Land Use Optimization Embedding in Ecological Suitability in the Embryonic Urban Agglomeration
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Methods
2.3.1. Evaluating the Land Ecological Suitability
- (1)
- Multi-factor weighted overlay analysis
- (2)
- The MCR model
- (3)
- Land ecological suitability zoning
2.3.2. The MCR-MOP-Dyna-CLUE Model Framework
- (1)
- Establishing probability maps of land use type distribution
- (2)
- Setting simulation scenarios
- (3)
- The MOP model
3. Results
3.1. Temporal and Spatial Changes of Land Use during 1995–2020
3.2. The Land Ecological Suitability Zoning
3.2.1. Minimum Cumulative Resistance Surfaces of Ecological Sources and Construction Sources
3.2.2. The Land Ecological Suitability Zones
3.3. Land Use Simulation Results
3.3.1. Logistic Regression of Driving Factors
3.3.2. Results of the Land Use Simulation
4. Discussion
4.1. The Compositive MCR-MOP-Dyna-CLUE Model Framework
4.2. Land Use Optimization Project
4.3. Policy Implications
4.4. Limitations and Future Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Required Data | Data Description | Data Source |
---|---|---|
Land use data | Land use data during 1995 and 2020 | Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn/, accessed on 20 May 2023) |
Geographic data | Digital elevation model (DEM) Slope data Vegetation cover (NDVI) data | Geospatial Data Cloud (http://www.gscloud.cn, accessed on 20 May 2023) Extracted from the DEM GEE (Google Earth Engine) |
Meteorological data | Annual average precipitation for 26 years from 1995 to 2020 | Data Center for Resources and Environmental Sciences (https://www.resdc.cn, accessed on 20 May 2023) |
Soil data | The soil type map | National Tibetan Plateau Data Center (http://data.tpdc.ac.cn, accessed on 20 May 2023) |
Traffic data | Maps of national highways, provincial highways, county highways, and railways in 1995, 2005, 2015, and 2020 | National Tibetan Plateau Data Center |
Statistical data | Population size, GDP, and output value of agriculture, forestry, animal husbandry, fisheries, and secondary and tertiary industries of 39 districts (counties) from 1995 to 2020 | Qinghai Statistical Yearbook and Gansu Province Development Yearbook |
Grain output and crop sown area | The National Economic and Social Development Bulletin of each region | |
Grain prices | National Agricultural Product Cost and Income Data Compilation |
Abbreviations | Descriptions |
---|---|
LX | Lanzhou–Xining |
LES | Land ecological suitability |
MOP | Multi-objective linear programming |
MCR | Minimum cumulative resistance |
Dyna-CLUE | Dynamic Conversion of Land Use and its Effects |
HES | High Ecological Suitability |
SES | Sub-high Ecological Suitability |
MES | Medium Ecological Suitability |
LES | Low Ecological Suitability |
NES | Non-Ecological Suitability |
Land Use Types | Cultivated Land | Woodland | Grassland | Water Area | Construction Land | Unused |
---|---|---|---|---|---|---|
Eco per area (Yuan/ha) | 490,726.6 | 2666.9 | 29,548.3 | 2270.9 | 3,793,715.3 | 0 |
ESV per area (Yuan/ha) | 5047 | 26,688.15 | 16,344.61 | 92,051.93 | 0 | 880.69 |
Constrained Objectives | The Formulas | The Illustration |
---|---|---|
Total land area constraint | The total area of various types of land use should be equal to that of the study area and remains unchanged. | |
