Skip to main content
SpringerLink
Log in

Impact of built environment on urban surface temperature based on multi-source data at the community level in Beilin District, Xi’an, China

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

With the global warming and rapid urbanization in China, the urban built environment has undergone rapid changes, and the land surface temperatures (LSTs) of urban communities have obvious spatial heterogeneity. To explore the key driving factors of community LSTs, the multi-source data and spatial statistical methods being jointly used to analyze the spatial characteristics and main influencing factors of LST at the community level in the Beilin District of Xi’an City, China. The results are as follows: (1) Compared with communities dominated by construction land, communities with large area of green space and water bodies have lower LST. (2) According to the Akaike’s information criterion (AICc) and maximum of adjusted R2, and other parameters, the No.1236 model was selected as the optimal model to analyze the influencing factors of community LST by exploratory data analysis, including building density (BD), building height standard deviation (BHS), percentage of public administration and public services land (PASL), percentage of green space and square land (PGSL), population density (POPD), normalized difference impervious surface index (NDISI), and perimeter-area fractal dimension (PAFRAC). (3) For each increase of one unit in NDISI and BHS when other factors remain unchanged, the LST will increase by 0.569 °C and decrease by 0.478 °C, respectively. (4) From the spatial stability and distribution of Local-R2, the warming factors of community LST are mainly NDISI, PAFRAC, BD, and PASL, while the cooling factors are BHS and PGSL. The spatial heterogeneity of community LST is not only related to the change of underlying surface properties but is also affected by intra-urban architectural morphology. Therefore, reasonable planning of urban built environment is of great significance for mitigating heat islands.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets used and analyzed during the current study available from the corresponding author on reasonable request.

References

  • Adulkongkaew T, Satapanajaru T, Charoenhirunyingyos S, Singhirunnusorn W (2020) Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area. Thailand Heliyon 6:e04485

    Google Scholar 

  • Afrakhteh R, Asgarian A, Sakieh Y, Soffianian A (2016) Evaluating the strategy of integrated urban-rural planning system and analyzing its effects on land surface temperature in a rapidly developing region. Habitat Int 56:147–156

    Google Scholar 

  • Alexander C (2020) Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). Int J Appl Earth Obs Geoinf 86:102013

    Google Scholar 

  • Alexander C (2021) Influence of the proportion, height and proximity of vegetation and buildings on urban land surface temperature. Int J Appl Earth Obs Geoinf 95:102265

    Google Scholar 

  • Asgarian A, Amiri BJ, Sakieh Y (2014) Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosystems 18:209–222

    Google Scholar 

  • Bechtel B, Demuzere M, Mills G, Zhan W, Sismanidis P, Small C, Voogt J (2019) SUHI analysis using Local climate zones—a comparison of 50 cities. Urban Climate 28:100451

    Google Scholar 

  • Bian G, Gao X, Zou Q, Cheng Q, Sun T, Sha S, Zhen M (2023) Effects of thermal environment and air quality on outdoor thermal comfort in urban parks of Tianjin. China, Environmental Science and Pollution Research

    Google Scholar 

  • Bowler DE, Buyung-Ali L, Knight TM, Pullin AS (2010) Urban greening to cool towns and cities: a systematic review of the empirical evidence. Landsc Urban Plan 97:147–155

    Google Scholar 

  • Buyantuyev A, Wu J (2009) Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecol 25:17–33

    Google Scholar 

  • Cao X, Onishi A, Chen J, Imura H (2010) Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landsc Urban Plan 96:224–231

    Google Scholar 

  • Cervero R, Kockelman K (1997) Travel demand and the 3Ds: density, diversity, and design. Transp Res Part D 2:199–219

    Google Scholar 

  • Chang Y, Xiao J, Li X, Weng Q (2023) Monitoring diurnal dynamics of surface urban heat island for urban agglomerations using ECOSTRESS land surface temperature observations. Sustain Cities Soc 98:104833

    Google Scholar 

  • Chen Y, Shan B, Yu X (2022) Study on the spatial heterogeneity of urban heat islands and influencing factors. Build Environ 208:108604

    Google Scholar 

  • Connors JP, Galletti CS, Chow WTL (2012) Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecol 28:271–283

    Google Scholar 

  • Das N, Mondal P, Sutradhar S, Ghosh R (2021) Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. Egyp J Remote Sens Space Sci 24:131–149

    Google Scholar 

  • Deilami K, Kamruzzaman M, Liu Y (2018) Urban heat island effect: a systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int J Appl Earth Obs Geoinf 67:30–42

    Google Scholar 

  • Ezimand K, Kakroodi AA, Kiavarz M (2018) The development of spectral indices for detecting built-up land areas and their relationship with land-surface temperature. Int J Remote Sens 39:8428–8449

