Land-Use Transfer and Its Ecological Effects in Rapidly Urbanizing Areas: A Case Study of Nanjing, China
Abstract
:1. Introduction
2. Area Studied and Data Sources
2.1. Area Studied
2.2. Data Sources and Data Processed
2.2.1. Data Sources
2.2.2. Data Processed
3. Methodology
3.1. Land-Use Change Analysis
3.1.1. Land Expansion Intensity
3.1.2. Land-Use Degree
3.1.3. Transfer Matrix of Land Use
3.2. Evaluation of Eco-Environment Quality: RSEI
3.3. Spatial Analysis
3.4. Data Statistical Analysis Method
4. Results
4.1. Spatial-Temporal Distribution of Land Use in Nanjing
4.1.1. Area of Various Types of Land and En
4.1.2. LUCI and ΔLUCI
4.1.3. Transfer of Land Use
4.2. Spatio-Temporal Changes in Ecological Quality
4.2.1. The Eco-Environmental Quality (RSEI) in Nanjing from 2003 to 2023
4.2.2. Spatio-Temporal Changes in RSEI in Nanjing from 2003 to 2023
4.3. Effects of Land-Use Changes Affecting Eco-Environment Quality
4.3.1. RSEI Differences of Land Types and PCA of RSEI
4.3.2. Correlation Analysis Between RSEI and Area of Land Types
4.3.3. Regression Analysis Between RSEI and the Area of Various Land-Use Types
4.3.4. Regression Analysis Between RSEI and the Area Proportion of Various
Land-Use Types
4.3.5. Regression Analysis Between RSEI and LUCI
5. Discussion
5.1. Measurement and Inversion of Eco-Environmental Quality
5.2. Response of Ecological Environmental Quality to Land-Use Transfer
5.3. Optimization Suggestions for Land-Use Planning and Management in Nanjing
5.4. Limitations
- (1)
- Due to data source constraints, the 30 m land-use data, while capable of distinguishing most land-use types, does not effectively separate green spaces within constructed areas in urban centers, in particular the lack of fine-grained classification of urban green spaces, which affects the accuracy of data extraction. Future research could benefit from employing machine learning methods to classify land-use data with 1 m and sub-meter accuracy. In particular, fine-grained classification of urban green space has important value for obtaining accurate urban green space data and is an important research direction in the future [55].
- (2)
- The inversion of RSEI shows significant seasonal variations. Although this study attempts to use remote sensing data from the same or similar months, data source limitations affect consistency. Additionally, because RSEI cannot assess the ecological quality of water bodies, which significantly impacts overall ecological quality, the inversion model has inherent limitations and may incur errors. Future studies should focus on optimizing RSEI to address the inability to assess water bodies.
- (3)
- Regarding future predictions of land-use changes, this study’s projections are based on fitted models and trends from the past five years. These predictions may be influenced by other factors, potentially reducing accuracy. Future research should incorporate comprehensive land-use forecasting models such as FLUS and PLUS to enhance the robustness of predictions in subsequent studies [56,57].
6. Conclusions
- (1)
- From 2003 to 2023, Nanjing experienced significant changes in land area and utilization. Construction land saw the largest increase, while agricultural land faced the most substantial reduction. LUCI steadily increased, while RSEI continued to decrease.
