Spatial Pattern Impact of Impervious Surface Density on Urban Heat Island Effect: A Case Study in Xuzhou, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. IPS Density Extraction from Mixed Pixels
2.3.1. Endmember Fraction Extraction
2.3.2. Endmember Fraction Validation
2.4. Landscape Pattern Analysis of IPS Density Levels
2.4.1. Landscape Metrics Selection for Five ISPD Levels
2.4.2. Scale Effect Analysis
2.5. Landscape Surface Temperature Retrieval
2.6. Bivariate Moran’s I Analysis
3. Results
3.1. Inversion Results of IPSD and LST
3.2. Spatial Correlation between Landscape Metrics of IPSD Levels and LST
3.3. The Impact Weights of Class-Level Metrics of IPSD LV4 and LV5 on LST
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
- Yan, Z.; Zhou, D.; Li, Y.; Zhang, L. An Integrated Assessment on the Warming Effects of Urbanization and Agriculture in Highly Developed Urban Agglomerations of China. Sci. Total Environ. 2022, 804, 150119. [Google Scholar] [CrossRef] [PubMed]
- Ahmed, H.A.; Singh, S.K.; Kumar, M.; Maina, M.S.; Dzwairo, R.; Lal, D. Impact of Urbanization and Land Cover Change on Urban Climate: Case Study of Nigeria. Urban Clim. 2020, 32, 100600. [Google Scholar] [CrossRef]
- Estoque, R.C.; Murayama, Y. Quantifying Landscape Pattern and Ecosystem Service Value Changes in Four Rapidly Urbanizing Hill Stations of Southeast Asia. Landsc. Ecol. 2016, 31, 1481–1507. [Google Scholar] [CrossRef]
- Chao, Z.; van Dijk, A.I.J.M.; Wang, L.; Che, M.; Hou, S. Effects of Different Urbanization Levels on Land Surface Temperature Change: Taking Tokyo and Shanghai for Example. Remote Sens. 2020, 12, 2022. [Google Scholar] [CrossRef]
- Santamouris, M. Recent Progress on Urban Overheating and Heat Island Research. Integrated Assessment of the Energy, Environmental, Vulnerability and Health Impact. Synergies with the Global Climate Change. Energy Build. 2020, 207, 109482. [Google Scholar] [CrossRef]
- Da Silva, J.M.C.; Prasad, S.; Diniz-Filho, J.A.F. The Impact of Deforestation, Urbanization, Public Investments, and Agriculture on Human Welfare in the Brazilian Amazonia. Land Use Policy 2017, 65, 135–142. [Google Scholar] [CrossRef]
- Yao, R.; Cao, J.; Wang, L.; Zhang, W.; Wu, X. Urbanization Effects on Vegetation Cover in Major African Cities during 2001–2017. Int. J. Appl. Earth Obs. Geoinf. 2019, 75, 44–53. [Google Scholar] [CrossRef]
- Qin, L.; Liu, H.; Shang, G.; Yang, H.; Yan, H. Thermal Environment Effects of Built-Up Land Expansion in Shijiazhuang. Land 2022, 11, 968. [Google Scholar] [CrossRef]
- Oke, T.R. The Energetic Basis of the Urban Heat Island. Q. J. R. Meteorol. Soc. 1982, 108, 1–24. [Google Scholar] [CrossRef]
- Oke, T.R. The Distinction between Canopy and Boundary-Layer Urban Heat Islands. Atmosphere 1976, 14, 268–277. [Google Scholar] [CrossRef]
- Estoque, R.C.; Murayama, Y. Monitoring Surface Urban Heat Island Formation in a Tropical Mountain City Using Landsat Data (1987–2015). ISPRS J. Photogramm. Remote Sens. 2017, 133, 18–29. [Google Scholar] [CrossRef]
