Skip to main content

Advertisement

Log in

Land surface temperature analysis in densely populated zones from the perspective of spectral indices and urban morphology

  • Original Paper
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

There is a relationship between spectral indices, urban morphology, and spatial patterns with land surface temperature (LST). In this paper, LST and spectral indices in Tehran have been calculated using Landsat satellite images from 2000 to 2019. LST was improved and the results were evaluated using synoptic stations in Tehran. Pearson correlation coefficient and mean root error are used to evaluate the accuracy of temperature difference. The spatial variables affecting LST are examined including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Digital Elevation Model (DEM), Building Density (BD), and Road Density (RD). The rise in the temperature of the land surface temperature, which causes the formation of heat islands, is continuously increasing over time and can be seen in some parts of the city. The findings indicate that LST relative accuracy was 0.98 and the root-mean-square error is 2.65 °C. The distance pattern based on the Pearson correlation test showed an effective relationship between LST and the examined indicators. Spots prone to heat islands were identified. Each of the heat island areas of Tehran was studied according to population centers (dense areas using residential, commercial, and industrial lands). The built-up and barren areas index had the highest correlation with surface temperature in most areas. The results showed that regions 9, 13, 18, 21, and especially region 22, which has the most barren land, have the highest surface temperature compared to the surrounding areas. The findings of this study can be used in future urban planning and policy-making.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The municipality of Tehran has provided some of the initial data of this research and is not allowed to publish it. Therefore, it is not possible to make all the initial data available in this article.

References

  • Aghdar H, Shayesteh K, Mohammadyari F, Rangzan K (2020) Evaluation of spatial distribution of earth surface temperature in Behbahan during 2000–2014 period using thermal remote sensing. Hum Geogr Res 52:817–832

    Google Scholar 

  • Akbari H, Kolokotsa D (2016) Three decades of urban heat islands and mitigation technologies research. Energy Build 133:834–842

    Article  Google Scholar 

  • Azhdari A, Soltani A, Alidadi M (2018) Urban morphology and landscape structure effect on land surface temperature: evidence from Shiraz, a semi-arid city. Sustain Cities Soc 41:853–864

    Article  Google Scholar 

  • Bokaie M, Zarkesh MK, Arasteh PD, Hosseini A (2016) Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustain Cities Soc 23:94–104

    Article  Google Scholar 

  • Bokaie M, Shamsipour A, Khatibi P, Hosseini A (2019) Seasonal monitoring of urban heat island using multi-temporal Landsat and MODIS images in Tehran. Int J Urban Sci 23:269–285

    Article  Google Scholar 

  • Brovkin V (2002) Climate-vegetation interaction. In: Journal de Physique IV (Proceedings), 57–72: EDP sciences

  • Caselles V, Coll C, Valor E, Rubio E (1995) Mapping land surface emissivity using AVHRR data application to La Mancha, Spain. Remote Sens Rev 12:311–333

    Article  Google Scholar 

  • Chen X, Zhang Y (2017) Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustain Cities Soc 32:87–99

    Article  Google Scholar 

  • Dai Z, Guldmann J-M, Hu Y (2018) Spatial regression models of park and land-use impacts on the urban heat island in central Beijing. Sci Total Environ 626:1136–1147

    Article  CAS  Google Scholar 

  • Duan S-B, Li Z-L, Wang C, Zhang S, Tang B-H, Leng P, Gao M-F (2019) Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product. Int J Remote Sens 40:1763–1778

    Article  Google Scholar 

  • Estoque RC, Murayama Y, Myint SW (2017) Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Sci Total Environ 577:349–359

    Article  CAS  Google Scholar 

  • Feng Y, Gao C, Tong X, Chen S, Lei Z, Wang J (2019) Spatial patterns of land surface temperature and their influencing factors: a case study in suzhou, China. Remote Sensing

  • Gautam R, Singh MK (2018) Urban heat island over Delhi punches holes in widespread fog in the indo-gangetic plains. Geophys Res Lett 45(2):1114–21

    Article  Google Scholar 

  • Haashemi S, Weng Q, Darvishi A, Alavipanah SK (2016) Seasonal variations of the surface urban heat island in a semi-arid city. Remote Sens 8:352