Economic benefit constraint | According to the average annual GDP growth rate of 6.5% and 5.5% in Gansu Province and Qinghai Province of the 14th Five-Year Plan, the total GDP in 2035 was predicted based on the total GDP in 2020. Considering the impact of uncertain factors such as epidemics and natural disasters, the forecast for 2035 was reduced by 5%, which was finally utilized as the minimum value of the total land economic benefits in 2035. | |
Grain yield constraint | 4487.779 s1 ≥ 450 * 13,698,000 | According to the food production safety standard in China, the total grain output of cultivated land in 2035 must meet the well-off standard line of the entire population in the urban agglomeration in the current year, that is, 450 kg/a per capita. The population in 2035 was predicted to be about 13.698 million by the GM (1,1) model on the basis of 1995–2020. |
Green equivalent constraint | 0.46s1 + s2 + 0.49s3 ≥ 46,239,966.870866 | “Green equivalent” refers to the “Green amount” with the ecological function of the quantitative forest, which can provide quantitative photosynthesis. Generally, land use types that can provide green equivalent include woodland, grassland, and cropland, with coefficients of 1, 0.49, and 0.46, respectively [35,59]. The green equivalent of the LX urban agglomeration in 2035 was expected not to be lower than that of 2020. |
Cultivated land constraint | 1,401,164.9 < s1 ≤ 1,846,355.7 * 0.02 | According to the land use planning of Gansu Province and Qinghai Province and the proportion of cultivated land in the LX area of the total cultivated land in the two provinces, the minimum quantity of cultivated land is 14,011,64.9 ha in 2020. This was determined to be the minimum of cultivated land area in 2035. According to the probability of land transfer, the maximum of cultivated land area would increase by 0.5% on the basis of 2020. |
Woodland constraints | 893,032.7 ≤ s2 ≤ 895,172.1 | Since 2005, the area of forest land in this area has been increasing. Thus, we set the area of woodland in 2020 as the minimum and the area in 2035 predicted by the Markov chain as the maximum. |
Grassland area constraint | 5,880,846.984 ≤ s3 ≤ 5,909,801.248 | Because the grassland of the LX region increased during 1995 and 2020, the minimum grassland area was the current situation in 2020, and the maximum was that in 2035 predicted by the Markov chain under the business as usual scenario. |
Water area constraint | 155,829.28 ≤ s4 ≤ 171,020.5359 | The water areas are also on the rise. Thus, the water areas in 2020 were set as the lower bound, and the water areas in 2035 predicted by the Markov chain were the upper bound. |
Construction land constraint | 233,547.0344 ≤ s5 ≤ 233,547.0344 * 1.25 | The built-up area is relatively stable and the probability of conversion to other land use type is small. Therefore, the current construction land area was set as the lower bound. Due to the limited construction land stock, the upper bound would only increase by 25% on the basis of 2020. |
Unutilized land constraint | s6 ≥ 642,032.4015 * 0.025 | In order to maintain the diversity of land landscape, the proportion of unused land was set at no less than 2.5% of the total area of the study area according to previous studies [29]. |
Land Use Types | 1995–2005 | 2005–2015 | 2015–2020 | 1995–2020 | ||||
---|---|---|---|---|---|---|---|---|
The Changed Area (ha) | Average Annual Change Rate (%) | The Changed Area (ha) | Average Annual Change Rate (%) | The Changed Area (ha) | Average Annual Change Rate (%) | The Changed Area (ha) | Average Annual Change Rate (%) | |
