A Coupled InVEST-PLUS Model for the Spatiotemporal Evolution of Ecosystem Carbon Storage and Multi-Scenario Prediction Analysis
<p>Location (<b>A</b>) and topographical map (<b>B</b>) of Chengdu Urban Agglomeration.</p> "> Figure 2
<p>Decision framework diagram of this study.</p> "> Figure 3
<p>Spatial–temporal evolution of land use in Chengdu Urban Agglomeration from 2000 to 2020 ((<b>A</b>) 2000; (<b>B</b>) 2010; (<b>C</b>) 2020).</p> "> Figure 4
<p>Sankey diagram of land use evolution in Chengdu Urban Agglomeration from 2000 to 2020.</p> "> Figure 5
<p>Spatial–temporal evolution of carbon storage in Chengdu Urban Agglomeration from 2000 to 2020 ((<b>A</b>) 2000; (<b>B</b>) 2010; (<b>C</b>) 2020).</p> "> Figure 6
<p>Future spatial evolution of land use in Chengdu Urban Agglomeration under different scenarios ((<b>A</b>) NDS; (<b>B</b>) UDS; (<b>C</b>) FPS; (<b>D</b>) EPS).</p> "> Figure 7
<p>Overall changes in land use types in Chengdu Urban Agglomeration under different scenarios.</p> "> Figure 8
<p>Spatial evolution of carbon storage in Chengdu Urban Agglomeration under different scenarios ((<b>A</b>) NDS; (<b>B</b>) UDS; (<b>C</b>) FPS; (<b>D</b>) EPS).</p> "> Figure 9
<p>Contributions of driving factors affecting land use types in Chengdu Urban Agglomeration ((<b>A</b>) biophysical factors; (<b>B</b>) socio-economic factors; (<b>C</b>) climate factors; (<b>D</b>) landscape factors).</p> ">
Abstract
:1. Introduction
2. Study Area
3. Decision Framework and Data Sources
3.1. Design of Decision Framework
3.2. Data Sources
4. Methods
4.1. Land Use Change Dynamics
4.2. Carbon Storage Calculation Based on the InVEST Model
4.3. Land Use Change Prediction Based on the PLUS Model
4.3.1. Land Expansion Analysis Strategy
4.3.2. Cellular Automaton Model Based on Multi-Class Stochastic Patch Seeds
- (1)
- Macro Demand and Local Competition Feedback Mechanism. This feedback mechanism primarily achieves the generation of multi-type random patch seeds, thereby simulating the calculation of the overall probability for land use type k [37]. The formula is as follows:
- (2)
- Multi-type Random Patch Seed Threshold Decrease. The PLUS model evolves patches of multiple land use types by calculating the overall probability process through a threshold-decreasing trend of multi-type random patch seeds [18,38]. When the neighborhood effect of land use type k is equal to 0, the overall probability is given by
4.4. Setting of Multiple Scenarios for Land Use
- (1)
- NDS: Employing a land use transfer matrix alongside the Markov model for the period 2000–2020, with a projection interval of 30 years, this scenario forecasts the area of each land use category within the research region for the year 2050 under the NDS. Reflecting the ongoing urbanization trend and without imposing limitations on the interchange among various land categories, this scenario establishes a foundation for modeling land use transformations in urban clusters.
- (2)
- UDS: Considering the requirements of urban development, this scenario increases the probability of conversion from farmland and grassland to construction land while taking into account natural laws and the requirements of the “Chengdu Urban Agglomeration Development Plan.” An urban development boundary is designated as a restrictive conversion area.
- (3)
- FPS: Ensuring the source of food security, this scenario protects farmland. Under the premise of maintaining the total planned area of farmland in the Chengdu Urban Agglomeration, the scenario implements policies for farmland occupation and replenishment, achieving the target of supplementary farmland area. The expansion of construction land is regulated in accordance with the overarching plan while also ensuring the increase in water bodies essential for agricultural use.
