A Spatiotemporal Pattern Analysis of High-Frequency Land-Use Changes in the Guangdong–Hong Kong–Macao Greater Bay Area, from 1990 to 2018
Abstract
:1. Introduction
- What are the spatiotemporal characteristics of construction land expansion in the GBA?
- Which plots have a higher change rate than other areas?
- Why was almost all this high-frequency change region finally transformed into ecological lands, such as forests, wetlands, and grassland?
- To maintain land stability and reach sustainable development on future land use, what methods can reduce change frequency and disturbance?
2. Materials and Methods
2.1. Study Area
2.2. Extracting Land-Use-Change Information
2.2.1. Determining a Land-Classification System
2.2.2. Remote-Sensing Data Selection and Processing
2.2.3. Transformed Flow-Direction Analysis
2.2.4. Plot Analysis of High-Frequency Change
2.2.5. Analysis of High-Frequency Areas of Change
2.3. Land-Management Process Data
3. Results
3.1. Spatiotemporal Characteristics of Land-Use Change, 1990–2018
3.1.1. Overall Growth Trend in Construction Land
3.1.2. Proportion and Variation Characteristics of Non-Construction Land Types
3.1.3. Distinctiveness of the Type and Rate of Change
3.2. Analysis of High-Frequency Land-Use Change by Region
3.2.1. Spatial Patterns of Zones with Different Frequencies of Land-Use Change
3.2.2. Spatial Distribution Characteristics of Areas with a High Frequency of Land-Use Changes
3.2.3. Clustering of High-Frequency Areas of Land-Use Change and Hot–Cold Spot Analysis
4. Discussion
4.1. Urban Planning
4.2. Natural Environment
4.3. Social and Economic Development
4.4. Limitations
5. Conclusions
- (1)
- The area of newly added farmland shall not be less than 5% of the original area of farmland.
- (2)
- If the adjustment involves permanent farmland, the area of newly added permanent farmland shall not be less than 5% of the adjusted area.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Primary Classification | Secondary Classification |
---|---|
Farmland | Paddy field, dry land |
Forestland | Forested land, shrubland, open woodland, and other woodlands |
Grassland | High-coverage grassland, medium-coverage grassland, and low-coverage grassland |
Wetland | Canal, lake, reservoir pit, tidal flat, and beach land |
Construction land | Town, rural residential area, and industrial and mining land |
Unused land | Sand, marshland, bare land, and other |
1990 | 2018 | ||||||
---|---|---|---|---|---|---|---|
Farmland | Forestland | Grassland | Wetland | Construction Land | Unused Land | Total | |
Farmland | 10,415.87 | 1022.08 | 57.53 | 1263.01 | 3115.24 | 1.62 | 15,875.35 |
65.57% | 6.43% | 0.36% | 7.95% | 19.61% | 0.01% | ||
Forestland | 950.62 | 28,010.41 | 320.22 | 225.72 | 1319.67 | 1.56 | 30,828.20 |
3.08% | 90.62% | 1.04% | 0.73% | 4.27% | 0.01% | ||
Grassland | 62.20 | 214.60 | 841.58 | 22.45 | 116.47 | 0.20 | 1257.50 |
4.91% | 16.95% | 66.47% | 1.77% | 9.20% | 0.02% | ||
Wetland | 579.93 | 174.59 | 19.71 | 2217.98 | 688.65 | 1.27 | 3682.13 |
15.67% | 4.72% | 0.53% | 59.94% | 18.61% | 0.03% | ||
Construction land | 267.88 | 142.49 | 7.96 | 78.35 | 2593.53 | 0.22 | 3090.43 |
8.64% | 4.60% | 0.26% | 2.53% | 83.65% | 0.01% | ||
Unused land | 23.94 | 29.72 | 3.35 | 31.93 | 36.68 | 7.34 | 132.96 |
15.87% | 19.70% | 2.22% | 21.17% | 24.32% | 4.87% | ||
Total | 12,300.44 | 29,593.89 | 1250.35 | 3839.44 | 7870.24 | 12.21 | 54,866.57 |
Sort | 1990–2018 (T0) | 1990–1995 (T1) | 1995–2000 (T2) | ||||||
---|---|---|---|---|---|---|---|---|---|
Type | Area (hm2) | Proportion (%) | Type | Area (hm2) | Proportion (%) | Type | Area (hm2) | Proportion (%) | |
1 | Farmland–construction land | 311,524 | 5.68 | Farmland–forestland | 252,090 | 4.59 | Forestland–farmland | 255,260 | 4.65 |
