Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (6): 819-838.doi: 10.1007/s11442-021-1873-5
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XIONG Ying1,2,3(), ZHANG Fang1
Received:
2021-01-27
Accepted:
2021-03-20
Published:
2021-08-25
About author:
Xiong Ying (1977-), Professor, specialized in land use and urban sprawl, regional and urban planning. E-mail: xiong2001ying@126.com
Supported by:
XIONG Ying, ZHANG Fang. Effect of human settlements on urban thermal environment and factor analysis based on multi-source data: A case study of Changsha city[J].Journal of Geographical Sciences, 2021, 31(6): 819-838.
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Table 1
Data sources"
Data series | Name of the data | Data source | Use | Time |
---|---|---|---|---|
Remote sensing data | Landsat 5 TM, Landsat 8 OLI TIRS | | Inversion of surface temperature and extraction of surface information | 2000.7; 2009.8; 2016.7 |
DEM | | Calculation of elevation and slope | 2016 | |
Vector data | Administrative boundary vector data of Changsha city | China Earth System Science Data Sharing Network | Extraction of boundary of the study area | 2016 |
Big spatial data | POI data of Changsha city | Scott map | Extraction of index of economic activity | 2016 |
Table 2
Classification of surface temperature"
Temperature rating | Extremely low temperature zone | Low temperature zone | Relatively low temperature zone | Medium temperature zone | Relatively high temperature zone | High temperature zone | Extremely high temperature zone |
---|---|---|---|---|---|---|---|
Temperature range | $T<u-2.5std$ | $\begin{matrix} & u-2.5std \le T \\ & <u-1.5std \\ \end{matrix}$ | $\begin{matrix} & u-1.5std \le T \\ & <u-0.5std \\ \end{matrix}$ | $\begin{matrix} & u-0.5std \le T \\ & <u+0.5std \\ \end{matrix}$ | $\begin{matrix} & u+0.5std \le T \\ & <u+1.5std \\ \end{matrix}$ | $\begin{matrix} & u+1.5std \le T \\ & <u+2.5std \\ \end{matrix}$ | $T\ge u+2.5std$ |
Table 3
Area (km2) and proportion (%) of different temperature categories in 2000, 2009, and 2016 "
Year | Extremely low | Low | Relatively low | Medium | Relatively high | High | Extremely high | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Area | Proportion | Area | Proportion | Area | Proportion | Area | Proportion | Area | Proportion | Area | Proportion | Area | Proportion | |
2000 | 76.04 | 1.93 | 456.42 | 11.59 | 2346.89 | 59.57 | 781.76 | 19.84 | 180.58 | 4.58 | 64.75 | 1.64 | 33.07 | 0.84 |
2009 | 11.67 | 0.30 | 1003.43 | 25.47 | 1443.27 | 36.64 | 871.28 | 22.12 | 280.12 | 7.11 | 227.68 | 5.78 | 102.07 | 2.59 |
2016 | 149.68 | 3.80 | 908.47 | 23.06 | 1274.13 | 32.34 | 852.05 | 21.63 | 371.64 | 9.43 | 278.21 | 7.06 | 105.34 | 2.67 |
Table 4
Statistics of surface temperature categories in administrative districts of Changsha city (km2) "
Category (temperature zone) | Furong | Tianxin | Yuelu | Kaifu | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
2000 | 2009 | 2016 | 2000 | 2009 | 2016 | 2000 | 2009 | 2016 | 2000 | 2009 | 2016 | |
Extremely low | 0.15 | 0.00 | 0.49 | 0.00 | 0.00 | 6.12 | 0.00 | 0.00 | 17.67 | 6.85 | 0.09 | 7.86 |
Low | 0.79 | 0.39 | 0.95 | 7.42 | 6.15 | 1.85 | 27.41 | 43.35 | 160.79 | 28.27 | 16.24 | 15.77 |
Relatively low | 5.53 | 1.40 | 0.80 | 23.41 | 4.12 | 3.64 | 304.21 | 194.52 | 145.28 | 105.07 | 40.88 | 36.57 |
Medium | 8.91 | 4.45 | 2.30 | 17.06 | 11.68 | 9.72 | 139.86 | 162.65 | 80.28 | 28.03 | 62.51 | 50.41 |
Relatively high | 13.39 | 9.15 | 8.78 | 14.02 | 16.26 | 16.52 | 34.94 | 55.74 | 62.31 | 13.59 | 37.60 | 42.48 |
High | 8.98 | 16.99 | 16.29 | 9.85 | 26.46 | 28.14 | 10.31 | 47.48 | 45.48 | 6.77 | 27.05 | 32.25 |
Extremely high | 5.93 | 11.30 | 14.06 | 3.75 | 10.86 | 9.53 | 3.33 | 16.33 | 8.26 | 7.61 | 11.83 | 10.86 |
Category (temperature zone) | Yuhua | Wangcheng | Changsha | |||||||||
