Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island
"> Figure 1
<p>Study areas: cities of Guangzhou and Foshan. False color image (red: near infrared band; green: red band; blue: green band).</p> "> Figure 2
<p>Selected Landsat 8 OLI and the related cloud cover areas. Years of 2013 and 2019 are the substitutes of the hot season in 2014 and 2020, respectively.</p> "> Figure 3
<p>Flowchart of research design. TIRS: Thermal Infrared Sensor; OLI: Operational Land Imager; LST: land surface temperature; ISA: impervious surface area; WS: white surface roof; BS: blue steel roof; DM: dark metal roof; DR: dark material roof; RR: residential roof. DIS: distance to city center; B_D: building density; B_H: average building height; _Z: standardized variables; for example, LST_Z means standardized land surface temperature samples; _D: dummy variables, for example, WS_D means white surface class’s dummy variable.</p> "> Figure 4
<p>Different building roofs and natural land cover types. WS: white surface roof; BS: blue steel roof; DM: dark metal roof; DR: other dark material roof; RR: residential roof; TR: tree; GR: grass; W: water.</p> "> Figure 5
<p>Example of building roof sample selection and according LST extraction. (<b>A</b>) Landsat image, (<b>B</b>) LST image, (<b>C</b>) high spatial resolution image. Blue solid line rectangle: building roof; green dash line rectangle: selected sample (pixel) in LST image.</p> "> Figure 6
<p>LST of selected dates (°C). left column: hot seasons; right column: cool seasons. Row 1: 2014 (hot season: 2013); Row 2: 2016; Row 3: 2018; Row 4: 2020 (hot season: 2019).</p> "> Figure 7
<p>Mean LST of each class in 2018.</p> "> Figure 8
<p>Mean LST of each class. 2013H* and 2019H* were the substitutes of the hot seasons of 2014 and 2020, respectively.</p> "> Figure 9
<p>R<sup>2</sup> of multivariate regressions.</p> "> Figure 10
<p>Standardized coefficients of land cover classes. 2013H* and 2019H* were the substitutes of the hot seasons of 2014 and 2020, respectively.</p> ">
Abstract
:1. Introduction
2. Materials and Process
2.1. Study Area
2.2. Data Sources
3. Method
3.1. Data Preparation and Sample Selection
3.1.1. Definitions of Types of Building Roofs and Natural Land Covers
3.1.2. Land Surface Temperature Retrieval
3.1.3. Sample Selection and LST Extraction
3.2. Data Exploratory Analysis
3.2.1. Between-Class Comparison Analysis
3.2.2. Seasonal Variation Analysis
3.3. Regression Analysis
3.3.1. Determining the Building Density, Average Building Height, and Distance to City Center
3.3.2. Dummy Variables Construction
3.3.3. Variable Standardization
3.3.4. Multivariate Linear Regression
4. Results
4.1. LST Variations
4.1.1. LST Distribution
4.1.2. Comparisons of Mean LSTs by Building Roof Types and Natural Land Classes in 2018
4.1.3. Seasonal Variability of LSTs by Years
4.2. Multivariate Regression Results
4.2.1. Model Summary
4.2.2. Regression Coefficients
5. Discussion
6. Conclusions
- (1)
- The mean LST of different types of building roofs were statistically different from each other; these differences were stronger during the hot season than the cool season. The LST of WS was significantly different from that of BS, DM, DR, and RR. The mean LSTs of BS, DM, and DR were significantly higher than those of WS and RR.
- (2)
- The impacts of building roof types on the LST differed. WS, BS, DR, and DM were positively correlated with LST, while RR was negatively correlated with LST during the cool season. The impact of WS on LST was significantly different from that of BS, DR, DM, and RR. The impacts of the finer ISA classes of BS, DR, and DM on LSTs were similar; however, they demonstrated a consistent descending order pattern (i.e., DM > BS > DR >WS), during the cool season.
