Impacts of Built-Up Area Expansion in 2D and 3D on Regional Surface Temperature
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Remote Sensing Images
2.3. Extraction of Land Use Information
2.4. Retrieval of LST
2.5. Impacts of Built-Up Land Expansion on LST
3. Results
3.1. Built-Up Land Expansion
3.2. Impacts of the Expansion of Built-Up Land in 3D on Regional LST
3.3. Impacts of the Expansion of Built-Up Land in 2D on Regional LST
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- World of Change: Global Temperature: Feature Articles. Available online: http://earthobservatory.nasa.gov/Features/WorldOfChange/decadaltemp.php?src=eoa-features (accessed on 10 March 2017).
- Team, C.W.; Pachauri, R.K.; Reisinger, A. Climate Change 2007: Synthesis Report; Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2007; p. 104. [Google Scholar]
- Oleson, K.W.; Monaghan, A.; Wilhelmi, O.; Barlage, M.; Brunsell, N.; Feddema, J.; Hu, L.; Steinhoff, D.F. Interactions between urbanization, heat stress, and climate change. Clim. Chang. 2015, 129, 525–541. [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]
- Kalnay, E.; Cai, M. Impact of urbanization and land-use change on climate. Nature 2003, 423, 528–531. [Google Scholar] [CrossRef] [PubMed]
- Lin, S.; Feng, J.M.; Wang, J.; Hu, Y.H. Modeling the contribution of long-term urbanization to temperature increase in three extensive urban agglomerations in China. J. Geophys. Res. Atmos. 2016, 121, 1683–1697. [Google Scholar] [CrossRef]
- Zhou, L.M.; Dickinson, R.E.; Tian, Y.H.; Fang, J.Y.; Li, Q.X.; Kaufmann, R.K.; Tucker, C.J.; Myneni, R.B. Evidence for a significant urbanization effect on climate in China. Proc. Natl. Acad. Sci. USA 2004, 101, 9540–9544. [Google Scholar] [CrossRef] [PubMed]
- Seto, K.C.; Fragkias, M.; Guneralp, B.; Reilly, M.K. A meta-analysis of global urban land expansion. PLoS ONE 2011, 6, e23777. [Google Scholar] [CrossRef] [PubMed]
- Yang, X.Y.; Li, Y.G.; Luo, Z.W.; Chan, P.W. The urban cool island phenomenon in a high-rise high-density city and its mechanisms. Int. J. Climatol. 2017, 37, 890–904. [Google Scholar] [CrossRef]
- Smoliak, B.V.; Snyder, P.K.; Twine, T.E.; Mykleby, P.M.; Hertel, W.F. Dense network observations of the twin cities canopy-layer urban heat island. J. Appl. Meteorol. Clim. 2015, 54, 1899–1917. [Google Scholar] [CrossRef]
- Wang, K.C.; Jiang, S.J.; Wang, J.K.; Zhou, C.L.; Wang, X.Y.; Lee, X. Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network. J. Geophys. Res. Atmos. 2017, 122, 2131–2154. [Google Scholar] [CrossRef]
- Deng, C.B.; Wu, C.S. Examining the impacts of urban biophysical compositions on surface urban heat island: A spectral unmixing and thermal mixing approach. Remote Sens. Environ. 2013, 131, 262–274. [Google Scholar] [CrossRef]
- Weng, Q.H. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. Isprs. J. Photogramm. 2009, 64, 335–344. [Google Scholar] [CrossRef]
- Nichol, J.E.; Fung, W.Y.; Lam, K.S.; Wong, M.S. Urban heat island diagnosis using aster satellite images and ‘in situ’ air temperature. Atmos. Res. 2009, 94, 276–284. [Google Scholar] [CrossRef]
