Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) |
Constraints | Water area | Land use constraints | Extraction of water from land use data for overlay analysis |
Socioeconomic | Population | Degree of population aggregation | Resource and Environment Science and Data Center [64] |
GDP | Level of economic development | Resource and Environment Science and Data Center [65] | |
Distance to adjacent trunk roads | Land value | Open street map; calculated using Euclidean distances | |
Distance to adjacent secondary roads | Regional development attractiveness | ||
Distance to adjacent tertiary roads | Access convenience | ||
Public Administration | Distance to adjacent railways | Inter-regional transportation development level | National Catalogue Service for Geographic Information; calculated using Euclidean distances. |
Distance to adjacent highways | Logistics and distribution potential | ||
Distance to adjacent stations | Land development possibility | ||
Distance to government | Administrative and public service area development potential | ||
Climatic | Average annual temperature | Suitability of temperature | China Meteorological Data Service Centre |
Average annual precipitation | Types of crop cultivation and risk of rainwater flooding | The land component of the 5th generation of European ReAnalysis (ERA5-Land) dataset published by the EU and organizations such as the European Centre for Medium-Range Weather Forecasts | |
Geographic-environmental | Soil type | Land base condition | The dataset is provided by the National Cryosphere Desert Data Center [66] |
Elevation | Availability of water resources | Geospatial Data Cloud | |
Slope | Soil erosion and construction suitability | Calculated from Digital Elevation Model (DEM) with the ArcGIS slope tool | |
Adjacent open waters | Capacity to provide water for living and production | National Catalogue Service For Geographic Information |
Land Use Type | Cultivated Land | Woodland | Grassland | Water | Construction Land | Unused Land |
---|---|---|---|---|---|---|
Cultivated land | 1 | 1 | 1 | 1 | 1 | 1 |
Woodland | 1 | 1 | 1 | 1 | 1 | 1 |
Grassland | 1 | 1 | 1 | 1 | 1 | 1 |
Water | 0 | 0 | 0 | 1 | 0 | 0 |
Construction land | 0 | 0 | 0 | 0 | 1 | 0 |
Unused land | 1 | 1 | 1 | 1 | 1 | 1 |
Land Use Type | Cultivated land | Woodland | Grassland | Water | Construction land | Unused land |
---|---|---|---|---|---|---|
Neighborhood Weight | 0.0911 | 0.1230 | 0.0899 | 0.0769 | 0.6162 | 0.0029 |
Cultivated Land | Woodland | Grassland | Water | Construction Land | Unused Land | Sea | |
---|---|---|---|---|---|---|---|
2000 | 25.56% | 51.25% | 16.17% | 1.74% | 5.19% | 0.09% | 0.00% |
2010 | 22.48% | 49.77% | 15.65% | 2.17% | 9.71% | 0.10% | 0.13% |
2015 | 22.18% | 49.73% | 15.62% | 2.16% | 10.19% | 0.10% | 0.03% |
2020 | 21.58% | 49.56% | 15.58% | 2.17% | 11.01% | 0.11% | 0.00% |
2020 2000 | Cultivated Land | Woodland | Grassland | Water | Construction Land | Unused Land | Sea | Total | Transfer From |
---|---|---|---|---|---|---|---|---|---|
Cultivated land | 522,413.28 | 14,830.83 | 5186.16 | 5163.93 | 97,795.62 | 30.06 | 3.69 | 645,423.57 | 123,010.29 |
Woodland | 13,980.78 | 1,222,437.60 | 17,334.99 | 2260.71 | 38,213.01 | 111.15 | 31.14 | 1,294,369.38 | 71,931.78 |
Grassland | 5296.05 | 15,274.98 | 370,592.10 | 719.28 | 16,457.40 | 169.02 | 0.00 | 408,508.83 | 37,916.73 |
Water | 906.57 | 648.72 | 235.53 | 32,641.29 | 8824.23 | 571.23 | 4.14 | 43,831.71 | 11,190.42 |