- Liu, K.; Li, X.; Wang, S.; Gao, X. Assessing the Effects of Urban Green Landscape on Urban Thermal Environment Dynamic in a Semiarid City by Integrated Use of Airborne Data, Satellite Imagery and Land Surface Model. Int. J. Appl. Earth Obs. Geoinf. 2022, 107, 102674. [Google Scholar] [CrossRef]
- Jacob, D.J.; Winner, D.A. Effect of Climate Change on Air Quality. Atmos. Environ. 2009, 43, 51–63. [Google Scholar] [CrossRef] [Green Version]
- Zhou, W.; Cao, F. Effects of Changing Spatial Extent on the Relationship between Urban Forest Patterns and Land Surface Temperature. Ecol. Indic. 2020, 109, 105778. [Google Scholar] [CrossRef]
- Santamouris, M.; Cartalis, C.; Synnefa, A.; Kolokotsa, D. On the Impact of Urban Heat Island and Global Warming on the Power Demand and Electricity Consumption of Buildings—A Review. Energy Build. 2015, 98, 119–124. [Google Scholar] [CrossRef]
- Zhou, W.; Huang, G.; Cadenasso, M.L. Does Spatial Configuration Matter? Understanding the Effects of Land Cover Pattern on Land Surface Temperature in Urban Landscapes. Landsc. Urban Plan. 2011, 102, 54–63. [Google Scholar] [CrossRef]
- Wang, Y.; Du, H.; Xu, Y.; Lu, D.; Wang, X.; Guo, Z. Temporal and Spatial Variation Relationship and Influence Factors on Surface Urban Heat Island and Ozone Pollution in the Yangtze River Delta, China. Sci. Total Environ. 2018, 631–632, 921–933. [Google Scholar] [CrossRef]
- Patz, J.A.; Campbell-Lendrum, D.; Holloway, T.; Foley, J.A. Impact of Regional Climate Change on Human Health. Nature 2005, 438, 310–317. [Google Scholar] [CrossRef]
- Ma, X.; Peng, S. Research on the Spatiotemporal Coupling Relationships between Land Use/Land Cover Compositions or Patterns and the Surface Urban Heat Island Effect. Environ. Sci. Pollut. Res. 2022, 29, 39723–39742. [Google Scholar] [CrossRef]
- Wang, Y.; Zhang, Y.; Ding, N.; Qin, K.; Yang, X. Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China. Remote Sens. 2020, 12, 578. [Google Scholar] [CrossRef]
- Song, J.; Chen, W.; Zhang, J.; Huang, K.; Hou, B.; Prishchepov, A.V. Effects of Building Density on Land Surface Temperature in China: Spatial Patterns and Determinants. Landsc. Urban Plan. 2020, 198, 103794. [Google Scholar] [CrossRef]
- Li, L.; Canters, F.; Solana, C.; Ma, W.; Chen, L.; Kervyn, M. Discriminating Lava Flows of Different Age within Nyamuragira’s Volcanic Field Using Spectral Mixture Analysis. Int. J. Appl. Earth Obs. Geoinf. 2015, 40, 1–10. [Google Scholar] [CrossRef] [Green Version]
- Barsi, J.; Schott, J.; Hook, S.; Raqueno, N.; Markham, B.; Radocinski, R. Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration. Remote Sens. 2014, 6, 11607–11626. [Google Scholar] [CrossRef] [Green Version]
- Montanaro, M.; Lunsford, A.; Tesfaye, Z.; Wenny, B.; Reuter, D. Radiometric Calibration Methodology of the Landsat 8 Thermal Infrared Sensor. Remote Sens. 2014, 6, 8803–8821. [Google Scholar] [CrossRef] [Green Version]
