Spatiotemporal Evolution and Driving Force Analysis of Habitat Quality in the Beibu Gulf Urban Agglomeration
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Processing
2.3. Methodology
2.3.1. Construction of RSEI Based on the GEE Platform
2.3.2. Theil–Sen and Mann–Kendall Trend Analysis
2.3.3. Hurst Index
2.3.4. Spatial Heterogeneity of EQ
2.3.5. Geodetector
3. Results and Analysis
3.1. Principal Component Analysis of the Four Indicators
3.2. Spatiotemporal Analysis of EQ Changes
3.3. Trend and Stability of EG in the BGUA
3.4. Spatial Heterogeneity of EQ in the BGUA
3.5. Analysis of the Driving Factors of EQ in the BGUA
3.5.1. Factor Detection Analysis
3.5.2. Interaction Detection Analysis
4. Discussion
4.1. The Spatiotemporal Changes in EQ of the BGUA
4.2. Impact of Driving Factors on RSEI
4.3. Future Development Trends of EQ in the BGUA
4.4. Limitations of the Study
5. Conclusions
- (1)
- EQ changes: From 2000 to 2010, EQ improved, especially between 2005 and 2010, driven by national policies and favorable climate conditions. From 2010 to 2015, quality declined, likely due to urban expansion and industrialization. After 2015, EQ rebounded due to stronger ecological policies and governance measures.
- (2)
- EQ trends: HI analysis shows a continued improvement in most areas, particularly in regions with effective ecological governance like southeast Chongzuo and northwest Fangchenggang.
- (3)
- Spatial differentiation: A significant positive spatial correlation exists in EQ. High-quality areas are primarily found in central Nanning, Fangchenggang, southern Chongzuo, and northern Yulin, indicating that improvements in these areas have a positive impact on surrounding regions. Low-quality areas are concentrated in Nanning, Beihai, and Yulin, influenced by urban expansion and industrial pollution. After 2015, the high-quality areas slightly shrank, while low-quality areas reduced in size, likely due to the implementation of ecological governance measures.
- (4)
- Driving factors: EQ changes are mainly driven by NDBSI (q = 0.806), with significant contributions from NDVI, LST, and WET. The combined effects of urban expansion leading to increased impervious surfaces (NDBSI rise) and vegetation loss (NDVI decline) (q = 0.856) and the interaction between LST and NDVI (q = 0.845) provided much greater explanatory power than individual factors, demonstrating that the synergy between urban expansion, vegetation loss, and temperature rise significantly impacts EQ.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Type | Data Name | Resolution/m | Data Sources |
---|---|---|---|
RSEI | MOD13A1 | 500 | https://modis.gsfc.nasa.gov/ (accessed on 15 December 2024) |
MOD11A2 | 1000 | ||
MOD09A1 | 500 | ||
Natural factors | Annual average temperature (TEM) | 1000 | https://data.tpdc.ac.cn/ (accessed on 15 December 2024) |
Annual average precipitation (PRE) | 1000 | ||
Socio-economic data | GDP | 1000 | http://www.resdc.cn/ (accessed on 15 December 2024) |
Population (POP) | 1000 | ||
Night-time light intensity (NLI) | 500 | http://www.geodata.cn (accessed on 15 December 2024) | |
Topographical factors | DEM | 30 | http://www.gscloud.cn/ (accessed on 15 December 2024) |
Slope | 30 | Extracted from DEM data (http://www.gscloud.cn/, accessed on 15 December 2024) | |
Aspect | 30 | ||
Land use | LULC | 30 | http://www.resdc.cn/ (accessed on 15 December 2024) |
Indicator | Calculation Formula | Parameter Explanation |
---|---|---|
Greenness (NDVI) | In each formula represents the reflectance of each MODIS band. DN refers to the pixel grayscale value. SI is the soil index, and IBI is the building index. | |
Humidity (WET) | ||
Heat (LST) | ||
Dryness (NDBSI) |