- (3)
- The contributions of finer ISA classes to LST variance were stronger during the cool season than the hot season, as their standardized coefficients were larger in the former than the latter.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
WS | BS | DM | DR | RR | GR | TR | W | ||
---|---|---|---|---|---|---|---|---|---|
2013H* | WS | −2.556 | −1.287 | −1.007 | 2.950 | 5.114 | 9.844 | 12.928 | |
BS | 0.000 | 1.269 | 1.548 | 5.506 | 7.670 | 12.400 | 15.484 | ||
DM | 0.005 | 0.046 | 0.279 | 4.237 | 6.401 | 11.131 | 14.215 | ||
DR | 0.099 | 0.009 | 0.996 | 3.957 | 6.121 | 10.851 | 13.935 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | 2.164 | 6.894 | 9.978 | ||
GR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 4.730 | 7.814 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.084 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
2014C | WS | −0.961 | −1.240 | −0.378 | 2.440 | 0.885 | 3.990 | 6.678 | |
BS | 0.010 | −0.279 | 0.582 | 3.401 | 1.846 | 4.951 | 7.638 | ||
DM | 0.000 | 0.974 | 0.862 | 3.680 | 2.125 | 5.230 | 7.917 | ||
DR | 0.900 | 0.644 | 0.093 | 2.818 | 1.263 | 4.368 | 7.056 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | −1.555 | 1.550 | 4.237 | ||
GR | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 3.105 | 5.792 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 2.687 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
2016H | WS | −2.737 | −3.028 | −3.135 | 1.716 | 5.393 | 8.484 | 11.310 | |
BS | 0.000 | −0.291 | −0.398 | 4.453 | 8.130 | 11.221 | 14.047 | ||
DM | 0.000 | 0.992 | −0.107 | 4.744 | 8.421 | 11.512 | 14.338 | ||
DR | 0.000 | 0.968 | 1.000 | 4.851 | 8.528 | 11.619 | 14.445 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | 3.677 | 6.768 | 9.594 | ||
GR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.091 | 5.917 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 2.826 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
2016C | WS | −1.939 | −2.352 | −2.432 | 2.397 | 1.449 | 4.111 | 6.019 | |
BS | 0.000 | −0.414 | −0.493 | 4.335 | 3.387 | 6.050 | 7.957 | ||
DM | 0.000 | 0.901 | −0.079 | 4.749 | 3.801 | 6.464 | 8.371 | ||
DR | 0.000 | 0.882 | 1.000 | 4.828 | 3.880 | 6.543 | 8.450 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | −0.948 | 1.715 | 3.622 | ||
GR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 2.663 | 4.570 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.907 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
2018H | WS | −2.743 | −2.228 | −2.825 | 2.269 | 4.354 | 8.784 | 13.710 | |
BS | 0.000 | 0.515 | −0.082 | 5.012 | 7.097 | 11.527 | 16.453 | ||
DM | 0.000 | 0.971 | −0.597 | 4.497 | 6.582 | 11.012 | 15.938 | ||
DR | 0.000 | 1.000 | 0.950 | 5.094 | 7.179 | 11.609 | 16.535 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | 2.085 | 6.515 | 11.441 | ||
GR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 4.430 | 9.356 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 4.926 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
2018C | WS | −1.940 | −2.274 | −1.627 | 2.185 | 0.534 | 4.525 | 7.563 | |
BS | 0.000 | −0.333 | 0.313 | 4.126 | 2.474 | 6.466 | 9.503 | ||