- Yuan, F.; Bauer, M.E. Comparison of Impervious Surface Area and Normalized Difference Vegetation Index as Indicators of Surface Urban Heat Island Effects in Landsat Imagery. Remote Sens. Environ. 2007, 106, 375–386. [Google Scholar] [CrossRef]
- Du, H.; Wang, D.; Wang, Y.; Zhao, X.; Qin, F.; Jiang, H.; Cai, Y. Influences of Land Cover Types, Meteorological Conditions, Anthropogenic Heat and Urban Area on Surface Urban Heat Island in the Yangtze River Delta Urban Agglomeration. Sci. Total Environ. 2016, 571, 461–470. [Google Scholar] [CrossRef]
- Estoque, R.C.; Murayama, Y.; Myint, S.W. Effects of Landscape Composition and Pattern on Land Surface Temperature: An Urban Heat Island Study in the Megacities of Southeast Asia. Sci. Total Environ. 2017, 577, 349–359. [Google Scholar] [CrossRef]
- Guo, G.; Zhou, X.; Wu, Z.; Xiao, R.; Chen, Y. Characterizing the Impact of Urban Morphology Heterogeneity on Land Surface Temperature in Guangzhou, China. Environ. Model. Softw. 2016, 84, 427–439. [Google Scholar] [CrossRef]
- Peng, J.; Jia, J.; Liu, Y.; Li, H.; Wu, J. Seasonal Contrast of the Dominant Factors for Spatial Distribution of Land Surface Temperature in Urban Areas. Remote Sens. Environ. 2018, 215, 255–267. [Google Scholar] [CrossRef]
- Ma, Y.; Zhang, S.; Yang, K.; Li, M. Influence of Spatiotemporal Pattern Changes of Impervious Surface of Urban Megaregion on Thermal Environment: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area of China. Ecol. Indic. 2021, 121, 107106. [Google Scholar] [CrossRef]
- Guo, G.; Wu, Z.; Chen, Y. Complex Mechanisms Linking Land Surface Temperature to Greenspace Spatial Patterns: Evidence from Four Southeastern Chinese Cities. Sci. Total Environ. 2019, 674, 77–87. [Google Scholar] [CrossRef]
- Fu, P.; Weng, Q. A Time Series Analysis of Urbanization Induced Land Use and Land Cover Change and Its Impact on Land Surface Temperature with Landsat Imagery. Remote Sens. Environ. 2016, 175, 205–214. [Google Scholar] [CrossRef]
- Gaur, A.; Eichenbaum, M.K.; Simonovic, S.P. Analysis and Modelling of Surface Urban Heat Island in 20 Canadian Cities under Climate and Land-Cover Change. J. Environ. Manage. 2018, 206, 145–157. [Google Scholar] [CrossRef]
- Son, N.-T.; Chen, C.-F.; Chen, C.-R.; Thanh, B.-X.; Vuong, T.-H. Assessment of Urbanization and Urban Heat Islands in Ho Chi Minh City, Vietnam Using Landsat Data. Sustain. Cities Soc. 2017, 30, 150–161. [Google Scholar] [CrossRef]
- Wang, Y.-C.; Hu, B.K.H.; Myint, S.W.; Feng, C.-C.; Chow, W.T.L.; Passy, P.F. Patterns of Land Change and Their Potential Impacts on Land Surface Temperature Change in Yangon, Myanmar. Sci. Total Environ. 2018, 643, 738–750. [Google Scholar] [CrossRef]
- Ridd, M.K. Exploring a V-I-S (Vegetation-Impervious Surface-Soil) Model for Urban Ecosystem Analysis through Remote Sensing: Comparative Anatomy for Cities†. Int. J. Remote Sens. 1995, 16, 2165–2185. [Google Scholar] [CrossRef]
- Liang, X.; Ji, X.; Guo, N.; Meng, L. Assessment of Urban Heat Islands for Land Use Based on Urban Planning: A Case Study in the Main Urban Area of Xuzhou City, China. Environ. Earth Sci. 2021, 80, 308. [Google Scholar] [CrossRef]
