Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Preprocessing
2.2.1. Extraction of the Impervious Surface Area (ISA)
2.2.2. Identification of the Planting Sites
2.2.3. Calculation of the Diurnal and Seasonal Average LST and SUHII
2.3. Statistical Analysis
2.3.1. Temporal Trend Analysis of SUHII and Pixel-Wise LST
2.3.2. Regression Analysis of LST Changes in Response to the ISA Dynamic and Afforestation
2.3.3. Scenario Prediction
3. Results
3.1. ISA Dynamics, Location of the Planting Sites, and SUHII Changes
3.2. Distribution of the Regionalized ISA Increment, Regionalized Planting Areas, and Changing Magnitude of Pixel-Wise LST
3.3. LST Changes in Response to ISA Dynamics and Afforestation
3.4. Contributions of ISA Change and Afforestation to SUHII Increases
4. Discussion
4.1. Spatial-Temporal Changes of ISA, Urban Forests, and SUHII in Beijing’s Plain Area
4.2. The Combined Effects of ISA Change and the Greening Project on LST and SUHII
4.3. Implications for Future Landscape Planning for Urban Thermal Environment Regulation
4.4. Limitations and Uncertainties
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Oke, T.R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 1982, 108, 1–24. [Google Scholar] [CrossRef]
- Graham, D.A.; Vanos, J.K.; Kenny, N.A.; Brown, R.D. The relationship between neighbourhood tree canopy cover and heat-related ambulance calls during extreme heat events in Toronto, Canada. Urban For. Urban Green. 2016, 20, 180–186. [Google Scholar] [CrossRef] [Green Version]
- Ward, K.; Lauf, S.; Kleinschmit, B.; Endlicher, W. Heat waves and urban heat islands in Europe: A review of relevant drivers. Sci. Total Environ. 2016, 569–570, 527–539. [Google Scholar] [CrossRef] [PubMed]
- Li, X.; Zhou, Y.; Yu, S.; Jia, G.; Li, H.; Li, W. Urban heat island impacts on building energy consumption: A review of approaches and findings. Energy 2019, 174, 407–419. [Google Scholar] [CrossRef]
- Li, H.; Meier, F.; Lee, X.; Chakraborty, T.; Liu, J.; Schaap, M.; Sodoudi, S. Interaction between urban heat island and urban pollution island during summer in Berlin. Sci. Total Environ. 2018, 636, 818–828. [Google Scholar] [CrossRef] [PubMed]
- Wong, L.P.; Alias, H.; Aghamohammadi, N.; Aghazadeh, S.; Nik Sulaiman, N.M. Urban heat island experience, control measures and health impact: A survey among working community in the city of Kuala Lumpur. Sustain. Cities Soc. 2017, 35, 660–668. [Google Scholar] [CrossRef]
- Leal Filho, W.; Echevarria Icaza, L.; Neht, A.; Klavins, M.; Morgan, E.A. Coping with the impacts of urban heat islands. A literature based study on understanding urban heat vulnerability and the need for resilience in cities in a global climate change context. J. Clean. Prod. 2018, 171, 1140–1149. [Google Scholar] [CrossRef] [Green Version]
- Voogt, J.A.; Oke, T.R. Thermal remote sensing of urban climates. Remote Sens. Environ. 2003, 86, 370–384. [Google Scholar] [CrossRef]
- Kuang, W.; Liu, Y.; Dou, Y.; Chi, W.; Chen, G.; Gao, C.; Yang, T.; Liu, J.; Zhang, R. What are hot and what are not in an urban landscape: Quantifying and explaining the land surface temperature pattern in Beijing, China. Landsc. Ecol. 2015, 30, 357–373. [Google Scholar] [CrossRef]
- Kuang, W.; Yang, T.; Liu, A.; Zhang, C.; Lu, D.; Chi, W. An EcoCity model for regulating urban land cover structure and thermal environment: Taking Beijing as an example. Sci. China Earth Sci. 2017, 60, 1098–1109. [Google Scholar] [CrossRef]