- (4)
- EPS: Considering the development of the ecological environment, this scenario is based on the growth rate of vegetation in ecological land as specified in the overall plan for land use in the Chengdu Urban Agglomeration. Building upon the EPS, it is possible to reduce the probability of farmland conversion to construction land, lower the probability of forest and grassland conversion to construction land, and moderately slow down the expansion of construction land. Natural reserves within the Chengdu Urban Agglomeration are designated as restricted areas.
5. Results
5.1. Spatial–Temporal Evolution Characteristics of Land Use in Chengdu Urban Agglomeration from 2000 to 2020
5.2. Spatial–Temporal Evolution Characteristics of Carbon Storage in Chengdu Urban Agglomeration from 2000 to 2020
5.3. Spatial Evolution Characteristics of Future Land Use under Different Scenarios
5.4. Future Evolution Characteristics of Carbon Storage under Different Scenarios
6. Discussion
6.1. Response Relationship between Land Use Change and Carbon Storage
6.2. Characteristics and Trends of Carbon Storage in the Chengdu Urban Agglomeration Ecosystem
6.3. Suggestions for Future Land Spatial Planning
- (1)
- Optimize the spatial layout of land use to enhance carbon sequestration ecosystem services. At the macro level, it is crucial to refine the overall land use framework by strictly managing the “three zones and three lines” and curbing the expansion rate of built-up areas. For existing land, adjustments to inefficient industrial lands and the enhancement of historical and cultural blocks with new functions are advised to elevate efficiency. Regarding new land, the focus should be on augmenting the ecological value of lands like forests and grasslands, thereby increasing the total carbon storage in the Chengdu–Chongqing region. At the micro level, strategies include converting farmland back to forests or grasslands, conserving biodiversity, and fostering active spatial development and carbon cycling within the Chengdu metropolitan area.
- (2)
- Implement an ecological carbon sequestration compensation mechanism to promote regional coordinated coupling relationships. It is vital to acknowledge the developmental disparities among different regions, utilizing Chengdu’s central urban area’s developmental edge to harmonize and address diverse needs, shifting toward a new development paradigm. Promoting the growth of surrounding small and medium-sized towns, solidifying the ecological green base, and pioneering a green development route are also essential. Recommendations include fostering green and low-carbon industries, enhancing land spatial management and protection, and concentrating on ecological restoration efforts in areas like Longmenshan and Qionglai Mountain, alongside ecological corridor construction. Achieving these objectives necessitates overcoming entrenched interests and administrative hurdles, establishing a cost and benefit-sharing mechanism based on ecological carbon sequestration, and forming a metropolitan network spatial development model with multiple support points and complementary functions.
- (3)
- Leverage the role of multiple stakeholders in negotiation to construct an ecological security assurance system. On the softer aspects, a market-driven, government-guided multi-stakeholder negotiation system is recommended. This entails creating a significant ecological planning decision-making mechanism that unites diverse entities toward constructing the Chengdu metropolitan area. Concurrently, it is important to bolster source management in Chengdu’s metropolitan area and enhance risk assessments and mechanisms pertinent to the carbon pool of the ecological system. This involves tightening control over the ecological system’s carbon pool, improving its management effectiveness, and fostering a systematic and modern ecological system’s carbon pool security assurance system.