- Heinz, D.C.; Chang, C.I. Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery. IEEE Trans. Geosci. Remote Sens. 2001, 39, 529–545. [Google Scholar] [CrossRef] [Green Version]
- Wu, C. Normalized Spectral Mixture Analysis for Monitoring Urban Composition Using ETM+ Imagery. Remote Sens. Environ. 2004, 93, 480–492. [Google Scholar] [CrossRef]
- Lechner, A.M.; Langford, W.T.; Bekessy, S.A.; Jones, S.D. Are Landscape Ecologists Addressing Uncertainty in Their Remote Sensing Data? Landsc. Ecol. 2012, 27, 1249–1261. [Google Scholar] [CrossRef]
- Wu, J. Effects of Changing Scale on Landscape Pattern Analysis: Scaling Relations. Landsc. Ecol. 2004, 19, 125–138. [Google Scholar] [CrossRef]
- Chefaoui, R.M. Landscape Metrics as Indicators of Coastal Morphology: A Multi-Scale Approach. Ecol. Indic. 2014, 45, 139–147. [Google Scholar] [CrossRef]
- Kong, F.; Nakagoshi, N. Spatial-Temporal Gradient Analysis of Urban Green Spaces in Jinan, China. Landsc. Urban Plan. 2006, 78, 147–164. [Google Scholar] [CrossRef]
- Qin, Z.; Karnieli, A.; Berliner, P. A Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region. Int. J. Remote Sens. 2001, 22, 3719–3746. [Google Scholar] [CrossRef]
- Wang, F.; Qin, Z.; Song, C.; Tu, L.; Karnieli, A.; Zhao, S. An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data. Remote Sens. 2015, 7, 4268–4289. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Li, L.; Chen, L.; Liao, Z.; Wang, Y.; Wang, B.; Yang, X. A Modified Multi-Source Parallel Model for Estimating Urban Surface Evapotranspiration Based on ASTER Thermal Infrared Data. Remote Sens. 2017, 9, 1029. [Google Scholar] [CrossRef] [Green Version]
- Anselin, L. Local Indicators of Spatial Association—LISA. Geogr. Anal. 1995, 27, 93–115. [Google Scholar] [CrossRef]
- Zhang, Y.; Liu, Y.; Zhang, Y.; Liu, Y.; Zhang, G.; Chen, Y. On the Spatial Relationship between Ecosystem Services and Urbanization: A Case Study in Wuhan, China. Sci. Total Environ. 2018, 637–638, 780–790. [Google Scholar] [CrossRef]
- Liu, R.X.; Kuang, J.; Gong, Q.; Hou, X.L. Principal Component Regression Analysis with Spss. Comput. Methods Programs Biomed. 2003, 71, 141–147. [Google Scholar] [CrossRef]
- Gage, E.A.; Cooper, D.J. Relationships between Landscape Pattern Metrics, Vertical Structure and Surface Urban Heat Island Formation in a Colorado Suburb. Urban Ecosyst. 2017, 20, 1229–1238. [Google Scholar] [CrossRef]
- Hou, H.; Estoque, R.C. Detecting Cooling Effect of Landscape from Composition and Configuration: An Urban Heat Island Study on Hangzhou. Urban For. Urban Green. 2020, 53, 126719. [Google Scholar] [CrossRef]
Sensor Type | Image ID | Acquisition Time (GMT) | Air Temperature Tair (K) | Air Relative Humidity RH (%) |
---|---|---|---|---|
Landsat 8 OLI: 30 m TIRS: 100 m | LC81210362014121LGN00 | 1 May 2014 02:42:29 | 297.42 | 55.12 |
LC81220362017136LGN00 | 16 May 2017 02:48:22 | 296.33 | 39.76 | |
LC81210362020138LGN00 | 17 May 2020 02:42:10 | 299.48 | 53.19 | |