DM | 0.000 | 0.960 | 0.647 | 4.459 | 2.807 | 6.799 | 9.837 | ||
DR | 0.000 | 0.975 | 0.535 | 3.812 | 2.161 | 6.152 | 9.190 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | −1.652 | 2.340 | 5.378 | ||
GR | 0.189 | 0.000 | 0.000 | 0.000 | 0.000 | 3.992 | 7.029 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.038 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
2019H* | WS | −2.867 | −3.593 | −2.796 | 1.625 | 3.082 | 6.952 | 9.771 | |
BS | 0.000 | −0.726 | 0.070 | 4.492 | 5.949 | 9.818 | 12.638 | ||
DM | 0.000 | 0.503 | 0.796 | 5.218 | 6.675 | 10.544 | 13.364 | ||
DR | 0.000 | 1.000 | 0.673 | 4.421 | 5.878 | 9.748 | 12.568 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | 1.457 | 5.327 | 8.146 | ||
GR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.870 | 6.689 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 2.820 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
2020C | WS | −2.469 | −3.078 | −2.288 | 3.224 | 0.760 | 4.306 | 5.920 | |
BS | 0.000 | −0.609 | 0.181 | 5.693 | 3.229 | 6.775 | 8.389 | ||
DM | 0.000 | 0.565 | 0.789 | 6.302 | 3.838 | 7.384 | 8.997 | ||
DR | 0.000 | 1.000 | 0.506 | 5.512 | 3.049 | 6.594 | 8.208 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | −2.464 | 1.082 | 2.696 | ||
GR | 0.002 | 0.000 | 0.000 | 0.000 | 0.000 | 3.546 | 5.159 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.614 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Year | R | R2 | Adjusted R2 | Std. Error of the Estimate | Durbin–Watson |
---|---|---|---|---|---|
2013H* | 0.935 | 0.873 | 0.872 | 0.359 | 1.737 |
2014C | 0.875 | 0.76 | 0.76 | 0.48 | 1.60 |
2016H | 0.923 | 0.851 | 0.850 | 0.397 | 1.824 |
2016C | 0.872 | 0.761 | 0.758 | 0.497 | 1.568 |
2018H | 0.895 | 0.801 | 0.799 | 0.448 | 1.156 |
2018C | 0.889 | 0.790 | 0.788 | 0.461 | 1.616 |
2019H* | 0.889 | 0.790 | 0.787 | 0.461 | 1.237 |
2020C | 0.862 | 0.743 | 0.740 | 0.511 | 1.395 |
Year | Sum of Square | df | Mean Square | F | Sig. |
---|---|---|---|---|---|
2013H* | 656.759 | 10 | 65.676 | 510.979 | 0.00 |
2014C | 575.310 | 10 | 57.531 | 241.273 | 0.00 |
2016H | 740.856 | 10 | 74.086 | 470.797 | 0.00 |
2016C | 646.708 | 10 | 64.671 | 261.290 | 0.00 |
2018H | 758.759 | 10 | 75.876 | 377.505 | 0.00 |
2018C | 747.535 | 10 | 74.754 | 351.989 | 0.00 |
2019H* | 639.855 | 10 | 63.985 | 300.475 | 0.00 |
2020C | 601.646 | 10 | 60.165 | 230.720 | 0.00 |
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Cool Season | Hot Season | |||
---|---|---|---|---|
Year | Date | Temperature (°C) | Date | Temperature (°C) |
2014 | 16 January 2014 | 4–19 | 9 August 2013 | 25–34 |
2016 | 7 February 2016 | 5–17 | 18 September 2016 | 25–33 |
2018 | 12 February 2018 | 7–18 | 22 July 2018 | 26–34 |
2020 | 18 February 2020 | 7-19 | 27 September 2019 | 23–32 |
Land Cover Types | Thermal Conductivity Coefficient (W/m K) | Color | Albedo |
---|---|---|---|
WS | 35–70 | White | 0.75 |
BS | 10–60 | Blue | 0.1–0.75 |
DM | 10–60 | Black, drown | <=0.1 |
DR | 0.75 | Black, deep gray | <=0.1 |
RR | 0.40–0.70 | Black, deep gray | <0.1 |
GR | - | Green | 0.18–0.25 |
TR | - | Green, deep green | 0.03–0.3 |