7. Conclusions and Limitations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Type | Data Name | Data Source | Explanation |
---|---|---|---|
Administrative Boundary | City Administrative Boundary Data | Resource and Environment Science Data Center (http://www.resdc.cn/data, accessed on 1 December 2023). | Referring to the administrative division map of Sichuan Province, there are four cross-administrative prefecture-level cities: Chengdu City, Deyang City, Meishan City, and Ziyang City. |
Land Use Type | 2000–2020 Land Use Type | Chinese Academy of Sciences Resource and Environmental Science Data Center (https://www.resdc.cn, accessed on 1 December 2023). | Spatial resolution is 30 m × 30 m, reclassified into six land use types: cropland, forest land, grassland, water area, built-up land, and unused land. |
Biophysical Factors | Digital Elevation Model (DEM) | The data are sourced from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn, accessed on 2 December 2023). | The data cover elevations ranging from 218 to 7100 m. |
Slope | Calculated based on DEM data using GIS platform. | The data range from 0° to 89.41°. | |
Normalized Difference Vegetation Index (NDVI) | Obtained from the Geospatial Cloud Platform (http://www.gscloud.cn, accessed on 2 December 2023). | It is a standardized method for measuring the health of vegetation in the study area. | |
Social and Economic Factors | Unit Gross Domestic Product (GDP) | Obtained from the Chinese Academy of Sciences Resource and Environmental Science Data Center (https://www.resdc.cn, accessed on 1 December 2023). | It reflects the total market value of all products produced by using production factors per unit area over a certain period. |
Population Density (POP) | WorldPop (https://www.worldpop.org/, accessed on 3 December 2023) | It represents the number of people per unit of land area and provides a measure of the population. | |
Nighttime Light | The first professional nighttime remote sensing satellite, “Luojiya-1” (http://59.175.109.173:8888/app/login.html, accessed on 3 December 2023). | It reflects the brightness of nighttime lights on the Earth’s surface, providing an indication of human activity intensity to some extent. | |
Road Density | Non-profit map service platform OpenStreetMap (http://www.openstreetmap.org, accessed on 2 December 2023). | It refers to the ratio of the total length of all roads in a certain area to the total area of that area. | |
Climate Factors | Annual Average Precipitation | National Earth System Science Data Center (http://www.geodata.cn/, accessed on 1 December 2023). | It is the average of the annual precipitation. |
Average Annual Temperature | National Earth System Science Data Center (http://www.geodata.cn/, accessed on 4 December 2023). | It refers to the arithmetic mean of the daily average temperatures throughout the year. | |
Annual Average Humidity | National Earth System Science Data Center (http://www.geodata.cn/, accessed on 4 December 2023). | It refers to the arithmetic mean of the daily average humidity throughout the year. | |