- Sun, Y.J.; Wang, J.F.; Zhang, R.H.; Gillies, R.R.; Xue, Y.; Bo, Y.C. Air temperature retrieval from remote sensing data based on thermodynamics. Theor. Appl. Climatol. 2005, 80, 37–48. [Google Scholar] [CrossRef]
- Chen, X.L.; Zhao, H.M.; Li, P.X.; Yin, Z.Y. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 2006, 104, 133–146. [Google Scholar] [CrossRef]
- Qiao, Z.; Tian, G.J.; 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]
- Xu, H.Q.; Ding, F.; Wen, X.L. Urban expansion and heat island dynamics in the Quanzhou region, China. IEEE J. Stars 2009, 2, 74–79. [Google Scholar] [CrossRef]
- Zhao, M.Y.; Cai, H.Y.; Qiao, Z.; Xu, X.L. Influence of urban expansion on the urban heat island effect in Shanghai. Int. J. Geogr. Inf. Sci. 2016, 30, 2421–2441. [Google Scholar] [CrossRef]
- Zhou, D.C.; Zhao, S.Q.; Zhang, L.X.; Sun, G.; Liu, Y.Q. The footprint of urban heat island effect in China. Sci. Rep. 2015, 5. [Google Scholar] [CrossRef] [PubMed]
- Arnfield, A.J. Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol. 2003, 23, 1–26. [Google Scholar] [CrossRef]
- Rizwan, A.M.; Dennis, Y.C.L.; Liu, C.H. A review on the generation, determination and mitigation of urban heat island. J. Environ. Sci. 2008, 20, 120–128. [Google Scholar] [CrossRef]
- Feizizadeh, B.; Blaschke, T. Examining urban heat island relations to land use and air pollution: Multiple endmember spectral mixture analysis for thermal remote sensing. IEEE J. Stars 2013, 6, 1749–1756. [Google Scholar] [CrossRef]
- Li, J.J.; Wang, X.R.; Wang, X.J.; Ma, W.C.; Zhang, H. Remote sensing evaluation of urban heat island and its spatial pattern of the shanghai metropolitan area, China. Ecol. Complex. 2009, 6, 413–420. [Google Scholar] [CrossRef]
- Zhou, B.; Rybski, D.; Kropp, J.P. The role of city size and urban form in the surface urban heat island. Sci. Rep. 2017, 7. [Google Scholar] [CrossRef] [PubMed]
- Buyantuyev, A.; Wu, J.G. Urban heat islands and landscape heterogeneity: Linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landsc. Ecol. 2010, 25, 17–33. [Google Scholar] [CrossRef]
- Li, X.M.; Zhou, Y.Y.; Asrar, G.R.; Imhoff, M.; Li, X.C. The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Sci. Total Environ. 2017, 605, 426–435. [Google Scholar] [CrossRef] [PubMed]
- Lindberg, F.; Grimmond, C.S.B. The influence of vegetation and building morphology on shadow patterns and mean radiant temperatures in urban areas: Model development and evaluation. Theor. Appl. Climatol. 2011, 105, 311–323. [Google Scholar] [CrossRef]
- Lindberg, F.; Grimmond, C.S.B. Nature of vegetation and building morphology characteristics across a city: Influence on shadow patterns and mean radiant temperatures in London. Urban Ecosyst. 2011, 14, 617–634. [Google Scholar] [CrossRef]
- Radhi, H.; Fikry, F.; Sharples, S. Impacts of urbanisation on the thermal behaviour of new built up environments: A scoping study of the urban heat island in Bahrain. Landsc. Urban Plan. 2013, 113, 47–61. [Google Scholar] [CrossRef]
- Wong, N.H.; Jusuf, S.K.; Syafii, N.I.; Chen, Y.X.; Hajadi, N.; Sathyanarayanan, H.; Manickavasagam, Y.V. Evaluation of the impact of the surrounding urban morphology on building energy consumption. Sol. Energy 2011, 85, 57–71. [Google Scholar] [CrossRef]