- Dai, Z.; Guldmann, J.; Hu, Y. Thermal impacts of greenery, water, and impervious structures in Beijing’s Olympic area: A spatial regression approach. Ecol. Indic. 2019, 97, 77–88. [Google Scholar] [CrossRef]
- Sun, R.; Chen, L. How can urban water bodies be designed for climate adaptation? Landsc. Urban Plan. 2012, 105, 27–33. [Google Scholar] [CrossRef]
- Wang, C.; Li, Y.; Myint, S.W.; Zhao, Q.; Wentz, E.A. Impacts of spatial clustering of urban land cover on land surface temperature across Köppen climate zones in the contiguous United States. Landsc. Urban Plan. 2019, 192, 103668. [Google Scholar] [CrossRef]
- Sun, R.; Xie, W.; Chen, L. A landscape connectivity model to quantify contributions of heat sources and sinks in urban regions. Landsc. Urban Plan. 2018, 178, 43–50. [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]
- Peng, J.; Xie, P.; Liu, Y.; Ma, J. Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sens. Environ. 2016, 173, 145–155. [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]
- Chen, A.; Yao, L.; Sun, R.; Chen, L. How many metrics are required to identify the effects of the landscape pattern on land surface temperature? Ecol. Indic. 2014, 45, 424–433. [Google Scholar] [CrossRef]
- Chen, Y.; Yu, S. Impacts of urban landscape patterns on urban thermal variations in Guangzhou, China. Int. J. Appl. Earth Obs. 2017, 54, 65–71. [Google Scholar] [CrossRef]
- Hu, D.; Meng, Q.; Zhang, L.; Zhang, Y. Spatial quantitative analysis of the potential driving factors of land surface temperature in different “Centers” of polycentric cities: A case study in Tianjin, China. Sci. Total Environ. 2020, 706, 135244. [Google Scholar] [CrossRef]
- Yao, L.; Li, T.; Xu, M.; Xu, Y. How the landscape features of urban green space impact seasonal land surface temperatures at a city-block-scale: An urban heat island study in Beijing, China. Urban For. Urban Green. 2020, 52, 126704. [Google Scholar] [CrossRef]
- Yu, Z.; Yao, Y.; Yang, G.; Wang, X.; Vejre, H. Strong contribution of rapid urbanization and urban agglomeration development to regional thermal environment dynamics and evolution. For. Ecol. Manag. 2019, 446, 214–225. [Google Scholar] [CrossRef]
- Akinyemi, F.O.; Ikanyeng, M.; Muro, J. Land cover change effects on land surface temperature trends in an African urbanizing dryland region. City Environ. Interact. 2019, 4, 100029. [Google Scholar] [CrossRef]
- Muro, J.; Strauch, A.; Heinemann, S.; Steinbach, S.; Thonfeld, F.; Waske, B.; Diekkrüger, B. Land surface temperature trends as indicator of land use changes in wetlands. Int. J. Appl. Earth Obs. 2018, 70, 62–71. [Google Scholar] [CrossRef]
- Qiao, Z.; Tian, G.; Xiao, L. Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data. ISPRS J. Photogramm. 2013, 85, 93–101. [Google Scholar] [CrossRef]
- Tran, D.X.; Pla, F.; Latorre-Carmona, P.; Myint, S.W.; Caetano, M.; Kieu, H.V. Characterizing the relationship between land use land cover change and land surface temperature. ISPRS J. Photogramm. 2017, 124, 119–132. [Google Scholar] [CrossRef] [Green Version]
- Nurwanda, A.; Honjo, T. The prediction of city expansion and land surface temperature in Bogor City, Indonesia. Sustain. Cities Soc. 2020, 52, 101772. [Google Scholar] [CrossRef]