Z | Trend Characteristics | |
---|---|---|
Highly significant improvement | ||
Significant improvement | ||
Marginally significant improvement | ||
No significant improvement | ||
Z | No change | |
No significant deterioration | ||
Marginally significant deterioration | ||
Significant deterioration | ||
Highly significant deterioration |
Types of Factor Interaction | Criteria for Judgment |
---|---|
Nonlinear weakening | |
Single-factor nonlinear weakening | |
Two-factor enhancement | |
Mutual independence | |
Nonlinear enhancement |
Year | Principal Component (PC) | NDVI | WET | LST | NDBSI | Eigenvalue | Eigenvalue Contribution Rate (%) |
---|---|---|---|---|---|---|---|
2000 | PC1 | 0.488 | 0.473 | −0.444 | −0.583 | 0.066 | 67.00 |
PC2 | 0.161 | 0.258 | 0.892 | −0.335 | 0.017 | 17.17 | |
PC3 | 0.660 | −0.743 | 0.060 | −0.096 | 0.014 | 13.84 | |
PC4 | −0.548 | −0.397 | −0.062 | −0.733 | 0.002 | 1.99 | |
2005 | PC1 | 0.448 | 0.553 | −0.512 | −0.481 | 0.055 | 60.92 |
PC2 | −0.791 | 0.374 | −0.452 | 0.174 | 0.019 | 21.43 | |
PC3 | −0.196 | 0.595 | 0.730 | −0.274 | 0.013 | 14.16 | |
PC4 | 0.368 | 0.448 | 0.040 | 0.814 | 0.003 | 3.49 | |
2010 | PC1 | 0.448 | 0.470 | −0.569 | −0.505 | 0.061 | 67.76 |
PC2 | 0.691 | −0.701 | 0.099 | −0.150 | 0.015 | 17.20 | |
PC3 | 0.199 | 0.391 | 0.815 | −0.378 | 0.011 | 12.41 | |
PC4 | −0.532 | −0.368 | −0.047 | −0.762 | 0.002 | 2.64 | |
2015 | PC1 | 0.481 | 0.453 | −0.560 | −0.500 | 0.061 | 67.58 |
PC2 | 0.646 | −0.736 | 0.111 | −0.170 | 0.015 | 16.69 | |
PC3 | 0.186 | 0.378 | 0.817 | −0.393 | 0.012 | 13.23 | |
PC4 | −0.563 | −0.332 | −0.081 | −0.753 | 0.002 | 2.49 | |
2022 | PC1 | 0.499 | 0.442 | −0.519 | −0.535 | 0.063 | 65.51 |
PC2 | −0.432 | 0.816 | 0.373 | −0.090 | 0.017 | 17.66 | |
PC3 | 0.593 | −0.062 | 0.766 | −0.241 | 0.013 | 13.71 | |
PC4 | −0.461 | −0.366 | 0.074 | −0.805 | 0.003 | 3.12 |
Type | Significant Degradation | Marginally Significant Degradation | Insignificant Degradation | No Change | Insignificant Improvement | Marginally Significant Improvement | Significant Improvement |
---|---|---|---|---|---|---|---|
Area (km2) | 1177.89 | 3831.99 | 29,582.26 | 357.90 | 32,147.60 | 3996.47 | 1340.22 |
Proportion (%) | 1.63 | 5.29 | 40.84 | 0.49 | 44.38 | 5.52 | 1.85 |
Indicators | WET | NDVI | NDBSI | LST | GDP | POP | NTL |
---|---|---|---|---|---|---|---|
q | 0.497 | 0.667 | 0.806 | 0.592 | 0.035 | 0.143 | 0.139 |
p | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Indicators | TEM | PRE | DEM | Slope | Aspect | LULC | |
q | 0.155 | 0.012 | 0.201 | 0.084 | 0.057 | 0.255 | |
p | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
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Jing, J.; Jiang, H.; Wei, F.; Xie, J.; Xie, L.; Jiang, Y.; Jia, Y.; Chen, Z. Spatiotemporal Evolution and Driving Force Analysis of Habitat Quality in the Beibu Gulf Urban Agglomeration. Land 2025, 14, 1556. https://doi.org/10.3390/land14081556
Jing J, Jiang H, Wei F, Xie J, Xie L, Jiang Y, Jia Y, Chen Z. Spatiotemporal Evolution and Driving Force Analysis of Habitat Quality in the Beibu Gulf Urban Agglomeration. Land. 2025; 14(8):1556. https://doi.org/10.3390/land14081556
Chicago/Turabian StyleJing, Jing, Hong Jiang, Feili Wei, Jiarui Xie, Ling Xie, Yu Jiang, Yanhong Jia, and Zhantu Chen. 2025. "Spatiotemporal Evolution and Driving Force Analysis of Habitat Quality in the Beibu Gulf Urban Agglomeration" Land 14, no. 8: 1556. https://doi.org/10.3390/land14081556
APA StyleJing, J., Jiang, H., Wei, F., Xie, J., Xie, L., Jiang, Y., Jia, Y., & Chen, Z. (2025). Spatiotemporal Evolution and Driving Force Analysis of Habitat Quality in the Beibu Gulf Urban Agglomeration. Land, 14(8), 1556. https://doi.org/10.3390/land14081556