W | 0.606 | - | 0.02–0.05 |
Types | 2014 | 2016 | 2018 | 2020 |
---|---|---|---|---|
WS | 113 | 107 | 96 | 101 |
BS | 97 | 103 | 91 | 100 |
DM | 61 | 94 | 97 | 90 |
DR | 80 | 98 | 109 | 109 |
RR | 100 | 109 | 122 | 99 |
TR | 100 | 108 | 164 | 101 |
GR | 91 | 108 | 164 | 106 |
W | 110 | 106 | 104 | 104 |
Total | 752 | 833 | 947 | 810 |
WS | BS | DM | DR | RR | GR | TR | W | ||
---|---|---|---|---|---|---|---|---|---|
2018H | WS | −2.743 | −2.228 | −2.825 | 2.269 | 4.354 | 8.784 | 13.710 | |
BS | 0.000 | 0.515 | −0.082 | 5.012 | 7.097 | 11.527 | 16.453 | ||
DM | 0.000 | 0.971 | −0.597 | 4.497 | 6.582 | 11.012 | 15.938 | ||
DR | 0.000 | 1.000 | 0.950 | 5.094 | 7.179 | 11.609 | 16.535 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | 2.085 | 6.515 | 11.441 | ||
GR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 4.430 | 9.356 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 4.926 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
2018C | WS | −1.940 | −2.274 | −1.627 | 2.185 | 0.534 | 4.525 | 7.563 | |
BS | 0.000 | −0.333 | 0.313 | 4.126 | 2.474 | 6.466 | 9.503 | ||
DM | 0.000 | 0.960 | 0.647 | 4.459 | 2.807 | 6.799 | 9.837 | ||
DR | 0.000 | 0.975 | 0.535 | 3.812 | 2.161 | 6.152 | 9.190 | ||
RR | 0.000 | 0.000 | 0.000 | 0.000 | −1.652 | 2.340 | 5.378 | ||
GR | 0.189 | 0.000 | 0.000 | 0.000 | 0.000 | 3.992 | 7.029 | ||
TR | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.038 | ||
W | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
2014 | 2016 | 2018 | 2020 | |||||
---|---|---|---|---|---|---|---|---|
Hot | Cool | Hot | Cool | Hot | Cool | Hot | Cool | |
Intercept | 0.178 | −0.152 | 0.203 | −0.269 | 0.173 | −0.055 ** | 0.059 ** | −0.592 |
B_H | −0.056 | −0.126 | −0.049 | −0.089 | −0.098 | −0.100 | −0.054 | −0.053 |
B_D | 0.017 ** | 0.052 * | 0.011 ** | −0.018 ** | 0.156 | −0.009 ** | −0.053 | −0.008 ** |
DIS | −0.010 ** | 0.016 ** | −0.031 | −0.005 ** | −0.012 ** | −0.011 ** | −0.048 | 0.010 ** |
WS | 0.440 | 0.684 | 0.183 | 0.548 | 0.308 | 0.441 | 0.224 | 0.830 |
BS | 0.877 | 1.001 | 0.722 | 1.128 | 0.808 | 0.968 | 0.788 | 1.489 |
DR | 0.658 | 0.884 | 0.805 | 1.352 | 0.735 | 0.949 | 0.821 | 1.444 |
DM | 0.678 | 1.104 | 0.776 | 1.319 | 0.649 | 1.109 | 0.994 | 1.675 |
GR | −0.445 | 0.385 | −0.744 | 0.095 ** | −0.308 | 0.239 | −0.382 | 0.590 |
TR | −1.263 | −0.638 | −1.309 | −0.707 | −1.029 | −0.897 | −1.158 | −0.373 |
W | −1.784 | −1.542 | −1.820 | −1.278 | −1.821 | −1.743 | −1.640 | −0.826 |
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Deng, Y.; Chen, R.; Xie, Y.; Xu, J.; Yang, J.; Liao, W. Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island. Remote Sens. 2021, 13, 2840. https://doi.org/10.3390/rs13142840
Deng Y, Chen R, Xie Y, Xu J, Yang J, Liao W. Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island. Remote Sensing. 2021; 13(14):2840. https://doi.org/10.3390/rs13142840
Chicago/Turabian StyleDeng, Yingbin, Renrong Chen, Yichun Xie, Jianhui Xu, Ji Yang, and Wenyue Liao. 2021. "Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island" Remote Sensing 13, no. 14: 2840. https://doi.org/10.3390/rs13142840