    Article  Google Scholar 

  • Hernández-Díaz C, Soto-Cervantes J, Corral-Rivas J, Montiel-Antuna E, Alvarado R, Goche-Télles R (2015) Impacts of forest roads on soil in a timber harvesting area in northwestern Mexico (a case study). Croatian J For Eng: J Theory Appl For Eng 36(2):259–267

  • Kafy A-A, Dey NN, Al Rakib A, Rahaman ZA, Nasher NR, Bhatt A (2021) Modeling the relationship between land use/land cover and land surface temperature in Dhaka Bangladesh Using CA-ANN Algorithm. Environ Chall 4:100190

    Article  Google Scholar 

  • Kakavand A, Nikakhtar S, Sardaripour M (2017) Prediction of internet addiction, based on perceived social support, loneliness and social phobia. J Sch Psychol 6:81–98

    Google Scholar 

  • Kamali Maskooni E, Hashemi H, Berndtsson R, Daneshkar Arasteh P, Kazemi M (2021) Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data. Int J Digit Earth 14:250–270

    Article  Google Scholar 

  • Koko AF, Yue W, Abubakar GA, Alabsi AAN, Hamed R (2021) Spatiotemporal influence of land use/land cover change dynamics on surface urban heat island: a case study of Abuja Metropolis, Nigeria. ISPRS Int J Geo-Inf 10:272

    Article  Google Scholar 

  • Kumari B, Tayyab M, Ahmed IA, Baig MRI, Khan MF, Rahman A (2020) Longitudinal study of land surface temperature (LST) using mono-and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arab J Geosci 13:1–19

    CAS  Google Scholar 

  • Lasaponara R, Masini N (2012) Satellite remote sensing: a new tool for archaeology. Springer Science & Business Media

  • Liu H, Zhan Q, Yang C, Wang J (2018) Characterizing the spatio-temporal pattern of land surface temperature through time series clustering: Based on the latent pattern and morphology. Remote sens 10(4):654

  • McFeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432

    Article  Google Scholar 

  • Moonen P, Defraeye T, Dorer V, Blocken B, Carmeliet J (2012) Urban physics: effect of the micro-climate on comfort, health and energy demand. Front Archit Res 1:197–228

    Article  Google Scholar 

  • Morabito M, Crisci A, Messeri A, Orlandini S, Raschi A, Maracchi G, Munafò M (2016) The impact of built-up surfaces on land surface temperatures in Italian urban areas. Sci Total Environ 551:317–326

    Article  Google Scholar 

  • Nadizadeh Shorabeh S, Hamzeh S, Zanganeh Shahraki S, Firozjaei MK, Jokar Arsanjani J (2020) Modelling the intensity of surface urban heat island and predicting the emerging patterns: Landsat multi-temporal images and Tehran as case study. Int J Remote Sens 41:7400–7426

    Article  Google Scholar 

  • Naserikia M, Asadi Shamsabadi E, Rafieian M, Leal Filho W (2019) The urban heat island in an urban context: a case study of Mashhad, Iran. Int J Environ Res Public Health 16:313

    Article  Google Scholar 

  • Qiu GY, Zou Z, Li X, Li H, Guo Q, Yan C, Tan S (2017) Experimental studies on the effects of green space and evapotranspiration on urban heat island in a subtropical megacity in China. Habitat Int 68:30–42

    Article  Google Scholar 

  • Ranagalage M, Estoque RC, Zhang X, Murayama Y (2018) Spatial changes of urban heat island formation in the Colombo district, Sri Lanka: implications for sustainability planning. Sustainability 10:1367

    Article  Google Scholar 

  • Rehman A, Qin J, Pervez A, Khan MS, Ullah S, Ahmad K, Rehman NU (2022) Land-Use/Land cover changes contribute to land surface temperature: a case study of the upper indus basin of Pakistan. Sustain 14(2):934

  • Rose N, Cowie C, Gillett R, Marks GB (2009) Weighted road density: a simple way of assigning traffic-related air pollution exposure. Atmos Environ 43:5009–5014

    Article  CAS  Google Scholar 

  • Ru C, Duan SB; Jiang XG, Li ZL, Jiang Y, Ren H, Leng P, Gao M (2021) Land surface temperature retrieval from Landsat 8 thermal infrared data over urban areas considering geometry effect: method and application. In: IEEE Transactions on geoscience and remote sensing