    Google Scholar 

  • Fan C, Myint SW, Zheng B (2015) Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures. Prog Phys Geogr: Earth Environ 39:199–219

    Google Scholar 

  • Fang L, Li H, Li M (2019) Does hotel location tell a true story? Evidence from geographically weighted regression analysis of hotels in Hong Kong. Tour Manage 72:78–91

    Google Scholar 

  • Fang X, Shi X, Gao W (2021) Measuring urban sustainability from the quality of the built environment and pressure on the natural environment in China: a case study of the Shandong Peninsula region. J Clean Prod 289:125145

    Google Scholar 

  • Gago EJ, Roldan J, Pacheco-Torres R, Ordóñez J (2013) The city and urban heat islands: a review of strategies to mitigate adverse effects. Renew Sustain Energy Rev 25:749–758

    Google Scholar 

  • Gohain KJ, Mohammad P, Goswami A (2020) Assessing the impact of land use land cover changes on land surface temperature over Pune city, India. Quat Int 575–576:259–269

    Google Scholar 

  • Guo A, Yang J, Sun W, Xiao X, Xia Cecilia J, Jin C, Li X (2020) Impact of urban morphology and landscape characteristics on spatiotemporal heterogeneity of land surface temperature. Sustain Cities Soc 63:102443

    Google Scholar 

  • Guo A, Yang J, Xiao X, Xia J, Jin C, Li X (2020) Influences of urban spatial form on urban heat island effects at the community level in China. Sustain Cities Soc 53:101972

    Google Scholar 

  • Huang G, Cadenasso ML (2016) People, landscape, and urban heat island: dynamics among neighborhood social conditions, land cover and surface temperatures. Landscape Ecol 31:2507–2515

    Google Scholar 

  • Hulley GC, Ghent D, Göttsche FM, Guillevic PC, Mildrexler DJ, Coll C (2019) Land surface temperature. Taking the Temperature of the Earth, Chapter 3:57–127. https://doi.org/10.1016/B978-0-12-814458-9.00003-4

  • Kolokotroni M, Ren X, Davies M, Mavrogianni A (2012) London’s urban heat island: impact on current and future energy consumption in office buildings. Energy Build 47:302–311

    Google Scholar 

  • Kotharkar R, Surawar M (2016) Land use, land cover, and population density impact on the formation of canopy urban heat islands through traverse survey in the Nagpur urban area, India. J Urban Plan Dev 142:04015003

    Google Scholar 

  • Kotharkar R, Bagade A, Ramesh A (2019) Assessing urban drivers of canopy layer urban heat island: a numerical modeling approach. Landsc Urban Plan 190:103586

    Google Scholar 

  • Lei Y, Wang Z, Wang D, Zhang X, Che H, Yue X, Tian C, Zhong J, Guo L, Li L, Zhou H, Liu L, Xu Y (2023) Co-benefits of carbon neutrality in enhancing and stabilizing solar and wind energy. Nat Clim Chang 13:693–700

    Google Scholar 

  • Logan TM, Zaitchik B, Guikema S, Nisbet A (2020) Night and day: the influence and relative importance of urban characteristics on remotely sensed land surface temperature. Remote Sens Environ 247:111861

    Google Scholar 

  • Mashhoodi B (2020) Land surface temperature and energy expenditures of households in the Netherlands: winners and losers. Urban Climate 34:100678

    Google Scholar 

  • Mohammad P, Goswami A, Chauhan S, Nayak S (2022) Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city. Urban Climate, India, p 42

    Google Scholar 

  • Nichol JE (1996) High-resolution surface temperature patterns related to urban morphology in a tropical city: a satellite-based study. J Appl Meteorol 35:135–146

    Google Scholar 

  • Nse OU, Okolie CJ, Nse VO (2020) Dynamics of land cover, land surface temperature and NDVI in Uyo City, Nigeria. Sci Afr 10:e00599. https://doi.org/10.1016/j.sciaf.2020.e00599

  • Peng J, Jia J, Liu Y, Li H, Wu J (2018) Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas. Remote Sens Environ 215:255–267

    Google Scholar 

  • Roy S, Pandit S, Eva EA, Bagmar MSH, Papia M, Banik L, Dube T, Rahman F, Razi MA (2020) Examining the nexus between land surface temperature and urban growth in Chattogram Metropolitan Area of Bangladesh using long term Landsat series data. Urban Climate 32:100593

    Google Scholar 

  • Sannigrahi S, Bhatt S, Rahmat S, Paul SK, Sen S (2018) Estimating global ecosystem service values and its response to land surface dynamics during 1995–2015. J Environ Manage 223:115–131