Construction land | 3650.85 | 1456.29 | 658.62 | 12,007.35 | 113,341.68 | 0.09 | 0.54 | 131,115.42 | 17,773.74 |
Unused land | 28.44 | 51.39 | 153.09 | 36.09 | 135.54 | 1852.65 | 0.00 | 2257.20 | 404.55 |
Total | 546,275.97 | 1,254,699.81 | 394,160.49 | 52,828.65 | 274,767.48 | 2734.20 | 39.51 | 2,525,506.11 | 262,227.51 |
Transfer to | 23,862.69 | 32,262.21 | 23,568.39 | 20,187.36 | 161,425.80 | 881.55 | 39.51 | 262,227.51 |
Period Land Use Type | 2000 | 2010 | 2015 | 2020 |
---|---|---|---|---|
Carbon Stock/Mg Percentage/% | ||||
Cultivated land | 80,828,337.02 14.76 | 71,178,744.21 13.54 | 70,222,217.45 13.40 | 68,410,088.19 13.12 |
Woodland | 413,958,518.04 75.58 | 402,456,107.75 76.58 | 402,113,371.67 76.71 | 401,311,677.80 76.94 |
Grassland | 51,653,789.05 9.43 | 50,047,740.65 9.52 | 49,940,021.36 9.53 | 49,874,122.09 9.56 |
Water | 836,897.10 0.15 | 1,042,499.87 0.20 | 1,037,014.09 0.20 | 1,046,509.30 0.20 |
Construction land | 406,744.87 0.07 | 760,751.49 0.14 | 798,917.57 0.15 | 864,107.31 0.17 |
Unused land | 51,560.61 0.01 | 55,949.32 0.01 | 55,324.42 0.01 | 62,449.13 0.01 |
Total | 547,735,846.70 100.00 | 525,541,793.28 100.00 | 524,166,866.56 100.00 | 521,568,953.83 100.00 |
Land Use Type | Type | 2020 | 2060 (Urban Priority Development Scenario) | Amount of Change | 2060 (Ecological Priority Development Scenario) | Amount of Change |
---|---|---|---|---|---|---|
Cultivated land | Area | 546,362.82 | 410,051.16 | −136,311.66 | 410,051.16 | −136,311.66 |
Carbon stocks | 68,410,088.69 | 51,342,505.74 | −17,067,582.95 | 51,342,505.74 | −17,067,582.95 | |
Woodland | Area | 1,254,883.23 | 1,184,275.62 | −70,607.61 | 1,224,632.70 | −30,250.53 |
Carbon stocks | 401,311,656.95 | 378,731,343.28 | −22,580,313.68 | 391,637,537.46 | −9,674,119.49 | |
Grassland | Area | 394,448.94 | 367,344.00 | −27,104.94 | 367,344.00 | −27,104.94 |
Carbon stocks | 49,874,123.97 | 46,446,975.36 | −3,427,148.61 | 46,446,975.36 | −3,427,148.61 | |
Water | Area | 54,992.61 | 95,132.07 | 40,139.46 | 55,650.60 | 657.99 |
Carbon stocks | 1,046,509.37 | 1,810,363.29 | 763,853.92 | 1,059,030.92 | 12,521.55 | |
Construction land | Area | 278,744.31 | 468,798.12 | 190,053.81 | 468,798.12 | 190,053.81 |
Carbon stocks | 864,107.36 | 1,453,274.17 | 589,166.81 | 1,453,274.17 | 589,166.81 | |
Unused land | Area | 2734.20 | 3153.69 | 419.49 | 2278.08 | −456.12 |
Carbon stocks | 62,449.13 | 72,030.28 | 9581.15 | 52,031.35 | −10,417.78 | |
Sea | Area | 43.11 | 0.00 | −43.11 | 0.00 | −43.11 |
Carbon stocks | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Total | Area | 2,532,209.22 | 2,528,754.66 | −3454.56 | 2,528,754.66 | −3454.56 |
Carbon stocks | 521,568,935.48 | 479,856,492.12 | −41,712,443.35 | 491,991,355.00 | −29,577,580.48 |
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Zeng, S.; Liu, X.; Tian, J.; Zeng, J. Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China. Land 2024, 13, 476. https://doi.org/10.3390/land13040476
Zeng S, Liu X, Tian J, Zeng J. Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China. Land. 2024; 13(4):476. https://doi.org/10.3390/land13040476
Chicago/Turabian StyleZeng, Suiping, Xinyao Liu, Jian Tian, and Jian Zeng. 2024. "Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China" Land 13, no. 4: 476. https://doi.org/10.3390/land13040476
APA StyleZeng, S., Liu, X., Tian, J., & Zeng, J. (2024). Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China. Land, 13(4), 476. https://doi.org/10.3390/land13040476