  • Sekertekin A, Bonafoni S (2020) Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens 12:294

    Article  Google Scholar 

  • Shafizadeh-Moghadam H, Weng Q, Liu H, Valavi R (2020) Modeling the spatial variation of urban land surface temperature in relation to environmental and anthropogenic factors: a case study of Tehran, Iran. Gisci Remote Sens 57:483–496

    Article  Google Scholar 

  • Siddique MA, Dongyun L, Li P, Rasool U, Khan TU, Farooqi TJA, Wang L, Fan B, Rasool MA (2020) Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China. Peerj 8:e9115

    Article  Google Scholar 

  • Snyder WC, Wan Z, Zhang Y, Feng Y-Z (1998) Classification-based emissivity for land surface temperature measurement from space. Int J Remote Sens 19:2753–2774

    Article  Google Scholar 

  • Soltani A, Sharifi E (2017) Daily variation of urban heat island effect and its correlations to urban greenery: a case study of Adelaide. Front Archit Res 6:529–538

    Article  Google Scholar 

  • Song J, Chen W, Zhang J, Huang K, Hou B, Prishchepov AV (2020) Effects of building density on land surface temperature in China: spatial patterns and determinants. Landsc Urban Plan 198:103794

    Article  Google Scholar 

  • Stewart ID (2011) A systematic review and scientific critique of methodology in modern urban heat island literature. Int J Climatol 31:200–217

    Article  Google Scholar 

  • Sun R, Chen L (2017) Effects of green space dynamics on urban heat islands: Mitigation and diversification. Ecosyst Serv 23:38–46

    Article  Google Scholar 

  • Tran H, Uchihama D, Ochi S, Yasuoka Y (2006) Assessment with satellite data of the urban heat island effects in Asian mega cities. Int J Appl Earth Obs Geoinf 8:34–48

  • Valor E, Caselles V (1996) Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote Sens Environ 57:167–184

    Article  Google Scholar 

  • Van TT, Bao HDX (2008) A study on urban development through land surface temperature by using remote sensing: in case of Ho Chi Minh City. VNU J Sci Earth Environ Sci 24

  • Wang Y, Zhan Q, Ouyang W (2019) How to quantify the relationship between spatial distribution of urban waterbodies and land surface temperature? Sci Total Environ 671:1–9

    Article  Google Scholar 

  • Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483

    Article  Google Scholar 

  • Windahl E, de Beurs K (2016) An intercomparison of Landsat land surface temperature retrieval methods under variable atmospheric conditions using in situ skin temperature. Int J Appl Earth Obs Geoinf 51:11–27

    Google Scholar 

  • Xiong Y, Huang S, Chen F, Ye H, Wang C, Zhu C (2012) The impacts of rapid urbanization on the thermal environment: a remote sensing study of Guangzhou, South China. Remote Sens 4:2033–2056

    Article  Google Scholar 

  • Yu X, Guo X, Wu Z (2014) Land surface temperature retrieval from Landsat 8 TIRS—comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing 6:9829–9852

    Article  Google Scholar 

  • Yu Z, Zhang J, Yang G, Schlaberg J (2021) Reverse thinking: a new method from the graph perspective for evaluating and mitigating regional surface heat islands. Remote Sens 13:1127

    Article  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

    Article  Google Scholar 

Download references

Acknowledgements

The paper is excerpted from the research thesis of the first author at Bu-Ali Sina University in Hamedan. The authors would like to thank the University's director of research for supporting this study. The authors would also like to thank the Tehran Municipality for providing us with the master plan for Tehran.

Funding

The authors received no financial support for the research, authorship, and/or publication of this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Heidarimozaffar.

Ethics declarations

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this paper.

Additional information

Editorial responsibility: Shahid Hussain.

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

Ghanbari, R., Heidarimozaffar, M., Soltani, A. et al. Land surface temperature analysis in densely populated zones from the perspective of spectral indices and urban morphology. Int. J. Environ. Sci. Technol. 20, 2883–2902 (2023). https://doi.org/10.1007/s13762-022-04725-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-022-04725-4

Keywords

Navigation