- (2)
- RSEI can well reflect the eco-environmental quality of rapid urbanization areas. Significant correlations (p < 0.05) and spatial autocorrelations were observed between land use changes and RSEI fluctuations. High ecological quality primarily clusters in forested regions, while low-quality areas are mainly in built-up zones; rural and suburban areas exhibit better ecological quality than urban centers. This conclusion is similar to other studies [39,47,49]
- (3)
- Under the policy background of building ecological civilization and green high-quality development, the monitoring of land-use transfer and ecological environment quality is particularly important for the construction of digital cities in rapidly urbanizing areas. On the one hand, they can monitor the direction and quantity of all kinds of land transfer in real time so as to scientifically formulate the land-use indicators of all kinds of land and provide a theoretical basis for protecting cultivated land, limiting the excessive expansion of construction land and realizing sustainable development; On the other hand, using remote sensing technology to retrieve the regional ecological quality is of great practical value to discover the weak areas of ecological quality in time and eliminate ecological risks, which is worthy of promotion and application by local governments in daily land management.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type | Sensor Type | Path/Row | Date | Data Source |
---|---|---|---|---|
LULC data | Land-use datasets (CLCD-30) | N50_30 | 2003, 2008, 2013, 2018, 2023 | http://www.resdc.cn https://zenodo.org/records/8176941 (accessed on 6 August 2024) |
Satellite remote sensing data | Landsat_5 TM | 120/038 | 20 August 2003 | http://www.gscloud.cn/search (accessed on 6 August 2024) |
120/038 | 9 August 2008 | |||
120/038 | 11 August 2013 | |||
Landsat_8 OLI/TIRS | 120/038 | 6 August 2018 | ||
120/038 | 7 August 2023 | |||
SHP data | administrative boundary data | / | 2023 | http://www.webmap.cn/ (accessed on 6 August 2024) National Geomatics Center of China |
Land Use | Area (km2) | En (%) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
2003 | 2008 | 2013 | 2018 | 2023 | 2003–2008 | 2008–2013 | 2013–2018 | 2018–2023 | 2003–2023 | |
FA | 4805.47 | 4517.86 | 4275.61 | 4158.00 | 4125.16 | −1.20 | −1.07 | −0.55 | −0.16 | −0.71 |
FO | 506.28 | 520.86 | 485.62 | 391.96 | 401.74 | 0.58 | −1.35 | −3.86 | 0.50 | −1.03 |
WB | 582.45 | 704.29 | 780.81 | 781.62 | 686.21 | 4.18 | 2.17 | 0.02 | −2.44 | 0.89 |
CO | 690.00 | 841.20 | 1042.02 | 1252.51 | 1371.05 | 4.38 | 4.77 | 4.04 | 1.89 | 4.93 |
BA | 0.03 | 0.02 | 0.17 | 0.14 | 0.07 | −6.67 | 150.00 | −3.53 | −10.00 | 6.67 |
Years | Land Type | FA | FO | GR | WB | BA | CO | Land Transition Chart |
---|---|---|---|---|---|---|---|---|
2003–2008 | FA | 5074.60 | 47.67 | 1.20 | 176.20 | 0.00 | 166.00 | |
FO | 29.74 | 542.82 | 0.03 | 0.00 | 0.00 | 2.61 | ||
GR | 0.18 | 0.04 | 0.54 | 0.01 | 0.00 | 0.14 | ||
WB | 34.06 | 0.20 | 0.01 | 621.38 | 0.01 | 6.70 | ||
BA | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.02 | ||
CO | 0.08 | 0.00 | 0.00 | 3.05 | 0.00 | 781.43 | ||
2008–2013 | FA | 4760.99 | 22.51 | 2.51 | 132.12 | 0.03 | 220.50 | |
FO | 61.50 | 525.96 | 0.01 | 0.04 | 0.00 | 3.23 | ||
GR | 0.14 | 0.02 | 1.09 | 0.00 | 0.15 | 0.39 | ||
WB | 40.11 | 0.31 | 0.06 | 752.95 | 0.01 | 7.21 | ||
BA | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.01 | ||