Cultivated land | −23,695.71 | −0.13 | −17,714.63 | −0.09 | −11,481.70 | −0.12 | −52,892.04 | −0.11 |
Woodland | −3928.82 | −0.04 | 2643.33 | 0.03 | −543.34 | −0.01 | −1828.82 | −0.01 |
Grassland | 26,514.93 | 0.05 | 71,258.19 | 0.12 | −23,246.03 | −0.08 | 74,527.09 | 0.05 |
Water areas | 10,237.45 | 0.77 | 12,658.44 | 0.88 | 5016.03 | 0.66 | 27,911.92 | 0.79 |
Construction land | 19,488.98 | 1.42 | 45,846.69 | 2.73 | 39,095.54 | 3.73 | 104,431.21 | 2.40 |
Unused land | −28,616.83 | −0.37 | −114,692.02 | −1.61 | −8840.50 | −0.27 | −152,149.35 | −0.85 |
Dynamic index | 2.83 | 3.20 | 1.41 | 2.29 |
Threshold Interval | LES Zones | Area (ha) | Proportion (%) | Development Direction |
---|---|---|---|---|
−184,525–31,378.70 | HES | 1,594,521.04 | 16.52 | Ecological core zone |
−31,378.70–20,233.24 | SES | 987,653.33 | 10.23 | |
−20,233.24–4248.30 | MES | 3,705,424.71 | 38.39 | Ecological buffer zone |
−4248.30–2790.93 | LES | 2,361,127.95 | 24.46 | Suitable construction zone |
2790.93–69,369 | NES | 1,002,907.37 | 10.39 | Suitable construction zone |
Driving Factors | Exp(B) and ROC Values for Regression Results in 1995 | |||||
---|---|---|---|---|---|---|
Cultivated Land | Woodland | Grassland | Water Areas | Construction Land | Unused Land | |
Elevation (m) | 0.99771 | 1.00037 | −0.00036 | 0.99828 | 1.00185 | |
Slope (°) | 0.86700 | 1.16772 | 1.01951 | 0.90863 | 0.84614 | 1.00810 |
Multi-year mean precipitation (mm) | 0.99991 | 1.00003 | 0.99999 | 1.00001 | 0.99968 | 0.99999 |
Soil type | 1.00001 | 1.00000 | 0.99999 | 0.99999 | 0.99997 | 1.00000 |
Vegetation coverage (%) | 1.00003 | 1.00002 | 1.00001 | 0.99636 | 0.99997 | 1.00001 |
Distance to main roads (m) | 1.00020 | 0.99995 | 0.99987 | 0.900074 | 1.00013 | |
Distance to city center (m) | 0.99097 | 1.00927 | 1.00335 | 0.98743 | 0.98000 | 0.98445 |
Distance to water areas (m) | 52.32222 | 2931.783 | 0.24949 | 0.28634 | 25.22859 | 0.00050 |
GDP per capita (yuan/person) | 1.00017 | 1.00015 | 1.00001 | 1.00007 | 0.99992 | |
Population Density (person/ha) | 1.00011 | 0.99980 | 0.99952 | 1.00054 | 0.99532 | |
ROC | 0.805202 | 0.856321 | 0.780534 | 0.982141 | 0.870614 | 0.885693 |
Driving factors | Exp(B) and ROC values for regression results in 2005 | |||||
Cultivated land | Woodland | Grassland | Water areas | Construction land | Unused land | |
Elevation (m) | 0.99759 | 0.99986 | 1.00044 | 0.99829 | 1.00226 | |
Slope (°) | 0.86547 | 1.15514 | 1.01841 | 0.91704 | 0.83336 | 0.99355 |
Multi-year mean precipitation (mm) | 0.99990 | 1.00004 | 0.99999 | 1.00002 | 0.99964 | 0.99997 |
Soil type | 1.00001 | 1.00000 | 0.99999 | 0.99999 | 0.99997 | 1.00000 |
Vegetation coverage (%) | 1.00004 | 1.00002 | 1.00001 | 0.99479 | 0.99998 | 0.99999 |
Distance to main roads (m) | 1.00027 | 0.99985 | 0.99992 | |||
Distance to city center (m) | 0.98925 | 1.00465 | 1.00348 | 0.98066 | 0.98105 | 0.97755 |
Distance to water areas (m) | 21.81966 | 1077.347 | 0.35112 | 0.26198 | 14.77016 | 0.00122 |
GDP per capita (yuan/person) | 1.00003 | 1.00000 | 1.00002 | 1.00003 | 0.99999 | |
Population Density (person/ha) | 0.99986 | 0.99967 | 1.00047 | 0.99767 | ||
ROC | 0.80036 | 0.85206 | 0.77652 | 0.98982 | 0.87329 | 0.8695 |
Driving factors | Exp(B) and ROC values for regression results in 2015 | |||||
Cultivated land | Woodland | Grassland | Water areas | Construction land | Unused land | |
Elevation (m) | 0.99804 | 1.00042 | 0.99975 | 0.99890 | 1.00200 | |