- Emmanuel, R.; Johansson, E. Influence of urban morphology and sea breeze on hot humid microclimate: The case of Colombo, Sri Lanka. Clim. Res. 2006, 30, 189–200. [Google Scholar] [CrossRef]
- Giridharan, R.; Ganesan, S.; Lau, S.S.Y. Daytime urban heat island effect in high-rise and high-density residential developments in Hong Kong. Energy Build. 2004, 36, 525–534. [Google Scholar] [CrossRef]
- Feng, Z.M.; Yang, L.; Yang, Y.Z.; You, Z. The process of population agglomeration/ shrinking and changes in spatial pattern in the Beijing-Tianjin-Hebei metropolitan region. J. Geo-Inf. Sci. 2013, 15, 11–18. [Google Scholar] [CrossRef]
- Chang, N.B.; Han, M.; Yao, W.; Chen, L.C.; Xu, S.G. Change detection of land use and land cover in an urban region with SPOT-5 images and partial lanczos extreme learning machine. J. Appl. Remote Sens. 2010, 4, 043551. [Google Scholar]
- Pongracz, R.; Bartholy, J.; Dezso, Z. Remotely sensed thermal information applied to urban climate analysis. Adv. Space Res. 2006, 37, 2191–2196. [Google Scholar] [CrossRef]
- Liu, J.Y.; Kuang, W.H.; Zhang, Z.X.; Xu, X.L.; Qin, Y.W.; Ning, J.; Zhou, W.C.; Zhang, S.W.; Li, R.D.; Yan, C.Z.; et al. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J. Geogr. Sci. 2014, 24, 195–210. [Google Scholar] [CrossRef]
- Liu, J.Y.; Liu, M.L.; Tian, H.Q.; Zhuang, D.F.; Zhang, Z.X.; Zhang, W.; Tang, X.M.; Deng, X.Z. Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data. Remote Sens. Environ. 2005, 98, 442–456. [Google Scholar] [CrossRef]
- Liu, J.Y.; Zhang, Z.X.; Xu, X.L.; Kuang, W.H.; Zhou, W.C.; Zhang, S.W.; Li, R.D.; Yan, C.Z.; Yu, D.S.; Wu, S.X.; et al. Spatial patterns and driving forces of land use change in China during the early 21st century. J. Geogr. Sci 2010, 20, 483–494. [Google Scholar] [CrossRef]
- Liasis, G.; Stavrou, S. Satellite images analysis for shadow detection and building height estimation. ISPRS J. Photogramm. 2016, 119, 437–450. [Google Scholar] [CrossRef]
- Cheng, F.; Thiel, K.H. Delimiting the building heights in a city from the shadow in a panchromatic SPOT-image.1. Test of 42 buildings. Int. J. Remote Sens. 1995, 16, 409–415. [Google Scholar] [CrossRef]
- Shettigara, V.K.; Sumerling, G.M. Height determination of extended objects using shadows in SPOT images. Photogramm. Eng. Remote Sens. 1998, 64, 35–44. [Google Scholar]
- Weng, Q.H.; Lu, D.S.; Schubring, J. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 2004, 89, 467–483. [Google Scholar] [CrossRef]
- Sobrino, J.A.; Jimenez-Munoz, J.C.; Paolini, L. Land surface temperature retrieval from Landsat TM 5. Remote Sens. Environ. 2004, 90, 434–440. [Google Scholar] [CrossRef]
- Ding, F.; Xu, H.Q. Comparison of three algorithms for retriveving land surface temperature from Landsat TM thermal infrared band. J. Fujian Norm. Univ. (Nat. Sci. Ed.) 2008, 24, 91–96. [Google Scholar]
- Hale, R.C.; Gallo, K.P.; Owen, T.W.; Loveland, T.R. Land use/land cover change effects on temperature trends at U.S. Climate normals stations. Geophys. Res. Lett. 2006, 33. [Google Scholar] [CrossRef]