CO | 0.07 | 0.00 | 0.00 | 2.73 | 0.00 | 954.11 | ||
2013–2018 | FA | 4574.30 | 8.90 | 0.17 | 54.62 | 0.00 | 224.81 | |
FO | 109.90 | 436.26 | 0.00 | 0.01 | 0.00 | 2.62 | ||
GR | 0.23 | 0.01 | 0.52 | 0.00 | 0.02 | 2.89 | ||
WB | 44.41 | 0.00 | 0.00 | 830.88 | 0.04 | 12.51 | ||
BA | 0.01 | 0.00 | 0.00 | 0.00 | 0.09 | 0.09 | ||
CO | 0.04 | 0.00 | 0.00 | 3.10 | 0.00 | 1182.31 | ||
2018–2023 | FA | 4537.51 | 44.59 | 0.01 | 20.00 | 0.00 | 126.79 | |
FO | 32.69 | 412.10 | 0.00 | 0.01 | 0.00 | 0.38 | ||
GR | 0.22 | 0.01 | 0.24 | 0.00 | 0.02 | 0.20 | ||
WB | 121.23 | 0.02 | 0.00 | 757.68 | 0.01 | 9.67 | ||
BA | 0.06 | 0.00 | 0.01 | 0.00 | 0.05 | 0.04 | ||
CO | 0.08 | 0.00 | 0.00 | 2.42 | 0.00 | 1422.73 |
RSEI Levels | 2003 | 2008 | 2013 | 2018 | 2023 | |||||
---|---|---|---|---|---|---|---|---|---|---|
Area | % | Area | % | Area | % | Area | % | Area | % | |
Poor (0–0.2) | 103.36 | 1.58 | 326.75 | 4.98 | 528.01 | 8.05 | 215.76 | 3.29 | 541.34 | 8.25 |
Inferior (0.2–0.4) | 693.67 | 10.57 | 880.28 | 13.42 | 988.15 | 15.06 | 835.75 | 12.74 | 1033.40 | 15.75 |
Medium (0.4–0.6) | 2118.65 | 32.29 | 1591.03 | 24.25 | 1538.50 | 23.45 | 1448.58 | 22.08 | 1358.32 | 20.70 |
Good (0.6–0.8) | 2587.17 | 39.43 | 2706.28 | 41.25 | 2439.50 | 37.18 | 2462.55 | 37.53 | 1985.87 | 30.27 |
Excellent (0.8–1) | 1058.33 | 16.13 | 1056.82 | 16.11 | 1056.37 | 16.10 | 1598.53 | 24.36 | 1642.25 | 25.03 |
Category | Change Level | 2003–2008 | 2008–2013 | 2013–2018 | 2018–2023 | ||||
---|---|---|---|---|---|---|---|---|---|
Ratio | Subtotal Ratio | Ratio | Subtotal Ratio | Ratio | Subtotal Ratio | Ratio | Subtotal Ratio | ||
Deteriorated | Deteriorated rapidly | 3.69% | 18.12% | 4.18% | 19.84% | 3.91% | 25.27% | 4.87% | 25.02% |
deteriorated slowly | 14.42% | 15.65% | 21.36% | 20.14% | |||||
Unchanged | Unchanged | 33.80% | 33.80% | 42.58% | 42.58% | 37.95% | 37.95% | 47.30% | 47.30% |
Improved | Improved slowly | 37.48% | 48.08% | 33.14% | 37.58% | 27.42% | 36.78% | 25.01% | 27.69% |
Improved rapidly | 10.60% | 4.45% | 9.37% | 2.67% |
Indicators | PC1 of RSEI | ||||
---|---|---|---|---|---|
2003 | 2008 | 2013 | 2018 | 2023 | |
NDVI | 0.824 | 0.7801 | 0.7536 | 0.8083 | 0.818 |
WET | 0.3914 | 0.5043 | 0.571 | 0.4076 | 0.339 |
LST | −0.281 | −0.2373 | −0.166 | −0.297 | −0.2573 |
NDBSI | −0.298 | −0.284 | −0.2799 | −0.3036 | −0.3868 |
Characteristic value | 0.2129 | 0.2326 | 0.2527 | 0.2816 | 0.3564 |
Contribution rate | 67.54% | 63.75% | 62.45% | 60.77% | 66.68% |
Correlation | RSEI | Area/km2 | ||||||
---|---|---|---|---|---|---|---|---|
Farmland | Forest Land | Grassland | Water | Unused Land | Construction Land | Total Area | ||
RSEI | / | |||||||
Farmland | 0.656 ** | / | ||||||
Forestland | 0.582 ** | 0.735 ** | / | |||||
Grassland | 0.138 | 0.422 ** | 0.658 ** | / | ||||
Water | 0.518 ** | 0.615 ** | 0.362 ** | 0.137 | / | |||
Unused land | 0.195 | 0.406 ** | 0.621 ** | 0.775 ** | 0.133 | / | ||
Construction land | 0.464 ** | 0.771 ** | 0.576 ** | 0.445 ** | 0.321 * | 0.604 ** | / | |
Total area | 0.669 ** | 0.993 ** | 0.765 ** | 0.454 ** | 0.656 ** | 0.460 ** | 0.803 ** | / |
Independent Variable (x) | Dependent Variable (y) | R2 | Sig. | Threshold Value/km2 |
---|---|---|---|---|
Farmland area | y = 0.0255 ln(x) + 0.6333 | 0.6138 | <0.001 | 400–500 |
Forestland area | y = 0.0144 ln(x) + 0.7299 | 0.5494 | <0.001 | 20–30 |
Water area | y = 0.0264 ln(x) + 0.6674 | 0.4028 | <0.001 | 100–120 |