GF-1 PAN: 2 m MSS: 8 m | GF1_PMS1_E117.2_N34.1_20200428_L1A0004767917 | 28 April 2020 03:14:21 & 03:14:17 | ||
GF1_PMS1_E117.3_N34.4_20200428_L1A0004767915 |
Landscape Metrics | Formulas | |
---|---|---|
Class level | PLAND | |
LPI | ||
LSI | ||
AI | ||
COHESION | ||
Landscape level | SHDI | |
SHEI | ||
PD | ||
CONTAG |
w (g·cm−2) | τ Functions |
---|---|
0.2–1.6 | 0.9184–0.0725 w |
1.6–4.4 | 1.0163–0.1330 w |
4.4–5.4 | 0.7029–0.0620 w |
Date | Regression Model | R2 | F | Siginficance |
---|---|---|---|---|
1 May 2014 | Linear | 0.403 | 56.692 | 0.000 |
Quadratic | 0.446 | 33.364 | 0.000 | |
Cubic | 0.447 | 22.080 | 0.000 | |
Exponential | 0.402 | 56.571 | 0.000 | |
16 May 2017 | Linear | 0.482 | 78.297 | 0.000 |
Quadratic | 0.534 | 47.616 | 0.000 | |
Cubic | 0.535 | 31.403 | 0.000 | |
Exponential | 0.482 | 78.295 | 0.000 | |
17 May 2020 | Linear | 0.443 | 66.734 | 0.000 |
Quadratic | 0.489 | 41.132 | 0.000 | |
Cubic | 0.512 | 28.699 | 0.000 | |
Exponential | 0.443 | 66.894 | 0.000 |
Landscape Metrics of IPSD Levels | 1 May 2014 | 16 May 2017 | 17 May 2020 | |||
---|---|---|---|---|---|---|
Moran’s I | z-Value | Moran’s I | z-Value | Moran’s I | z-Value | |
PLAND_LV1 | −0.4104 *** | −484.3881 | −0.3640 *** | −445.4438 | −0.5860 *** | −624.7005 |
PLAND_LV2 | −0.6232 *** | −685.0685 | −0.575 | −650.749 | −0.5059 *** | −570.771 |
PLAND_LV3 | 0.1072 *** | 137.3938 | 0.0677 *** | 87.2685 | 0.1210 *** | 151.1338 |
PLAND_LV4 | 0.5383 *** | 614.4767 | 0.5590 *** | 643.6663 | 0.5756 *** | 631.9944 |
PLAND_LV5 | 0.5694 *** | 620.9965 | 0.6040 *** | 676.2391 | 0.5970 *** | 644.6729 |
LPI_LV1 | −0.3197 *** | −393.3837 | −0.3121 *** | −389.215 | −0.5315 *** | −583.4593 |
LPI_LV2 | −0.5868 *** | −654.7533 | −0.5346 | −614.5225 | −0.4395 *** | −505.6169 |
LPI_LV3 | 0.0647 *** | 81.7962 | 0.0180 *** | 23.9672 | 0.0641 *** | 82.5256 |
LPI_LV4 | 0.4297 *** | 488.2168 | 0.3920 *** | 478.9052 | 0.4547 *** | 523.3753 |
LPI_LV5 | 0.4673 *** | 559.9899 | 0.5190 *** | 585.9848 | 0.4871 *** | 544.9192 |
LSI_LV1 | −0.4573 *** | −532.3214 | −0.3848 *** | −461.4945 | −0.4999 *** | −563.362 |
LSI_LV2 | −0.4002 *** | −476.3153 | −0.3055 *** | −392.9659 | −0.4857 *** | −540.3122 |
LSI_LV3 | 0.1536 *** | 195.9282 | 0.1254 *** | 158.7598 | 0.1820 *** | 220.0359 |
LSI_LV4 | 0.5388 *** | 606.2473 | 0.5350 *** | 626.5211 | 0.5551 *** | 609.7773 |
LSI_LV5 | 0.5870 *** | 648.4811 | 0.5832 *** | 656.4933 | 0.5881 *** | 636.8686 |
COHESION_LV1 | −0.3765 *** | −448.9476 | −0.3388 *** | −412.4622 | −0.5433 *** | −599.7174 |
COHESION_LV2 | −0.5047 *** | −576.0259 | −0.4572 *** | −529.8952 | −0.4486 *** | −522.592 |
COHESION_LV3 | 0.0579 *** | 73.9933 | 0.0410 *** | 53.2148 | 0.0860 *** | 113.1896 |
COHESION_LV4 | 0.4797 *** | 537.8656 | 0.4602 *** | 542.6128 | 0.5117 *** | 564.794 |
COHESION_LV5 | 0.4883 *** | 578.6784 | 0.5277 *** | 597.2353 | 0.5227 *** | 579.8648 |
AI_LV1 | −0.2701 *** | −331.9032 | −0.2186 *** | −275.91 | −0.4366 *** | −511.5759 |