- Yang, Q.; Huang, X.; Tang, Q. The footprint of urban heat island effect in 302 Chinese cities: Temporal trends and associated factors. Sci. Total Environ. 2019, 655, 652–662. [Google Scholar] [CrossRef]
- Huang, K.; Li, X.; Liu, X.; Seto, K.C. Projecting global urban land expansion and heat island intensification through 2050. Environ. Res. Lett. 2019, 14, 114037. [Google Scholar] [CrossRef] [Green Version]
- Tran, Y.L.; Siry, J.P.; Bowker, J.M.; Poudyal, N.C. Atlanta households’ willingness to increase urban forests to mitigate climate change. Urban For. Urban Green. 2017, 22, 84–92. [Google Scholar] [CrossRef]
- Sanchez, L.; Reames, T.G. Cooling Detroit: A socio-spatial analysis of equity in green roofs as an urban heat island mitigation strategy. Urban For. Urban Green. 2019, 44, 126331. [Google Scholar] [CrossRef]
- Nastran, M.; Kobal, M.; Eler, K. Urban heat islands in relation to green land use in European cities. Urban For. Urban Green. 2019, 37, 33–41. [Google Scholar] [CrossRef] [Green Version]
- Myint, S.W.; Brazel, A.; Okin, G.; Buyantuyev, A. Combined Effects of Impervious Surface and Vegetation Cover on Air Temperature Variations in a Rapidly Expanding Desert City. GISci. Remote Sens. 2013, 47, 301–320. [Google Scholar] [CrossRef]
- Jamei, Y.; Rajagopalan, P.; Sun, Q.C. Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Sci. Total Environ. 2019, 659, 1335–1351. [Google Scholar] [CrossRef] [PubMed]
- Xu, H.; Lin, D.; Tang, F. The impact of impervious surface development on land surface temperature in a subtropical city: Xiamen, China. Int. J. Climatol. 2013, 33, 1873–1883. [Google Scholar] [CrossRef]
- Sun, R.; Chen, L. Effects of green space dynamics on urban heat islands: Mitigation and diversification. Ecosyst. Serv. 2017, 23, 38–46. [Google Scholar] [CrossRef]
- Shen, W.; Li, M.; Huang, C.; He, T.; Tao, X.; Wei, A. Local land surface temperature change induced by afforestation based on satellite observations in Guangdong plantation forests in China. Agric. For. Meteorol. 2019, 276–277, 107641. [Google Scholar] [CrossRef]
- Bowler, D.E.; Buyung-Ali, L.; Knight, T.M.; Pullin, A.S. Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landsc. Urban Plan. 2010, 97, 147–155. [Google Scholar] [CrossRef]
- Zhou, D.; Xiao, J.; Bonafoni, S.; Berger, C.; Deilami, K.; Zhou, Y.; Frolking, S.; Yao, R.; Qiao, Z.; Sobrino, J. Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives. Remote Sens. 2019, 11, 48. [Google Scholar] [CrossRef] [Green Version]
- Schwarz, N.; Lautenbach, S.; Seppelt, R. Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sens. Environ. 2011, 115, 3175–3186. [Google Scholar] [CrossRef]
- Zhou, D.; Zhang, L.; Hao, L.; Sun, G.; Liu, Y.; Zhu, C. Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China. Sci. Total Environ. 2016, 544, 617–626. [Google Scholar] [CrossRef] [PubMed]
- Li, K.; Chen, Y.; Wang, M.; Gong, A. Spatial-temporal variations of surface urban heat island intensity induced by different definitions of rural extents in China. Sci. Total Environ. 2019, 669, 229–247. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Zhou, Y.; Yue, W.; Li, X.; Liu, Y.; Lu, D. Spatiotemporal patterns of summer urban heat island in Beijing, China using an improved land surface temperature. J. Clean. Prod. 2020, 257, 120529. [Google Scholar] [CrossRef]
