Simulating Urban Growth Scenarios Based on Ecological Security Pattern: A Case Study in Quanzhou, China
<p>Location of Quanzhou.</p> "> Figure 2
<p>Methodological framework.</p> "> Figure 3
<p>(<b>a</b>) Water ESP; (<b>b</b>) Geology ESP; (<b>c</b>) Biodiversity ESP; (<b>d</b>) Recreation ESP.</p> "> Figure 4
<p>(<b>a</b>) Nighttime light in Quanzhou city; (<b>b</b>) Revised resistance surface.</p> "> Figure 5
<p>Integrated ESP with three different scenarios in Quanzhou city.</p> "> Figure 6
<p>(<b>a</b>) Urban growth scenario A; (<b>b</b>) Urban growth scenario B; (<b>c</b>) Urban growth scenario C.</p> ">
Abstract
:1. Introduction
2. Study Context and Data Sources
2.1. Study Context
2.2. Data
3. Methodology
3.1. ESP Identification and Construction
3.1.1. Overlay Analysis
3.1.2. Soil Conservation Service Curve Number (SCS-CN) Model
3.1.3. Minimum Cumulative Resistance (MCR) model
3.1.4. Resistance Surface Revision
3.1.5. Integrated ESP Construction
3.2. SLEUTH Model Calibration
4. Results
4.1. ESP Identification and Construction
4.1.1. Water ESP Identification
4.1.2. Geology ESP Identification
4.1.3. Biodiversity ESP Identification
4.1.4. Recreation ESP Identification
4.1.5. Integrated ESP Identification
4.2. Urban Growth Modeling Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Data | Utility | Data Source |
---|---|---|
Land use | ESP identification; Urban growth simulation | Resource and Environment Cloud Platform http://www.resdc.cn/ |
Annual rainfall | Water & Geology ESP identification | National Meteorological Information Center http://data.cma.cn/; Local weather station records |
NDVI | Biodiversity & Geology ESP identification | U.S. Geological Survey Landsat image |
Slope | Water & Geology & Biodiversity ESP identification; Urban growth simulation | Calculated from DEM data; Geospatial Data Cloud https://www.gscloud.cn/ |
Elevation | Water & Geology & Biodiversity ESP identification | Derived from DEM data; Geospatial Data Cloud https://www.gscloud.cn/ |
Curvature | Geology ESP identification | Calculated from DEM data; Geospatial Data Cloud https://www.gscloud.cn/ |
Hill-shade | Urban growth simulation | Calculated from DEM data; Geospatial Data Cloud https://www.gscloud.cn/ |
Road | Geology & Biodiversity ESP identification; Urban growth simulation | National Geoinformation Service http://www.webmap.cn |
Geological hazards | Geology ESP identification | Fujian Seismological Bureau; Fujian Water Conservancy Bureau |
Soil type | Geology ESP identification | Fujian Agriculture Department |
Nighttime light | Biodiversity ESP revision | Luojia-1 Satellite image http://www.hbeos.org.cn |
Recreation resources | Recreation ESP identification | Ministry of Ecology and Environment of China; Fujian Forestry Bureau |
Growth Coefficients | Coarse | Fine | Final | BFC | |||
---|---|---|---|---|---|---|---|
MCI = 5 | MCI = 7 | MCI = 9 | |||||
NI = 3163 | NI = 7851 | NI = 7796 | |||||
OSM = 0.4679 | OSM = 0.4723 | OSM = 0.5034 | |||||
Range | Step | Range | Step | Range | Step | ||
Dispersion | 0–100 | 25 | 25–100 | 15 | 40–75 | 7 | 81 |
Breed | 0–100 | 25 | 50–100 | 10 | 50–75 | 5 | 51 |
Road gravity | 0–100 | 25 | 0–75 | 15 | 30–75 | 9 | 66 |
Slope | 0–100 | 25 | 25–70 | 9 | 25–40 | 3 | 35 |
Spread | 0–100 | 25 | 25–100 | 15 | 25–55 | 6 | 42 |
Modelling Results | 2005 | 2010 | 2015 |
---|---|---|---|
Actual value (number of pixels) | 64,455 | 106,264 | 127,652 |
Simulation value (number of pixels) | 56,427 | 94,628 | 11,6457 |
Simulation accuracy (%) | 87.54 | 89.05 | 91.23 |
Exclusion Layers | Urban Growth Scenario A | Urban Growth Scenario B | Urban Growth Scenario C |
---|---|---|---|
Built–up area in 2015 | 0 | 0 | 0 |
Water body | 100 | 100 | 100 |
Cultivated land | 100 | 100 | 100 |
Basic IESP | 100 | 100 | 100 |
Intermediate IESP | 70 | 70 | 0 |
Optimal IESP | 50 | 0 | 0 |
Evaluation Factor | Basic Water ESP | Intermediate Water ESP | Optimal Water ESP |
---|---|---|---|
Distance to river and lake (m) | ≤50 | 50–150 | 150–500 |
Distance to surface water (m) | ≤500 | 500–1000 | 1000–1500 |
Flood storage area (m3) | 3rd level of water storage area | 2nd level of water storage area | 1st level of water storage area |
Distance to inundation area (km2) | 10–Year rain event | 50–Year rain event | 1000–Year rain event |