- Jones, P.D.; Groisman, P.Y.; Coughlan, M.; Plummer, N.; Wang, W.C.; Karl, T.R. Assessment of urbanization effects in time-series of surface air-temperature over land. Nature 1990, 347, 169–172. [Google Scholar] [CrossRef]
- Jones, P.D.; Lister, D.H.; Li, Q. Urbanization effects in large-scale temperature records, with an emphasis on China. J. Geophys. Res. Atmos. 2008, 113. [Google Scholar] [CrossRef]
- Kuang, W.H.; Dou, Y.Y.; Zhang, C.; Chi, W.F.; Liu, A.L.; Liu, Y.; Zhang, R.H.; Liu, J.Y. Quantifying the heat flux regulation of metropolitan land use/land cover components by coupling remote sensing modeling with in situ measurement. J. Geophys. Res. Atmos. 2015, 120, 113–130. [Google Scholar] [CrossRef]
- Li, J.X.; Song, C.H.; Cao, L.; Zhu, F.G.; Meng, X.L.; Wu, J.G. Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sens. Environ. 2011, 115, 3249–3263. [Google Scholar] [CrossRef]
- Ren, G.Y.; Zhou, Y.Q.; Chu, Z.Y.; Zhou, J.X.; Zhang, A.Y.; Guo, J.; Liu, X.F. Urbanization effects on observed surface air temperature trends in north China. J. Clim. 2008, 21, 1333–1348. [Google Scholar] [CrossRef]
- Weng, Q. A remote sensing-gis evaluation of urban expansion and its impact on surface temperature in the Zhujiang delta, China. Int. J. Remote Sens. 2001, 22, 1999–2014. [Google Scholar]
- Zhang, Y.S.; Balzter, H.; Wu, X.C. Spatial-temporal patterns of urban anthropogenic heat discharge in Fuzhou, China, observed from sensible heat flux using Landsat TM/ETM plus data. Int. J. Remote Sens. 2013, 34, 1459–1477. [Google Scholar] [CrossRef]
- Weng, Q.H.; Lu, D.S. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. Int. J. Appl. Earth Obs. 2008, 10, 68–83. [Google Scholar] [CrossRef]
- Weng, Q.H.; Rajasekar, U.; Hu, X.F. Modeling urban heat islands and their relationship with impervious surface and vegetation abundance by using ASTER images. IEEE Trans. Geosci. Remote Sens. 2011, 49, 4080–4089. [Google Scholar] [CrossRef]
Land Use Types | Number of Test Samples | Number of Correctly Interpreted Samples | Accuracy (%) |
---|---|---|---|
Cropland | 93 | 87 | 93.5 |
Forest | 21 | 19 | 90.5 |
Grassland | 22 | 19 | 86.4 |
Water and wetland | 20 | 19 | 95.0 |
LMRB land | 32 | 31 | 96.9 |
HRB land | 24 | 22 | 91.7 |
Rural settlements | 38 | 35 | 92.1 |
Industrial and mining land | 28 | 26 | 92.9 |
Urban green land | 22 | 20 | 90.0 |
Total | 300 | 278 | 92.7 |
Level Name | Level Label | Classification Criteria |
---|---|---|
Very low level | 1 | |
Slightly low level | 2 | |
Moderate level | 3 | |
Slightly high level | 4 | |
Very high level | 5 |
Average LST Changes (°C) | Between the Transformed Land from LMRB To HRB and the Non-Transformed LMRB Land | |||||||
---|---|---|---|---|---|---|---|---|
300 (m) | 500 (m) | 700 (m) | 1000 (m) | 1500 (m) | 2000 (m) | 3000 (m) | 5000 (m) | |
Sample number | 26 | 30 | 36 | 37 | 39 | 41 | 41 | 41 |
LMRB to HRB (LHT) | 3.53 | 3.29 | 3.50 | 3.55 | 3.59 | 3.74 | 3.74 | 3.74 |
Non-transformed LMRB (NLT) | 4.82 | 5.14 | 5.26 | 5.21 | 5.39 | 5.47 | 5.47 | 5.48 |