Annual Average Wind Speed | National Earth System Science Data Center (http://www.geodata.cn/, accessed on 4 December 2023) | It refers to the arithmetic mean of daily average wind speed throughout the year. | |
Landscape Factors | CONTAG | Calculated by the Fragstats4.2 software based on land use data. | It describes the degree of aggregation or dispersion of different patch types in the landscape. |
SHDI | Calculated by the Fragstats4.2 software based on land use data. | It reflects landscape heterogeneity. | |
PD | Calculated by the Fragstats4.2 software based on land use data. | It reflects the overall heterogeneity and fragmentation of the landscape as well as the degree of fragmentation of a specific land cover type. | |
LPI | Calculated by the Fragstats4.2 software based on land use data. | It refers to the proportion of the area of the largest patch in a specific land cover type to the total area of that type. | |
ED | Calculated by the Fragstats4.2 software based on land use data. | It reveals the degree to which the landscape or a specific land cover type is divided by boundaries. | |
Space Policy | Nature Reserve Range | Sichuan Provincial Geographical Information Public Service Platform (http://sichuan.tianditu.gov.cn/, accessed on 1 December 2023). | As a restrictive factor, this region involves policy constraints related to ecological reserves, and land types are set as unalterable. |
Land Use Type | Aboveground Carbon Density | Belowground Carbon Density | Soil Carbon Density | Dead Organic Matter Carbon Density |
---|---|---|---|---|
Cultivated Land | 38.70 | 80.70 | 92.90 | 1.00 |
Forest Land | 55.56 | 144.87 | 206.45 | 3.50 |
Grassland | 29.30 | 52.90 | 135 | 1.00 |
Water Area | 21.40 | 73.10 | 113 | 1.00 |
Construction Land | 3.30 | 87.30 | 115.30 | 0 |
Unused Land | 22.60 | 136.90 | 171.80 | 0 |
Multi-Scenario | Land Use Types | Cultivated Land | Forest Land | Grassland | Water Area | Unused Land | Construction Land |
---|---|---|---|---|---|---|---|
NDS | Farmland | 1 | 0 | 1 | 0 | 1 | 1 |
Forest Land | 0 | 1 | 1 | 1 | 1 | 1 | |
Grassland | 1 | 1 | 1 | 1 | 1 | 1 | |
Water Area | 0 | 0 | 1 | 1 | 0 | 0 | |
Unused Land | 0 | 1 | 1 | 1 | 1 | 1 | |
Construction Land | 0 | 1 | 1 | 1 | 1 | 1 | |
UDS | Farmland | 1 | 1 | 1 | 0 | 1 | 0 |
Forest Land | 0 | 1 | 1 | 1 | 1 | 0 | |
Grassland | 0 | 1 | 1 | 1 | 1 | 0 | |
Water Area | 0 | 0 | 1 | 1 | 0 | 0 | |
Unused Land | 0 | 0 | 0 | 0 | 1 | 0 | |
Construction Land | 1 | 1 | 1 | 1 | 1 | 1 | |
FPS | Farmland | 0 | 0 | 1 | 0 | 1 | 1 |
Forest Land | 0 | 1 | 1 | 0 | 0 | 0 | |
Grassland | 1 | 1 | 1 | 1 | 1 | 1 | |
Water Area | 0 | 0 | 1 | 1 | 0 | 0 | |
Unused Land | 0 | 0 | 0 | 0 | 1 | 0 | |
Construction Land | 0 | 0 | 1 | 0 | 1 | 1 | |
EPS | Farmland | 1 | 0 | 0 | 0 | 1 | 1 |
Forest Land | 0 | 1 | 1 | 0 | 1 | 1 | |
Grassland | 0 | 1 | 1 | 0 | 1 | 1 | |
Water Area | 0 | 0 | 0 | 1 | 0 | 0 | |
Unused Land | 0 | 0 | 0 | 0 | 1 | 0 | |
Construction Land | 0 | 0 | 0 | 0 | 1 | 1 |
Urban | Land Use Types | 2000 (km2) | 2010 (km2) | 2020 (km2) | Land Use Change Dynamics 2000–2020 |
---|---|---|---|---|---|