Evaluation Factor | Standardized Value | Weight | ||||
---|---|---|---|---|---|---|
No Impact Area | Optimal ESP | Intermediate ESP | Basic Security Pattern | |||
Insensitive (1) | Mildly Sensitive (3) | Moderately Sensitive (5) | Sensitive (7) | Highly Sensitive (9) | ||
Average annual rainfall (mm) | <1300 | 1300–1400 | 1400–1500 | 1500–1600 | >1600 | 0.15 |
Slope (°) | <5 | 5–15 | 15–25 | 25–35 | >35 | 0.1 |
Elevation (m) | <200 | 200–500 | 500–800 | 800–1000 | >1000 | 0.1 |
Curvature | −0.5–0.5 | (−1.5, −0.5], [0.5–1.5) | (−2.5, −1.5], [1.5–2.5) | (−3.5, −2.5], [2.5–3.5) | (−∞,−3.5], [3.5, ∞) | 0.1 |
Soil type | Paddy soil Calcareous soil | Saline soil Sandy soil Meadow soil Limestone soil | Lateritic soil | Yellow soil Yellow–red soil Rhogosol Lithosol | Purple soil | 0.1 |
Normalized difference vegetation index (NDVI) | <0.55 | 0.4–0.55 | 0.25–0.4 | 0.1–0.25 | <0.1 | 0.1 |
Land cover | Construction land Waterbody Wetland | Forest Natural grassland Improved grassland | Irrigable land Dryland Garden plot | Artificial grassland Wild grassland Saline–alkali land | Slash land Bare land Sandy land Gravel land | 0.1 |
Distance to major road (m) | >5000 | 3000–5000 | 1500–3000 | 500–1500 | <500 | 0.1 |
Geological hazards number (per 25 km2) | <2 | 2–4 | 4–6 | 6–8 | >8 | 0.15 |
Evaluation Factor | Classification | Value | Weight |
---|---|---|---|
Land cover | Urban and other construction lands | 0 | 0.35 |
Rural residential land | 1 | ||
Bare land | 2 | ||
Lowly covered grassland | 3 | ||
Dryland and medium covered grassland | 5 | ||
Sparse forest and waterway | 6 | ||
Shrub and highly covered grassland | 7 | ||
Closed forest, lake, reservoir, wetland | 8 | ||
Paddy filed and mudflats | 10 | ||
Elevation (m) | 0–100 | 5 | 0.10 |
100–800 | 10 | ||
800–1500 | 5 | ||
>1500 | 1 | ||
Distance to water sources (m) | 0–2000 | 6 | 0.25 |
2000–7000 | 8 | ||
7000–15000 | 10 | ||
15000–30000 | 5 | ||
>30000 | 2 | ||
Distance to Built–up area (m) | >6000 | 10 | 0.15 |
4000–6000 | 5 | ||
2000–4000 | 3 | ||
0–2000 | 1 | ||
0 | 0 | ||
Distance to road (m) | 0–500 | 0 | 0.15 |
500–1000 | 1 | ||
1000–2000 | 3 | ||
2000–4000 | 5 | ||
>4000 | 10 |
Land Cover | Resistance Coefficient | Land Cover | Resistance Coefficient |
---|---|---|---|
Closed forest | 1 | Mudflats | 100 |
Shrub forest, highly covered grassland | 10 | Dryland | 200 |
Medium covered grassland | 20 | Bare land and saline–alkali land | 300 |
Sparse forest | 30 | Rural residential land | 400 |
Paddy field | 50 | Urban land | 500 |
Waterbody | 50 | Other construction lands | 500 |
Evaluation Factor | Basic Biodiversity ESP | Intermediate Biodiversity ESP | Optimal Biodiversity ESP |
---|---|---|---|
Distance to biodiversity source (m) | 0 | 0–200 | 200–300 |
MCR value | Level 1 | Level 2 | Level 3 |
Distance to biodiversity corridor (m) | <100 | 100–200 | 200–300 |
Evaluation Factor | Basic Recreation ESP | Intermediate Recreation ESP | Optimal Recreation ESP |
---|---|---|---|
Distance to recreation source (m) | 0 | 0–200 | 200–300 |
MCR value | Level 1 | Level 2 | Level 3 |
Distance to recreation corridor (m) | <100 | 100–200 | 200–300 |
Period | Urban Growth Scenarios | Urban Growth Area (km2) | Annual Urban Growth Rate (%) |
---|---|---|---|
2000–2015 | Historical record | 538.8 | 4.4% |
2015–2030 | Urban growth scenario A | 750.5 | 3.6% |
Urban growth scenario B | 677.7 | 3.3% | |
Urban growth scenario C | 498.4 | 2.5% |
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Liu, X.; Wei, M.; Zeng, J. Simulating Urban Growth Scenarios Based on Ecological Security Pattern: A Case Study in Quanzhou, China. Int. J. Environ. Res. Public Health 2020, 17, 7282. https://doi.org/10.3390/ijerph17197282
Liu X, Wei M, Zeng J. Simulating Urban Growth Scenarios Based on Ecological Security Pattern: A Case Study in Quanzhou, China. International Journal of Environmental Research and Public Health. 2020; 17(19):7282. https://doi.org/10.3390/ijerph17197282
Chicago/Turabian StyleLiu, Xiaoyang, Ming Wei, and Jian Zeng. 2020. "Simulating Urban Growth Scenarios Based on Ecological Security Pattern: A Case Study in Quanzhou, China" International Journal of Environmental Research and Public Health 17, no. 19: 7282. https://doi.org/10.3390/ijerph17197282