    Google Scholar 

  • Santamouris M, Kolokotsa D (2015) On the impact of urban overheating and extreme climatic conditions on housing, energy, comfort and environmental quality of vulnerable population in Europe. Energy Build 98:125–133

    Google Scholar 

  • Sekertekin A, Zadbagher E (2021) Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecol Ind 122:107230

    Google Scholar 

  • Soydan O (2020) Effects of landscape composition and patterns on land surface temperature: urban heat island case study for Nigde. Turkey Urban Climate 34:100688

    Google Scholar 

  • Sun D, Zhou L, Li Y, Liu H, Shen X, Wang Z, Wang X (2017) New-type urbanization in China: predicted trends and investment demand for 2015–2030. J Geog Sci 27:943–966

    Google Scholar 

  • Taloor AK, Drinder Singh M, Chandra Kothyari G (2021) Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data. Appl Comput Geosci 9:100051

    Google Scholar 

  • Theeuwes NE, Steeneveld G-J, Ronda RJ, Rotach MW, Holtslag AAM (2015) Cool city mornings by urban heat. Environ Res Lett 10:114022

    Google Scholar 

  • Voelkel J, Hellman D, Sakuma R, Shandas V (2018) Assessing vulnerability to urban heat: a study of disproportionate heat exposure and access to refuge by socio-demographic status in Portland, Oregon. Int J Environ Res Public Health 15:640

    Google Scholar 

  • Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86:370–384

    Google Scholar 

  • Wang R, Murayama Y (2020) Geo-simulation of land use/cover scenarios and impacts on land surface temperature in Sapporo. Jpn Sustain Cities Soc 63:102432

    Google Scholar 

  • Wicki A, Parlow E (2017) Multiple regression analysis for unmixing of surface temperature data in an urban environment. Remote Sens 9:684

    Google Scholar 

  • Wu Z, Yao L, Zhuang M, Ren Y (2020) Detecting factors controlling spatial patterns in urban land surface temperatures: a case study of Beijing. Sustain Cities Soc 63:102454

    Google Scholar 

  • Xi C, Han L, Wang J, Feng Z, Kumar P, Cao S-J (2023) How can greenery space mitigate urban heat island? An analysis of cooling effect, carbon sequestration, and nurturing cost at the street scale. J Clean Prod 419:138230

    Google Scholar 

  • Yang C, Yan F, Zhang S (2020) Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city. J Environ Manage 265:110563

    Google Scholar 

  • Yang J, Zhou J, Göttsche F-M, Long Z, Ma J, Luo R (2020) Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data. Int J Appl Earth Obs Geoinf 91:102136

    Google Scholar 

  • Yuan F, Bauer ME (2007) Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ 106:375–386

    Google Scholar 

  • Zheng Z, Zhou W, Yan J, Qian Y, Wang J, Li W (2019) The higher, the cooler? Effects of building height on land surface temperatures in residential areas of Beijing. Phys Chem Earth, Parts a/b/c 110:149–156

    Google Scholar 

  • Zhou D, Xiao J, Bonafoni S, Berger C, Deilami K, Zhou Y, Frolking S, Yao R, Qiao Z, Sobrino J (2018) Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sens 11:48

    Google Scholar 

  • Zhou G, Wang H, Chen W, Zhang G, Luo Q, Jia B (2019) Impacts of urban land surface temperature on tract landscape pattern, physical and social variables. Int J Remote Sens 41:683–703

    Google Scholar 

Download references

Funding

This research was funded by the “The Program of National Natural Science Foundation of China” (No. 41971178).

Author information

Authors and Affiliations

  1. School of Public Affairs, Zhejiang University, Hangzhou, 310058, China

    Dianyuan Zheng

  2. College of Urban and Environmental Sciences, Northwest University, Xi’an, 710127, China

    Xiaojun Huang, Mingyue Qi, Xin Zhao, Yuxing Zhang & Minghan Yang

  3. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi’an, 710127, China

    Xiaojun Huang

  4. Shaanxi Xi’an Urban Forest Ecosystem Research Station, Xi’an, 710127, China

    Xiaojun Huang

Authors
  1. Dianyuan Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaojun Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingyue Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuxing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Minghan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZD interpreted the data, reviewed the literature, and was the major contributor in writing the manuscript. QM, ZX, ZY, and YM contributed in the research article in collecting data from different sources, analyzing the data. Reviewing the final manuscript was done by ZD and HX.

Corresponding author

Correspondence to Xiaojun Huang.

Ethics declarations

Ethics approval

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, D., Huang, X., Qi, M. et al. Impact of built environment on urban surface temperature based on multi-source data at the community level in Beilin District, Xi’an, China. Environ Sci Pollut Res 30, 111410–111422 (2023). https://doi.org/10.1007/s11356-023-30119-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-023-30119-1

Keywords

Search

Navigation