Slope (°) | 0.86107 | 1.15786 | 1.02914 | 0.91733 | 0.80097 | 0.96678 |
Multi-year mean precipitation (mm) | 0.99990 | 1.00003 | 0.99999 | 1.00004 | 0.99956 | 0.99998 |
Soil type | 1.00000 | 0.99999 | 1.00000 | 1.00000 | 0.99993 | 1.00001 |
Vegetation coverage (%) | 1.00003 | 1.00002 | 1.00002 | 0.99295 | 0.99996 | |
Distance to main roads (m) | 1.00005 | 0.99994 | 0.99993 | 0.99979 | 0.90033 | 1.00011 |
Distance to city center (m) | 0.98589 | 1.00405 | 0.96897 | 0.98491 | 0.98891 | |
Distance to water areas (m) | 17.93796 | 565.7318 | 0.29255 | 1.43875 | 0.70525 | 0.00090 |
GDP per capita (yuan/person) | 1.00001 | 1.00001 | 1.00000 | 1.00001 | 0.99999 | |
Population Density (person/ha) | 0.99992 | 1.00011 | 0.99975 | 1.00023 | 0.99984 | |
ROC | 0.7955 | 0.85607 | 0.79188 | 0.99368 | 0.87892 | 0.88425 |
Driving factors | Exp(B) and ROC values for regression results in 2020 | |||||
Cultivated land | Woodland | Grassland | Water areas | Construction land | Unused land | |
Elevation (m) | 0.99801 | 1.00023 | 1.00030 | 0.99964 | 0.99916 | 1.00165 |
Slope (°) | 0.85526 | 1.15430 | 1.02840 | 0.89867 | 0.76904 | 0.97528 |
Multi-year mean precipitation (mm) | 0.99989 | 1.00003 | 1.00006 | 0.99966 | 0.99997 | |
Soil type | 1.00000 | 1.00000 | 1.00000 | 0.99999 | 0.99993 | 1.00001 |
Vegetation coverage (%) | 1.00003 | 1.00002 | 1.00002 | 0.92930 | 0.99999 | 1.00001 |
Distance to main roads (m) | 1.00016 | 0.99975 | 0.90054 | 1.00010 | ||
Distance to city center (m) | 0.98490 | 1.00291 | 1.00264 | 0.96661 | 0.99171 | 0.99621 |
Distance to water areas (m) | 28.81471 | 3281.99 | 0.42438 | 0.02481 | 0.00071 | |
GDP per capita (yuan/person) | 1.00003 | 1.00001 | 1.00001 | 1.00001 | 0.99999 | |
Population Density (person/ha) | 0.99993 | 1.00010 | 0.99979 | 0.99989 | 1.00018 | 0.99908 |
ROC | 0.79286 | 0.86102 | 0.76721 | 0.99657 | 0.87343 | 0.89383 |
Simulation Years | 2020 Simulated in 1995 | 2015 Simulated in 1995 | 2005 Simulated in 1995 | 2020 Simulated in 2005 | 2015 Simulated in 2005 | 2020 Simulated in 2015 |
---|---|---|---|---|---|---|
Overall Accuracy | 0.9694 | 0.9760 | 0.9959 | 0.9727 | 0.9807 | 0.9834 |
Kappa Coefficient | 0.9355 | 0.9442 | 0.9844 | 0.9472 | 0.9564 | 0.9891 |
Simulation Scenarios | Ecological Benefits | Economic Benefits |
---|---|---|
Business as usual scenario | 172,386.2339 | 157,677,615.9 |
Ecological optimization scenario | 187,490.4595 | 151,808,605.1 |
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Chen, X.; Zhao, R.; Shi, P.; Zhang, L.; Yue, X.; Han, Z.; Wang, J.; Dou, H. Land Use Optimization Embedding in Ecological Suitability in the Embryonic Urban Agglomeration. Land 2023, 12, 1164. https://doi.org/10.3390/land12061164
Chen X, Zhao R, Shi P, Zhang L, Yue X, Han Z, Wang J, Dou H. Land Use Optimization Embedding in Ecological Suitability in the Embryonic Urban Agglomeration. Land. 2023; 12(6):1164. https://doi.org/10.3390/land12061164
Chicago/Turabian StyleChen, Xidong, Ruifeng Zhao, Peiji Shi, Lihua Zhang, Xiaoxin Yue, Ziyi Han, Jingfa Wang, and Hanmei Dou. 2023. "Land Use Optimization Embedding in Ecological Suitability in the Embryonic Urban Agglomeration" Land 12, no. 6: 1164. https://doi.org/10.3390/land12061164
APA StyleChen, X., Zhao, R., Shi, P., Zhang, L., Yue, X., Han, Z., Wang, J., & Dou, H. (2023). Land Use Optimization Embedding in Ecological Suitability in the Embryonic Urban Agglomeration. Land, 12(6), 1164. https://doi.org/10.3390/land12061164