- Li, X.; Zhou, Y.; Asrar, G.R.; Imhoff, M.; Li, X. The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Sci. Total Environ. 2017, 605–606, 426–435. [Google Scholar] [CrossRef] [PubMed]
- Yao, R.; Wang, L.; Huang, X.; Gong, W.; Xia, X. Greening in Rural Areas Increases the Surface Urban Heat Island Intensity. Geophys. Res. Lett. 2019, 46, 2204–2212. [Google Scholar] [CrossRef]
- Wang, W.; Shu, J. Urban Renewal Can Mitigate Urban Heat Islands. Geophys. Res. Lett. 2020, 47, e2019GL085948. [Google Scholar] [CrossRef] [Green Version]
- Dou, Y.; Kuang, W. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Sci. Total Environ. 2020, 706, 135828. [Google Scholar] [CrossRef]
- Yao, N.; Konijnendijk Van Den Bosch, C.C.; Yang, J.; Devisscher, T.; Wirtz, Z.; Jia, L.; Duan, J.; Ma, L. Beijing’s 50 million new urban trees: Strategic governance for large-scale urban afforestation. Urban For. Urban Green. 2019, 44, 126392. [Google Scholar] [CrossRef]
- Wu, W.; Zhao, S.; Zhu, C.; Jiang, J. A comparative study of urban expansion in Beijing, Tianjin and Shijiazhuang over the past three decades. Landsc. Urban Plan. 2015, 134, 93–106. [Google Scholar] [CrossRef]
- Li, X.; Zhou, W.; Ouyang, Z. Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors? Appl. Geogr. 2013, 38, 1–10. [Google Scholar] [CrossRef]
- Guo, M.; Chen, S.; Wang, W.; Liang, H.; Hao, G.; Liu, K. Spatiotemporal variation of heat fluxes in Beijing with land use change from 1997 to 2017. Phys. Chem. Earth Parts A/B/C 2019, 110, 51–60. [Google Scholar] [CrossRef]
- Liu, K.; Su, H.; Li, X.; Wang, W.; Yang, L.; Liang, H. Quantifying spatial--temporal pattern of urban heat island in Beijing: An improved assessment using land surface temperature (LST) time series observations from LANDSAT, MODIS, and Chinese new satellite GaoFen-1. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 2028–2042. [Google Scholar] [CrossRef]
- Song, X.; Wang, S.; Li, T.; Tian, J.; Ding, G.; Wang, J.; Wang, J.; Shang, K. The impact of heat waves and cold spells on respiratory emergency department visits in Beijing, China. Sci. Total Environ. 2018, 615, 1499–1505. [Google Scholar] [CrossRef] [PubMed]
- Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2007, 27, 3025–3033. [Google Scholar] [CrossRef]
- Deng, C.; Wu, C. BCI: A biophysical composition index for remote sensing of urban environments. Remote Sens. Environ. 2012, 127, 247–259. [Google Scholar] [CrossRef]
- Ridd, M.K. Exploring a VIS (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]
- Baig, M.H.A.; Zhang, L.; Shuai, T.; Tong, Q. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sens. Lett. 2014, 5, 423–431. [Google Scholar] [CrossRef]
- Wan, Z. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sens. Environ. 2008, 112, 59–74. [Google Scholar] [CrossRef]
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- Li, H.; Zhou, Y.; Li, X.; Meng, L.; Wang, X.; Wu, S.; Sodoudi, S. A new method to quantify surface urban heat island intensity. Sci. Total Environ. 2018, 624, 262–272. [Google Scholar] [CrossRef]
- Mann, H.B. Nonparametric tests against trend. Econom. J. Econom. Soc. 1945, 13, 245–259. [Google Scholar] [CrossRef]
- Kendall, M.G. Rank Correlation Methods; Griffin: London, UK, 1975. [Google Scholar]
- Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