2000 | 2009 | 2016 | 2000 | 2009 | 2016 | 2000 | 2009 | 2016 | ||||
Extremely low | 1.63 | 0.00 | 0.24 | 1.04 | 0.02 | 65.80 | 66.37 | 11.55 | 51.51 | |||
Low | 7.96 | 0.30 | 1.30 | 83.00 | 213.84 | 249.37 | 301.57 | 723.16 | 478.44 | |||
Relatively low | 38.20 | 3.20 | 3.70 | 582.09 | 361.73 | 347.29 | 1288.38 | 837.42 | 736.84 | |||
Medium | 24.50 | 13.72 | 9.03 | 275.08 | 304.85 | 211.57 | 288.31 | 311.42 | 488.74 | |||
Relatively high | 18.44 | 21.57 | 24.15 | 35.62 | 68.61 | 69.31 | 50.59 | 71.20 | 148.09 | |||
High | 12.87 | 42.28 | 51.56 | 6.04 | 27.05 | 32.25 | 9.92 | 40.38 | 72.24 | |||
Extremely high | 7.48 | 30.00 | 21.09 | 2.61 | 9.39 | 9.89 | 2.35 | 12.38 | 31.64 |
Table 5
Indicators influencing urban thermal environment"
First level indicators | Second level indicators | Third level indicators |
---|---|---|
Natural factors | Vegetation and water body | FVC |
MNDWI | ||
Topographic features | DEM | |
Slope | ||
Human factors | Landscape pattern index | CONTAG |
SHDI | ||
DIVISION | ||
COHESION | ||
Intensity of urban construction | NDISI | |
NDBBI | ||
Albedo | ||
Socio-economic activities | POI |
Table 6
Correlation coefficients between factors and surface temperature"
Index | NDISI | FVC | MNDWI | NDBBI | POI | DEM |
---|---|---|---|---|---|---|
Pearson correlation coefficient | 0.369** | -0.508** | -0.326** | 0.815** | 0.519** | -0.514** |
Correlation results | significant | significant | significant | significant | significant | significant |
Index | Slope | DIVISION | SHDI | CONTAG | COHESION | Albedo |
Pearson correlation coefficient | -0.270** | -0.276** | -0.373** | -0.338* | 0.433** | 0.395** |
Correlation results | significant | significant | significant | significant | significant | significant |
Table 7
Eigenvalue and contribution rate of principal components"
Component | Initial eigenvalue | The load sum of squares | ||||
---|---|---|---|---|---|---|
Eigenvalue | Contribution rate (%) | Cumulative contribution rate (%) | Eigenvalue | Variance percentage (%) | Cumulative contribution rate (%) | |
1 | 3.557 | 29.644 | 29.644 | 3.557 | 29.644 | 29.644 |
2 | 2.637 | 21.978 | 51.622 | 2.637 | 21.978 | 51.622 |
3 | 1.893 | 15.778 | 67.400 | 1.893 | 15.778 | 67.400 |
4 | 0.991 | 8.254 | 75.654 | |||
5 | 0.786 | 6.554 | 82.208 | |||
6 | 0.762 | 6.354 | 88.562 | |||
7 | 0.514 | 4.286 | 92.848 | |||
8 | 0.428 | 3.569 | 96.417 | |||
9 | 0.259 | 2.159 | 98.575 | |||
10 | 0.134 | 1.114 | 99.689 | |||
11 | 0.030 | 0.246 | 99.935 | |||
12 | 0.008 | 0.065 | 100.000 |
Table 8
Principal component scoring load matrix"
Impact factors | First principal component | Second principal component | Third principal component |
---|---|---|---|
POI | 0.085 | 0.207 | -0.295 |
MNDWI | -0.506 | -0.470 | 0.136 |
FVC | 0.395 | -0.457 | 0.284 |
DIVISION | 0.930 | -0.175 | -0.219 |
SHDI | 0.936 | -0.188 | -0.227 |
CONTAG | 0.511 | 0.054 | -0.180 |
COHESION | -0.918 | 0.193 | 0.004 |
NDISI | 0.431 | 0.687 | 0.243 |
Slope | -0.206 | -0.600 | 0.432 |
DEM | 0.086 | -0.269 | 0.762 |
Albedo | -0.144 | 0.817 | -0.125 |
NDBBI | 0.192 | 0.699 | -0.331 |
Table 9
Regression coefficient and test results"
Impact factors | Nonstandardized coefficient | Standardization coefficient Beta | t | Significance | Collinearity statistics | ||
---|---|---|---|---|---|---|---|
B | Standard error | Tolerance | VIF | ||||
Constant | 0.361 | 0.010 | - | 35.175 | 0.000 | - | - |
1 | 0.116 | 0.007 | 0.327 | 15.442 | 0.000 | 0.625 | 1.601 |
2 | 0.012 | 0.008 | 0.031 | 1.471 | 0.000 | 0.644 | 1.553 |
3 | -0.289 | 0.011 | -0.640 | -26.032 | 0.006 | 0.463 | 2.159 |
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[1] | XIONG Ying. Uncertainty evaluation of the coordinated development of urban human settlement environment and economy in Changsha city [J]. Journal of Geographical Sciences, 2011, 21(6): 1123-1137. |
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