Journal of Geographical Sciences >
Dynamics of the eco-environmental quality in response to land use changes in rapidly urbanizing areas:A case study of Wuhan, China from 2000 to 2018
Hu Can (1994-), PhD Candidate, specialized in utilization and management of land resources. E-mail: mshucan@outlook.com |
Received date: 2021-08-04
Accepted date: 2022-03-14
Online published: 2023-02-21
Supported by
National Natural Science Foundation of China(71774174)
National Social Science Foundation of China(18ZDA054)
Humanities and Social Sciences Youth Foundation of China(21YJA790072)
China Scholarship Council(202106760072)
Copyright
The dramatic land use changes that occur in rapidly urbanized areas are important inducement to changes in the eco-environmental quality. Investigating urban land use changes and their eco-environmental quality responses can provide theoretical support and a decision-making basis for sustainable and high-quality development in rapidly urbanizing areas. Taking Wuhan, China, as the study area, this paper extracts land use information using Landsat satellite remote sensing images and a support vector machine classification. Based on this, a remote sensing-based ecological index evaluation model including humidity, greenness, dryness and heat is constructed to explore the changes in land use and their eco-environmental quality responses from 2000 to 2018. The results show that (1) the structure, extent and spatial layout of land use in Wuhan from 2000 to 2018 have undergone tremendous changes under rapid urbanization, and the change of construction land is the greatest among all land use types; (2) the overall quality of eco-environment in Wuhan continues to improve as the scale of the improved eco-environment areas is greater than that of the deteriorated areas. The direction and magnitude of the impact of each indicator on the eco-environmental quality are different; (3) the improvement or deterioration of eco-environmental quality is closely related to the changes of different land use types within the study area. The eco-environmental quality shows significant spatial heterogeneity, especially between the main urban areas and the suburban areas. This paper argues that reasonably adjusting the land use structure can serve to maintain or even improve the quality of the regional eco-environment. Finally, this study puts forward suggestions for the coordinated development of land use and the eco-environment in rapidly urbanizing areas.
HU Can , SONG Min , ZHANG Anlu . Dynamics of the eco-environmental quality in response to land use changes in rapidly urbanizing areas:A case study of Wuhan, China from 2000 to 2018[J]. Journal of Geographical Sciences, 2023 , 33(2) : 245 -265 . DOI: 10.1007/s11442-023-2081-2
Figure 1 Location of Wuhan, Hubei province, central China |
Table 1 Data sources |
Type | Name of the data | Date | Path/row | Data source |
---|---|---|---|---|
Remote sensing data | Landsat 5 TM | 2000-09-13 | 123/39 | https://earthexplorer.usgs.gov/ United States Geological Survey (USGS) |
2000-09-13 | 123/38 | |||
2000-06-18 | 122/39 | |||
2009-09-06 | 123/39 | |||
2009-09-06 | 123/38 | |||
2009-07-13 | 122/39 | |||
Landsat 8 OLI/TIRS | 2018-09-15 | 123/39 | ||
2018-09-15 | 123/38 | |||
2018-09-08 | 122/39 | |||
Vector data | Administrative boundary of Wuhan | 2017 | / | http://www.webmap.cn/ National Geomatics Center of China |
Figure 2 Land use status of Wuhan in 2000, 2009 and 2018 |
Table 2 Changes in land use areas in Wuhan in 2000-2018 |
Land use | Area (km2) | K1 (%) | ||||
---|---|---|---|---|---|---|
2000 | 2009 | 2018 | 2000-2009 | 2009-2018 | 2000-2018 | |
Forestland | 1113.17 | 1313.52 | 1036.21 | 2.00 | -2.35 | -0.38 |
Farmland | 4935.72 | 4625.39 | 4755.36 | -0.70 | 0.31 | -0.20 |
Construction land | 827.54 | 1258.11 | 1602.83 | 5.78 | 3.04 | 5.20 |
Water | 1378.62 | 1247.17 | 1148.62 | -1.06 | -0.88 | -0.93 |
Unused land | 314.10 | 124.96 | 26.12 | -6.69 | -8.79 | -5.09 |