2 | Forestland–construction land | 131,967 | 2.41 | Forestland–farmland | 173,489 | 3.16 | Farmland–forestland | 181,790 | 3.31 |
3 | Farmland–wetland | 126,301 | 2.30 | Farmland–construction land | 163,132 | 2.97 | Construction land–farmland | 105,681 | 1.92 |
4 | Farmland–forestland | 102,208 | 1.86 | Farmland–wetland | 120,079 | 2.19 | Farmland–wetland | 76,173 | 1.39 |
5 | Forestland–farmland | 95,062 | 1.73 | Forestland–construction land | 63,635 | 1.16 | Wetland–farmland | 75,319 | 1.37 |
Sort | 2000–2005 (T3) | 2005–2010 (T4) | 2010–2018 (T5) | ||||||
Type | Area (hm2) | Proportion (%) | Type | Area (hm2) | Proportion (%) | Type | Area (hm2) | Proportion (%) | |
1 | Farmland–construction land | 107,079 | 1.95 | Farmland–construction land | 36,673 | 0.67 | Forestland–farmland | 139,717 | 2.55 |
2 | Forestland–construction land | 45,310 | 0.82 | Forestland–construction land | 21,755 | 0.40 | Farmland–forestland | 135,368 | 2.47 |
3 | Wetland–construction land | 32,316 | 0.59 | Wetland–construction land | 11,821 | 0.21 | Farmland–construction land | 115,595 | 2.11 |
4 | Farmland–wetland | 18,279 | 0.33 | Farmland–forestland | 4379 | 0.08 | Wetland–farmland | 82,692 | 1.51 |
5 | Farmland–forestland | 5825 | 0.11 | Farmland–wetland | 2743 | 0.05 | Farmland–wetland | 79,747 | 1.45 |
Type | Area (km2) | Rate (%) |
---|---|---|
No change (Flc = 0) | 36,205.39 | 66.113 |
Slightly low (Flc = 1) | 6887.75 | 12.577 |
Neutral (Flc = 2) | 8966.43 | 16.373 |
Slightly high (Flc = 3) | 2562.15 | 4.679 |
High (Flc = 4) | 138.96 | 0.254 |
Extremely high (Flc = 5) | 2.61 | 0.005 |
City | Area of High-Frequency Land-Use Change within 10 km Buffer Zone (km2) | Citywide High-Frequency Land-Use Change Area (km2) | Proportion (%) |
---|---|---|---|
Macao | 3.89 | 3.89 | 100.00 |
Hong Kong | 173.52 | 173.52 | 100.00 |
Zhuhai | 284.43 | 284.99 | 99.80 |
Shenzhen | 490.81 | 514.85 | 95.33 |
Dongguan | 660.55 | 821.30 | 80.43 |
Foshan | 997.86 | 1241.08 | 80.40 |
Zhongshan | 299.83 | 393.07 | 76.28 |
Guangzhou | 1074.40 | 1662.59 | 64.62 |
Huizhou | 965.34 | 1935.82 | 49.87 |
Zhaoqing | 1132.11 | 2490.76 | 45.45 |
Jiangmen | 919.19 | 2152.14 | 42.71 |
City | Purchase Indicators | Purchase Amount | ||
---|---|---|---|---|
Area (acre) | Proportion (%) | Amount (billion yuan) | Proportion (%) | |
Foshan | 8609.80 | 25.06 | 53.11 | 25.08 |
Zhuhai | 5203.53 | 15.15 | 34.80 | 16.43 |
Guangzhou | 4730.68 | 13.77 | 27.46 | 12.97 |
Dongguan | 4552.34 | 13.25 | 27.95 | 13.20 |
Huizhou | 3520.85 | 10.25 | 19.36 | 9.14 |
Zhongshan | 3000.00 | 8.73 | 20.09 | 9.48 |
Jiangmen | 2377.17 | 6.92 | 14.17 | 6.68 |
Shenzhen | 1930.07 | 5.63 | 12.53 | 5.92 |
Zhaoqing | 427.24 | 1.24 | 2.33 | 1.10 |
Total | 34,351.68 | 100 | 211.8 | 100 |
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Lv, C.; Lian, A.; Wang, Z.; Jia, T.; Sun, X.; Dong, R. A Spatiotemporal Pattern Analysis of High-Frequency Land-Use Changes in the Guangdong–Hong Kong–Macao Greater Bay Area, from 1990 to 2018. Land 2023, 12, 102. https://doi.org/10.3390/land12010102
Lv C, Lian A, Wang Z, Jia T, Sun X, Dong R. A Spatiotemporal Pattern Analysis of High-Frequency Land-Use Changes in the Guangdong–Hong Kong–Macao Greater Bay Area, from 1990 to 2018. Land. 2023; 12(1):102. https://doi.org/10.3390/land12010102
Chicago/Turabian StyleLv, Chencan, Anxin Lian, Zerui Wang, Tianxia Jia, Xiaomeng Sun, and Rencai Dong. 2023. "A Spatiotemporal Pattern Analysis of High-Frequency Land-Use Changes in the Guangdong–Hong Kong–Macao Greater Bay Area, from 1990 to 2018" Land 12, no. 1: 102. https://doi.org/10.3390/land12010102
APA StyleLv, C., Lian, A., Wang, Z., Jia, T., Sun, X., & Dong, R. (2023). A Spatiotemporal Pattern Analysis of High-Frequency Land-Use Changes in the Guangdong–Hong Kong–Macao Greater Bay Area, from 1990 to 2018. Land, 12(1), 102. https://doi.org/10.3390/land12010102