- Chen, J.; Lyu, Y.; Zhao, Z.; Liu, H.; Zhao, H.; Li, Z. Using the multidimensional synthesis methods with non-parameter test, multiple time scales analysis to assess water quality trend and its characteristics over the past 25 years in the Fuxian Lake, China. Sci. Total Environ. 2019, 655, 242–254. [Google Scholar] [CrossRef] [PubMed]
- Butler, N.A. The efficiency of ordinary least squares in designed experiments subject to spatial or temporal variation. Stat. Probabil. Lett. 1999, 41, 73–81. [Google Scholar] [CrossRef]
- Peng, J.; Hu, Y.; Liu, Y.; Ma, J.; Zhao, S. A new approach for urban-rural fringe identification: Integrating impervious surface area and spatial continuous wavelet transform. Landsc. Urban Plan. 2018, 175, 72–79. [Google Scholar] [CrossRef]
- Chen, W.; Zhang, Y.; Pengwang, C.; Gao, W. Evaluation of Urbanization Dynamics and its Impacts on Surface Heat Islands: A Case Study of Beijing, China. Remote Sens. 2017, 9, 453. [Google Scholar]
- Yang, Y.; Liu, Y.; Li, Y.; Du, G. Quantifying spatio-temporal patterns of urban expansion in Beijing during 1985–2013 with rural-urban development transformation. Land Use Policy 2018, 74, 220–230. [Google Scholar] [CrossRef]
- Huang, H.; Chen, Y.; Clinton, N.; Wang, J.; Wang, X.; Liu, C.; Gong, P.; Yang, J.; Bai, Y.; Zheng, Y.; et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens. Environ. 2017, 202, 166–176. [Google Scholar] [CrossRef]
- Yao, R.; Wang, L.; Huang, X.; Niu, Y.; Chen, Y.; Niu, Z. The influence of different data and method on estimating the surface urban heat island intensity. Ecol. Indic. 2018, 89, 45–55. [Google Scholar] [CrossRef]
- 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]
- Zhou, D.; Zhao, S.; Liu, S.; Zhang, L.; Zhu, C. Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote Sens. Environ. 2014, 152, 51–61. [Google Scholar] [CrossRef]
- Meng, Q.; Zhang, L.; Sun, Z.; Meng, F.; Wang, L.; Sun, Y. Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China. Remote Sens. Environ. 2018, 204, 826–837. [Google Scholar] [CrossRef]
- Ma, Q.; Wu, J.; He, C. A hierarchical analysis of the relationship between urban impervious surfaces and land surface temperatures: Spatial scale dependence, temporal variations, and bioclimatic modulation. Landsc. Ecol. 2016, 31, 1139–1153. [Google Scholar] [CrossRef]
- Mathew, A.; Khandelwal, S.; Kaul, N. Investigating spatial and seasonal variations of urban heat island effect over Jaipur city and its relationship with vegetation, urbanization and elevation parameters. Sustain. Cities Soc. 2017, 35, 157–177. [Google Scholar] [CrossRef]
- Duncan, J.M.A.; Boruff, B.; Saunders, A.; Sun, Q.; Hurley, J.; Amati, M. Turning down the heat: An enhanced understanding of the relationship between urban vegetation and surface temperature at the city scale. Sci. Total Environ. 2019, 656, 118–128. [Google Scholar] [CrossRef] [PubMed]
- Chun, B.; Guldmann, J. Impact of greening on the urban heat island: Seasonal variations and mitigation strategies. Comput. Environ. Urban Syst. 2018, 71, 165–176. [Google Scholar] [CrossRef]
- Su, Y.; Liu, L.; Liao, J.; Wu, J.; Ciais, P.; Liao, J.; He, X.; Liu, X.; Chen, X.; Yuan, W.; et al. Phenology acts as a primary control of urban vegetation cooling and warming: A synthetic analysis of global site observations. Agric. For. Meteorol. 2020, 280, 107765. [Google Scholar] [CrossRef]