Chengdu City | Farmland | 10,583.37 | 10,648.13 | 9350.10 | −0.58% |
Forest Land | 3067.48 | 2450.15 | 3231.11 | 0.27% | |
Grassland | 100.21 | 109.44 | 117.66 | 0.87% | |
Water Area | 102.29 | 144.59 | 131.25 | 1.42% | |
Unused Land | 5.73 | 6.97 | 6.57 | 0.73% | |
Construction Land | 478.90 | 978.70 | 1501.29 | 10.67% | |
Deyang City | Farmland | 4526.54 | 4540.28 | 4205.63 | −0.35% |
Forest Land | 1159.30 | 1046.41 | 1294.32 | 0.58% | |
Grassland | 66.23 | 67.03 | 68.88 | 0.20% | |
Water Area | 29.54 | 44.31 | 35.20 | 0.96% | |
Unused Land | 0.76 | 0.97 | 2.11 | 8.92% | |
Construction Land | 129.93 | 213.30 | 306.17 | 6.78% | |
Meishan City | Farmland | 5265.55 | 5410.34 | 5121.31 | −0.14% |
Forest Land | 1717.71 | 1508.88 | 1714.70 | −0.01% | |
Grassland | 1.19 | 2.86 | 4.93 | 15.76% | |
Water Area | 74.53 | 99.14 | 96.01 | 1.44% | |
Unused Land | 0.02 | 0.00 | 0.11 | 21.52% | |
Construction Land | 77.41 | 115.19 | 199.36 | 7.88% | |
Ziyang City | Farmland | 5471.85 | 5375.55 | 5213.97 | −0.24% |
Forest Land | 189.86 | 256.41 | 378.82 | 4.98% | |
Grassland | 0.01 | 1.11 | 0.67 | 410.56% | |
Water Area | 59.46 | 66.81 | 59.19 | −0.02% | |
Unused Land | 0.00 | 0.00 | 0.03 | Less variation | |
Construction Land | 23.53 | 44.83 | 92.03 | 14.56% | |
Total | Farmland | 25,843.90 | 25,971.02 | 23,887.85 | −0.38% |
Forest Land | 6135.20 | 5262.60 | 6619.76 | 0.39% | |
Grassland | 167.70 | 180.51 | 192.24 | 0.73% | |
Water Area | 265.89 | 354.92 | 321.74 | 1.05% | |
Unused Land | 6.54 | 7.95 | 8.81 | 1.74% | |
Construction Land | 709.65 | 1351.88 | 2098.48 | 9.79% |
Year | Zone | Mean (t) | Standard Deviation | Total (t) | Area Per Ton of Carbon (t/m2) |
---|---|---|---|---|---|
2000 | Chengdu City | 22.9743 | 7.2878 | 365,850,785.9093 | 39.1805 |
Deyang City | 22.6651 | 7.0498 | 148,815,847.8685 | 39.7224 | |
Meishan City | 23.4540 | 7.5894 | 185,961,198.1907 | 38.3840 | |
Ziyang City | 19.7760 | 3.1727 | 126,209,155.6740 | 45.5229 | |
Total | 22.2174 | 6.2749 | 826,836,987.6425 | 40.0670 | |
2010 | Chengdu City | 22.1871 | 6.7019 | 353,315,139.5032 | 40.5706 |
Deyang City | 22.3163 | 6.7823 | 146,525,414.1408 | 40.3434 | |
Meishan City | 22.9298 | 7.2512 | 181,804,786.6303 | 39.2615 | |
Ziyang City | 19.9785 | 3.6656 | 127,501,491.0436 | 45.0615 | |
Total | 21.8529 | 6.1002 | 809,146,831.3180 | 40.9430 | |
2020 | Chengdu City | 23.1296 | 7.4529 | 368,324,507.4691 | 38.9173 |
Deyang City | 23.0523 | 7.3542 | 151,357,949.5543 | 39.0553 | |
Meishan City | 23.4337 | 7.5920 | 185,799,908.4509 | 38.4173 | |
Ziyang City | 20.3520 | 4.4078 | 129,885,096.7639 | 44.2346 | |
Total | 22.4919 | 6.7017 | 835,367,462.2381 | 39.6578 |
City | Land Use Type | NDS (km2) | UDS (km2) | FPS (km2) | EPS (km2) |
---|---|---|---|---|---|
Chengdu City | Farmland | 10,676.93 | 8865.42 | 10,676.83 | 10,646.53 |
Forest Land | 2452.39 | 2409.04 | 2450.86 | 2449.98 | |
Grassland | 98.01 | 97.35 | 98.00 | 109.49 | |
Water Area | 115.64 | 102.64 | 144.65 | 144.61 | |
Unused Land | 6.58 | 6.53 | 6.56 | 6.96 | |
Construction Land | 986.66 | 2855.23 | 959.31 | 978.64 | |
Deyang City | Farmland | 4555.70 | 4015.45 | 4555.37 | 4540.20 |