AI _LV2 | −0.4040 *** | −474.7578 | −0.3443 *** | −421.8647 | −0.2690 *** | −329.8153 |
AI _LV3 | 0.0114 *** | 14.3478 | 0.0210 *** | 27.7116 | 0.0382 *** | 49.9856 |
AI _LV4 | 0.2148 *** | 256.5564 | 0.2014 *** | 254.5295 | 0.2943 *** | 340.0671 |
AI _LV5 | 0.3271 *** | 411.6849 | 0.3711 *** | 444.3869 | 0.3693 *** | 437.643 |
SHDI | 0.0444 *** | 57.7489 | 0.1342 *** | 176.6873 | 0.0123 *** | 15.8512 |
SHEI | 0.2120 *** | 275.7464 | 0.2885 *** | 366.9209 | 0.1894 *** | 240.8287 |
PD | 0.1598 *** | 203.1749 | 0.1885 *** | 248.4368 | 0.1525 *** | 192.6977 |
CONTAG | −0.2891 *** | −366.797 | −0.3494 *** | −432.5666 | −0.2885 *** | −356.5613 |
Date | KMO | Sums of Squared Loadings | F1 ① | F2 ① |
---|---|---|---|---|
1 May 2014 | 0.7596 | Eigenvalue (ηi) | 4.076 | 3.836 |
Cumulative Percent (%) | 79.119 | |||
16 May 2017 | 0.7335 | Eigenvalue (ηi) | 3.942 | 3.928 |
Cumulative Percent (%) | 78.706 | |||
17 May 2020 | 0.7387 | Eigenvalue (ηi) | 4.030 | 3.868 |
Cumulative Percent (%) | 78.985 |
Normalized Original Variables (Class-Level VFLM) | 1 May 2014 | 16 May 2017 | 17 May 2020 | |||
---|---|---|---|---|---|---|
F1 (θ1) | F2 (θ2) | F1 (θ1) | F2 (θ1) | F1 (θ1) | F2 (θ2) | |
(V1) PLAND_LV5 | 0.449 | 0.836 | 0.886 | 0.240 | 0.722 | 0.525 |
(V2) PLAND_LV4 | 0.209 | 0.855 | 0.902 | 0.278 | / | 0.882 |
(V3) LSI_LV5 | 0.624 | 0.635 | 0.835 | / | 0.877 | 0.358 |
(V4) LSI_LV4 | 0.871 | 0.282 | 0.623 | 0.630 | 0.233 | 0.888 |
(V5) LPI _LV5 | 0.897 | 0.300 | 0.873 | / | 0.320 | 0.888 |
(V6) LPI_LV4 | 0.809 | / | 0.522 | 0.719 | / | 0.815 |
(V7) COHESION_LV5 | 0.619 | 0.659 | / | 0.874 | 0.635 | 0.599 |
(V8) COHESION_LV4 | 0.861 | / | 0.240 | 0.905 | 0.874 | / |
(V9) AI _LV5 | 0.304 | 0.893 | / | 0.732 | 0.901 | 0.270 |
(V10) AI_LV4 | / | 0.764 | 0.377 | 0.870 | 0.767 | / |
Regression Coefficients | 1 May 2014 | 16 May 2017 | 17 May 2020 | |
---|---|---|---|---|
r | 0.599 *** | 0.640 *** | 0.650 *** | |
R2 | 0.359 *** | 0.410 *** | 0.422 *** | |
Standardized Coefficients (βi) | F1 | 0.317 *** | 0.430 *** | 0.345 *** |
F2 | 0.303 *** | 0.245 *** | 0.340 *** |
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Zhang, Y.; Wang, Y.; Ding, N.; Yang, X. Spatial Pattern Impact of Impervious Surface Density on Urban Heat Island Effect: A Case Study in Xuzhou, China. Land 2022, 11, 2135. https://doi.org/10.3390/land11122135
Zhang Y, Wang Y, Ding N, Yang X. Spatial Pattern Impact of Impervious Surface Density on Urban Heat Island Effect: A Case Study in Xuzhou, China. Land. 2022; 11(12):2135. https://doi.org/10.3390/land11122135
Chicago/Turabian StyleZhang, Yu, Yuchen Wang, Nan Ding, and Xiaoyan Yang. 2022. "Spatial Pattern Impact of Impervious Surface Density on Urban Heat Island Effect: A Case Study in Xuzhou, China" Land 11, no. 12: 2135. https://doi.org/10.3390/land11122135
APA StyleZhang, Y., Wang, Y., Ding, N., & Yang, X. (2022). Spatial Pattern Impact of Impervious Surface Density on Urban Heat Island Effect: A Case Study in Xuzhou, China. Land, 11(12), 2135. https://doi.org/10.3390/land11122135