- Peng, S.; Piao, S.; Ciais, P.; Friedlingstein, P.; Ottle, C.; Bréon, F.; Nan, H.; Zhou, L.; Myneni, R.B. Surface Urban Heat Island Across 419 Global Big Cities. Environ. Sci. Technol. 2011, 46, 696–703. [Google Scholar] [CrossRef]
- Oleson, K.W.; Bonan, G.B.; Feddema, J.; Jackson, T. An examination of urban heat island characteristics in a global climate model. Int. J. Climatol. 2011, 31, 1848–1865. [Google Scholar] [CrossRef] [Green Version]
- Waseem, S.; Khayyam, U. Loss of vegetative cover and increased land surface temperature: A case study of Islamabad, Pakistan. J. Clean. Prod. 2019, 234, 972–983. [Google Scholar] [CrossRef]
- Alkama, R.; Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 2016, 351, 600–604. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dinda, S. Environmental Kuznets Curve Hypothesis: A Survey. Ecol. Econ. 2004, 49, 431–455. [Google Scholar] [CrossRef] [Green Version]
Time | Statistical Parameter | Spring | Summer | Autumn | Winter | Warm Season | Cold Season |
---|---|---|---|---|---|---|---|
Daytime | ZMK | - | 2.33 | −0.18 | - | 3.22 | - |
QSen | - | 0.22 | −0.04 | - | 0.12 | - | |
p-value | - | 0.02 * | 0.86 | - | 0.00 * | - | |
Nighttime | ZMK | 1.43 | 0.36 | 1.43 | 2.68 | 0.89 | 2.5 |
QSen | 0.13 | 0.04 | 0.1 | 0.21 | 0.03 | 0.18 | |
p-value | 0.15 | 0.72 | 0.15 | 0.01 * | 0.37 | 0.01 * |
Time | Variable | Coefficient | Std Error | t-Statistic | Probability | Observations | R2 | Adjusted R2 | AICs |
---|---|---|---|---|---|---|---|---|---|
Summer day | FORKDE | −0.0024 | 0.00 | −2.38 | 0.0174 * | 6325 | 0.1273 | 0.1270 | 13701 |
ΔISAKDE | 0.0408 | 0.00 | 30.18 | 0.0000 * | |||||
Intercept | 0.5281 | 0.01 | 37.66 | 0.0000 * | |||||
Winter night | FORKDE | −0.0173 | 0.00 | −27.00 | 0.0000 * | 6325 | 0.2247 | 0.2245 | 7847 |
ΔISAKDE | −0.0235 | 0.00 | −27.60 | 0.0000 * | |||||
Intercept | 1.2660 | 0.01 | 143.42 | 0.0000 * | |||||
Warm season day | FORKDE | 0.0012 | 0.00 | 1.20 | 0.2314 | 6325 | 0.1159 | 0.1156 | 1302 |
ΔISAKDE | 0.0360 | 0.00 | 28.04 | 0.0000 * | |||||
Intercept | −0.0908 | 0.01 | −6.83 | 0.0000 * | |||||
Cold season night | FORKDE | −0.0154 | 0.00 | −25.87 | 0.0000 * | 6325 | 0.1929 | 0.1926 | 6962 |
ΔISAKDE | −0.0188 | 0.00 | −23.67 | 0.0000 * | |||||
Intercept | 1.1680 | 0.01 | 141.90 | 0.0000 * |
Scenarios | Time Divisions | Actual Increase of SUHII | Predicted Increase of SUHII | Difference | Contribution of ISA Change | Contribution of Afforestation |
---|---|---|---|---|---|---|
A: Without afforestation | Summer day | 1.40 | 0.37 | 1.03 | 26.4% | 73.6% |
Warm season day | 0.82 | 0.14 | 0.68 | 17.1% | 82.9% | |
B: Without ISA change | Winter night | 0.98 | 0.19 | 0.79 | 80.6% | 19.4% |
Cold season night | 0.71 | 0.15 | 0.56 | 78.9% | 21.1% |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yao, N.; Huang, C.; Yang, J.; Konijnendijk van den Bosch, C.C.; Ma, L.; Jia, Z. Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis. Remote Sens. 2020, 12, 3906. https://doi.org/10.3390/rs12233906
Yao N, Huang C, Yang J, Konijnendijk van den Bosch CC, Ma L, Jia Z. Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis. Remote Sensing. 2020; 12(23):3906. https://doi.org/10.3390/rs12233906
Chicago/Turabian StyleYao, Na, Conghong Huang, Jun Yang, Cecil C. Konijnendijk van den Bosch, Lvyi Ma, and Zhongkui Jia. 2020. "Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis" Remote Sensing 12, no. 23: 3906. https://doi.org/10.3390/rs12233906