Table 3 Land use transition matrix of Wuhan in 2000-2009 |
Land use (km2) | Forestland | Farmland | Construction land | Water | Unused land | Total in 2000 |
---|---|---|---|---|---|---|
Forestland | 653.92 | 393.35 | 47.92 | 10.98 | 6.99 | 1113.17 |
Farmland | 598.00 | 3562.02 | 589.01 | 121.80 | 64.89 | 4935.72 |
Construction land | 20.96 | 268.53 | 501.12 | 29.95 | 6.99 | 827.54 |
Water | 25.97 | 202.79 | 79.92 | 1068.93 | 0.99 | 1378.62 |
Unused land | 15.05 | 198.70 | 40.14 | 15.05 | 45.16 | 314.10 |
Total in 2009 | 1313.91 | 4625.40 | 1258.11 | 1246.71 | 125.02 | 8569.15 |
Table 4 Land use transition matrix of Wuhan in 2009-2018 |
Land use (km2) | Forestland | Farmland | Construction land | Water | Unused land | Total in 2009 |
---|---|---|---|---|---|---|
Forestland | 670.82 | 574.28 | 51.99 | 12.47 | 3.95 | 1313.52 |
Farmland | 332.37 | 3460.53 | 712.20 | 108.20 | 12.09 | 4625.39 |
Construction land | 11.18 | 464.71 | 752.67 | 27.69 | 1.85 | 1258.11 |
Water | 5.29 | 171.42 | 68.49 | 996.94 | 5.03 | 1247.17 |
Unused land | 16.11 | 84.73 | 17.67 | 3.29 | 3.16 | 124.96 |
Total in 2018 | 1035.78 | 4755.67 | 1603.03 | 1148.58 | 26.09 | 8569.15 |
Table 5 Contributions of the four indicators and the remote sensing-based ecological index in 2000-2018 |
Year | PC1 | PC2 | PC3 | PC4 | RSEI | |
---|---|---|---|---|---|---|
2000 | WET | 0.390 | 0.548 | -0.522 | 0.524 | 0.5658 |
NDVI | 0.479 | -0.650 | 0.224 | 0.546 | ||
NDSI | -0.686 | 0.142 | 0.291 | 0.652 | ||
LST | -0.384 | -0.507 | -0.770 | 0.050 | ||
Eigenvalue | 0.056 | 0.010 | 0.007 | 0.001 | ||
Percent eigenvalue (%) | 75.68% | 13.51% | 9.46% | 1.35% | ||
2009 | WET | 0.357 | 0.625 | -0.175 | 0.672 | 0.6023 |
NDVI | 0.560 | -0.736 | -0.157 | 0.347 | ||
NDSI | -0.634 | -0.247 | 0.333 | 0.653 | ||
LST | -0.395 | -0.083 | -0.913 | 0.050 | ||
Eigenvalue | 0.059 | 0.014 | 0.006 | 0.001 | ||
Percent eigenvalue (%) | 73.75% | 17.50% | 7.50% | 1.25% | ||
2018 | WET | 0.313 | 0.545 | -0.515 | 0.582 | 0.6036 |
NDVI | 0.643 | -0.679 | 0.066 | 0.348 | ||
NDSI | -0.552 | -0.109 | 0.379 | 0.735 | ||
LST | -0.428 | -0.479 | -0.766 | 0.001 | ||
Eigenvalue | 0.043 | 0.008 | 0.006 | 0.000 | ||
Percent eigenvalue (%) | 75.44% | 14.04% | 10.53% | 0.00% |
Table 6 Levels of the remote sensing-based ecological index in Wuhan in 2000, 2009 and 2018 (%) |
RSEI Level | 2000 | 2009 | 2018 |
---|---|---|---|
Poor (0.0-0.2) | 0.75 | 0.66 | 0.69 |
Inferior (0.2-0.4) | 16.24 | 11.09 | 13.53 |
Medium (0.4-0.6) | 38.23 | 32.94 | 28.82 |
Good (0.6-0.8) | 38.16 | 46.62 | 47.30 |
Excellent (0.8-1.0) | 6.63 | 8.69 | 9.66 |
Figure 3 Spatial distributions of the remote sensing-based ecological index levels in Wuhan in 2000-2018 |
Table 7 Change detection of the remote sensing-based ecological index levels in Wuhan in 2000-2018 |
Category | Differences | 2000-2009 | 2009-2018 | 2000-2018 | |||
---|---|---|---|---|---|---|---|
Ratio | Subtotal ratio | Ratio | Subtotal ratio | Ratio | Subtotal ratio | ||
Deteriorated | -4 | 0.01% | 17.49% | 0.02% | 24.03% | 0.02% | 22.06% |
-3 | 0.31% | 0.43% | 0.60% | ||||
-2 | 2.73% | 4.47% | 4.76% | ||||
-1 | 14.45% | 19.12% | 16.68% | ||||
Unchanged | 0 | 48.07% | 48.07% | 49.70% | 49.70% | 39.85% | 39.85% |
Improved | 1 | 30.66% | 34.44% | 22.89% | 26.27% | 30.02% | 38.10% |
2 | 3.58% | 3.14% | 7.48% | ||||
3 | 0.20% | 0.24% | 0.59% | ||||
4 | 0.00% | 0.01% | 0.01% |
Table 8 Results of ordinary least square and ridge regression |
Model 1 (k=0, OLS) | Model 2 (k=0.25) | |||
---|---|---|---|---|
Estimate | SE | Estimate | SE | |
Forestland | 0.197** | 0.068 | 0.211* | 0.083 |
Farmland | 0.297*** | 0.045 | 0.215*** | 0.055 |
Constructionland | -0.120** | 0.040 | -0.128** | 0.049 |
Unusedland | -1.282*** | 0.268 | -0.752* | 0.328 |
_cons | 0.436*** | 0.034 | 0.467*** | 0.041 |
R2 | 0.960 | 0.940 | ||
Adjusted R2 | 0.955 | 0.933 | ||
MSE | 0.020 | 0.024 |
Note: ***p<0.001; **p<0.01; *p<0.05; When k=0, ridge regression is the OLS method. |
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