Construction land area | y = 0.0472 ln(x) + 0.5524 | 0.2591 | <0.001 | 120–150 |
Total area | y = 0.0364 ln(x) + 0.5473 | 0.5595 | <0.001 | 600–800 |
Correlation | RSEI | Percentage of Land Area/% | |||||
---|---|---|---|---|---|---|---|
Farmland | Forestland | Grassland | Water | Unused Land | Construction Land | ||
RSEI | -- | ||||||
Farmland area% | 0.763 ** | -- | |||||
Forestland area% | 0.438 ** | 0.052 | -- | ||||
Grassland area% | −0.385 | −0.229 | −0.006 | -- | |||
Water area% | −0.078 | 0.039 | −0.382 ** | −0.177 | -- | ||
Unused land area% | 0.011 | −0.052 | 0.056 | 0.461 ** | −0.075 | -- | |
Construction land area% | −0.805 ** | −0.946 ** | −0.228 | 0.262 | −0.219 | 0.053 | -- |
Percentage of Land Area | OLS Model (k = 0) | Model 2 (k = 0.10) | ||||||
---|---|---|---|---|---|---|---|---|
B | Beta | SE | VIF | B | Beta | SE | VIF | |
farmland area% | −0.146 * | −0.464 | 0.057 | 70.64 | 0.096 *** | 0.306 | 0.005 | 0.563 |
forestland area% | −0.022 | −0.027 | 0.056 | 10.24 | 0.195 *** | 0.241 | 0.016 | 0.800 |
Water area% | −0.302 ** | −0.469 | 0.056 | 16.42 | −0.058 *** | −0.09 | 0.012 | 0.759 |
construction land area% | −0.365 ** | −1.319 | 0.056 | 90.33 | −0.113 *** | −0.409 | 0.004 | 0.477 |
_cons. | 0.987 ** | -- | 0.056 | 0.742 *** | -- | 0.004 | -- | |
R2 | 0.674 | 0.663 | ||||||
Adjust R2 | 0.672 | 0.661 | ||||||
MSE | 0.056 | 0.010 |
Independent Variable (x) | Dependent Variable (y) | R2 | Sig. | SE |
---|---|---|---|---|
Percentage of farmland area | y = 0.2236 x + 0.6459 | 0.5032 | <0.001 | 0.066 |
Percentage of forestland area | y = 0.3365 x + 0.7266 | 0.1728 | <0.001 | 0.085 |
Percentage of water area | y = −0.0162 x + 0.7488 | 0.0006 | 0.5030 | 0.094 |
Percentage of construction land area | y = −0.2104 x + 0.8293 | 0.5774 | <0.001 | 0.061 |
Year | Percentage of Area/% | En/% | LUCI | RSEI Observed Value | RSEI Model Value | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
FA | FO | WB | CO | FA | FO | WB | CO | ||||
2003 | 72.99 | 7.68 | 8.85 | 10.48 | −1.20 | 0.55 | 4.18 | 4.38 | 293.92 | 0.8049 | 0.7909 |
2008 | 68.62 | 7.89 | 10.70 | 12.78 | −1.07 | −1.42 | 2.17 | 4.77 | 294.12 | 0.7901 | 0.7906 |
2013 | 64.94 | 7.33 | 11.86 | 15.83 | −0.55 | −3.77 | 0.02 | 4.04 | 296.49 | 0.7741 | 0.7866 |
2018 | 63.15 | 5.94 | 11.87 | 19.02 | −0.16 | 0.52 | −2.44 | 1.89 | 301.18 | 0.8110 | 0.7786 |
2023 | 62.65 | 6.10 | 10.42 | 20.82 | −0.16 | 0.52 | −2.44 | 1.89 | 304.29 | 0.8108 | 0.7733 |
2028 | 62.54 | 6.13 | 10.17 | 21.21 | −0.16 | 0.52 | −2.44 | 1.89 | 305.07 | / | 0.7720 |
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Zhou, Y.; Cao, W.; Zhou, J. Land-Use Transfer and Its Ecological Effects in Rapidly Urbanizing Areas: A Case Study of Nanjing, China. Sustainability 2024, 16, 10615. https://doi.org/10.3390/su162310615
Zhou Y, Cao W, Zhou J. Land-Use Transfer and Its Ecological Effects in Rapidly Urbanizing Areas: A Case Study of Nanjing, China. Sustainability. 2024; 16(23):10615. https://doi.org/10.3390/su162310615
Chicago/Turabian StyleZhou, Yinqiao, Wei Cao, and Jiandong Zhou. 2024. "Land-Use Transfer and Its Ecological Effects in Rapidly Urbanizing Areas: A Case Study of Nanjing, China" Sustainability 16, no. 23: 10615. https://doi.org/10.3390/su162310615
APA StyleZhou, Y., Cao, W., & Zhou, J. (2024). Land-Use Transfer and Its Ecological Effects in Rapidly Urbanizing Areas: A Case Study of Nanjing, China. Sustainability, 16(23), 10615. https://doi.org/10.3390/su162310615