Forest Land | 1058.12 | 1033.88 | 1057.19 | 1046.34 | |
Grassland | 53.96 | 72.25 | 54.73 | 67.03 | |
Water Area | 33.55 | 31.07 | 44.24 | 44.30 | |
Unused Land | 1.31 | 0.69 | 0.68 | 0.97 | |
Construction Land | 209.50 | 758.79 | 199.92 | 213.30 | |
Meishan City | Farmland | 5423.69 | 5174.22 | 5423.65 | 5410.28 |
Forest Land | 1511.87 | 1460.92 | 1509.43 | 1508.91 | |
Grassland | 1.36 | 1.30 | 1.37 | 2.86 | |
Water Area | 87.74 | 85.21 | 99.15 | 99.13 | |
Unused Land | 0.00 | 0.00 | 0.00 | 0.00 | |
Construction Land | 111.72 | 414.73 | 102.78 | 115.19 | |
Ziyang City | Farmland | 5380.09 | 5366.20 | 5380.18 | 5375.79 |
Forest Land | 259.02 | 178.08 | 256.42 | 256.42 | |
Grassland | 0.11 | 0.07 | 0.11 | 1.11 | |
Water Area | 63.36 | 63.27 | 66.83 | 66.82 | |
Unused Land | 0.00 | 0.00 | 0.00 | 0.00 | |
Construction Land | 42.40 | 137.36 | 41.44 | 44.83 |
Type | Zone | Mean (t) | Standard Deviation | Total (t) | Carbon Storage Per Unit Area (t/km2) |
---|---|---|---|---|---|
NDS | Chengdu City | 22.1899 | 6.7044 | 353,360,498.7690 | 24,655.4823 |
Deyang City | 22.3525 | 6.8121 | 146,763,063.8039 | 24,836.0642 | |
Meishan City | 22.9381 | 7.2559 | 181,870,253.8332 | 25,486.7488 | |
Ziyang City | 19.9870 | 3.6831 | 127,555,724.7243 | 22,207.7656 | |
Total | 21.8669 | 6.1139 | 809,549,541.1303 | 24,442.3192 | |
UDS | Chengdu City | 22.0498 | 6.6988 | 351,129,091.8230 | 24,499.7875 |
Deyang City | 22.2183 | 6.7804 | 145,882,274.9034 | 24,687.0122 | |
Meishan City | 22.7832 | 7.1800 | 180,642,308.9299 | 25,314.6683 | |
Ziyang City | 19.7260 | 3.0794 | 125,890,213.2875 | 21,917.7959 | |
Total | 21.6943 | 5.9347 | 803,543,888.9437 | 24,260.9936 | |
FPS | Chengdu City | 22.1884 | 6.7025 | 353,336,574.2822 | 24,653.8130 |
Deyang City | 22.3489 | 6.8092 | 146,739,429.4895 | 24,832.0647 | |
Meishan City | 22.9322 | 7.2515 | 181,823,436.6423 | 25,480.1880 | |
Ziyang City | 19.9788 | 3.6654 | 127,503,514.5780 | 22,198.6757 | |
Total | 21.8621 | 6.1072 | 809,402,954.9920 | 24,437.8934 | |
EPS | Chengdu City | 22.1871 | 6.7019 | 353,315,139.5032 | 24,652.3174 |
Deyang City | 22.3163 | 6.7823 | 146,525,414.1408 | 24,795.8478 | |
Meishan City | 22.9298 | 7.2512 | 181,804,786.6303 | 25,477.5745 | |
Ziyang City | 19.9785 | 3.6656 | 127,501,491.0436 | 22,198.3234 | |
Total | 21.8529 | 6.1002 | 809,146,831.3180 | 24,430.1604 |
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Zhang, Y.; Liao, X.; Sun, D. A Coupled InVEST-PLUS Model for the Spatiotemporal Evolution of Ecosystem Carbon Storage and Multi-Scenario Prediction Analysis. Land 2024, 13, 509. https://doi.org/10.3390/land13040509
Zhang Y, Liao X, Sun D. A Coupled InVEST-PLUS Model for the Spatiotemporal Evolution of Ecosystem Carbon Storage and Multi-Scenario Prediction Analysis. Land. 2024; 13(4):509. https://doi.org/10.3390/land13040509
Chicago/Turabian StyleZhang, Yan, Xiaoyong Liao, and Dongqi Sun. 2024. "A Coupled InVEST-PLUS Model for the Spatiotemporal Evolution of Ecosystem Carbon Storage and Multi-Scenario Prediction Analysis" Land 13, no. 4: 509. https://doi.org/10.3390/land13040509