LHT − NLT (Number and percentage of samples where LHT − NLT < 0) | −1.29 (20, 77%) | −1.85 (25, 83%) | −1.75 (27, 75%) | −1.66 (28, 76%) | −1.80 (32, 82%) | −1.73 (32, 78%) | −1.73 (32, 78%) | −1.74 (32, 78%) |
Average LST Changes (°C) | Among the Non-Transformed Cropland and the Transformed Lands from Cropland to HRB Land and Cropland to LMRB Land | |||||||
---|---|---|---|---|---|---|---|---|
300 (m) | 500 (m) | 700 (m) | 1000 (m) | 1500 (m) | 2000 (m) | 3000 (m) | 5000 (m) | |
Sample number | 4 | 6 | 8 | 9 | 17 | 19 | 29 | 36 |
Cropland to HRB (CHT) | 6.37 | 5.97 | 6.46 | 6.70 | 6.34 | 6.78 | 6.68 | 6.46 |
Cropland to LMRB (CLT) | 7.09 | 7.28 | 7.39 | 7.37 | 7.33 | 7.15 | 7.20 | 7.56 |
Non-transformed cropland (NCT) | 6.77 | 5.47 | 5.98 | 5.59 | 5.90 | 5.67 | 5.69 | 5.71 |
CHT − CLT (Number and percentage of samples where CHT − CLT < 0) | −0.72 (3, 75%) | −1.31 (5, 83%) | −0.93 (6, 75%) | −0.67 (5, 56%) | −0.99 (11, 65%) | −0.37 (11, 58%) | −0.52 (15, 52%) | −1.10 (24, 67%) |
CHT − NCT (Number and percentage of samples where CHT − NCT > 0) | −0.40 (2, 50%) | 0.50 (5, 83%) | 0.48 (5, 63%) | 1.11 (6, 67%) | 0.44 (9, 53%) | 1.11 (14, 74%) | 0.99 (19, 66%) | 0.75 (23, 64%) |
CLT − NCT (Number and percentage of samples where CLT − NCT > 0) | 0.31 (2, 50%) | 1.82 (6, 100%) | 1.41 (8, 100%) | 1.78 (9, 100%) | 1.44 (14, 82%) | 1.48 (17, 89%) | 1.51 (26, 90%) | 1.86 (31, 86%) |
Average LST Changes (°C) | Among the Non-Transformed Urban Green Land and the Transformed Lands from Urban Green Land to HRB Land and Urban Green Land to LMRB Land | |||||||
---|---|---|---|---|---|---|---|---|
300 (m) | 500 (m) | 700 (m) | 1000 (m) | 1500 (m) | 2000 (m) | 3000 (m) | 5000 (m) | |
Sample number | 2 | 2 | 2 | 3 | 7 | 9 | 12 | 17 |
Urban green land to HRB (GHT) | 4.20 | 4.20 | 4.20 | 4.74 | 4.58 | 4.32 | 4.57 | 5.03 |
Urban green land to LMRB (GLT) | 5.62 | 5.62 | 5.36 | 6.07 | 6.62 | 6.59 | 6.93 | 6.71 |
Non-transformed urban green land (NGT) | 6.74 | 6.74 | 7.42 | 6.52 | 5.69 | 5.95 | 6.09 | 6.12 |
GHT − GLT (Number and percentage of samples where GHT − GLT < 0) | −1.42 (1, 50%) | −1.42 (1, 50%) | −1.16 (1, 50%) | −1.33 (2, 67%) | −2.04 (5, 71%) | −2.26 (7, 78%) | −2.36 (10, 83%) | −1.69 (13, 76%) |
GHT − NGT (Number and percentage of samples where GHT − NGT > 0) | −2.54 (0, 0%) | −2.54 (0, 0%) | −3.22 (0, 0%) | −1.79 (1, 33%) | −1.11 (3, 43%) | −1.62 (3, 33%) | −1.52 (3, 25%) | −1.10 (5, 29%) |
GLT − NGT (Number and percentage of samples where GLT − NGT > 0) | −1.12 (1, 50%) | −1.12 (1, 50%) | −2.06 (0, 0%) | −0.46 (1, 33%) | 0.93 (4, 57%) | 0.64 (6, 67%) | 0.84 (8, 67%) | 0.59 (11, 65%) |
© 2017 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
Cai, H.; Xu, X. Impacts of Built-Up Area Expansion in 2D and 3D on Regional Surface Temperature. Sustainability 2017, 9, 1862. https://doi.org/10.3390/su9101862
Cai H, Xu X. Impacts of Built-Up Area Expansion in 2D and 3D on Regional Surface Temperature. Sustainability. 2017; 9(10):1862. https://doi.org/10.3390/su9101862
Chicago/Turabian StyleCai, Hongyan, and Xinliang Xu. 2017. "Impacts of Built-Up Area Expansion in 2D and 3D on Regional Surface Temperature" Sustainability 9, no. 10: 1862. https://doi.org/10.3390/su9101862