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Spatial and temporal analysis of urban heat island using Landsat satellite images

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Abstract

The average land surface temperature (LST) of Earth has increased since the late nineteenth century due to the warming of the Earth’s atmosphere. Increased surface temperatures, especially in cities, are a significant environmental problem that intensifies urban heat islands (UHIs). In this study, land surface temperature, urban thermal field variance index (UTFVI), and UHI index were mapped using Landsat 4, 5, 7, and 8 satellite images to identify the distribution and determine the intensities of the UHI. Maps of land use at multi-year intervals between 1995 and 2016 were created using the support vector machine (SVM) method. These were used to compare LST variations to land-use changes and to determine the linkages between the two. The results showed that the highest recorded temperatures in Ahvaz, the capital of Khozestan Province, Iran, occurred in areas of bare land (42.93°C) and residential development (40.06°C) in 2017. Land use classification showed that the highest classification accuracy (in 2016) was 93%. The most varying extents of land use in Ahvaz were bare lands, residential lands, and green spaces. Green spaces in the study area in 1995 and 2016 covered 14% and 7% of the area, respectively, which showed a 50% reduction in green space over 21 years. A composite map of UTFVI and UHI showed that the locations classified as very hot had the worst UTFVI. The results of this study of Ahvaz, Iran’s heat islands, can inform and guide urban planners in locational matters and in efforts to mitigate and adapt changing land uses in order to limit the intensification of the UHI.

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References

  • Alahmad B, Tomasso LP, Al-Hemoud A, James P, Koutrakis P (2020) Spatial distribution of land surface temperatures in kuwait: urban heat and cool islands. Int J Environ Res Public Health 17:2993

    Article  Google Scholar 

  • Amiri R, Weng Q, Alimohammadi A, Alavipanah SK (2009) Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing Environ 113:2606–2617

    Article  Google Scholar 

  • Asadifard E, Masoudi M (2018) Status and prediction of carbon monoxide as an air pollutant in Ahvaz City, Iran. Caspian J Environ Sci 16:203–213

    Google Scholar 

  • Ashie Y, Ca VT, Asaeda T (1999) Building canopy model for the analysis of urban climate. J Wind Eng Ind Aerodynamics 81:237–248

    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. Sustainable Cities Soc 23:94–104

    Article  Google Scholar 

  • Borojerdnia A, Rozbahani MM, Nazarpour A, Ghanavati N, Payandeh K (2020) Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran. Sci Total Environ 740:140103

    Article  CAS  Google Scholar 

  • Bouyer J, Musy M, Huang Y, Athamena K (2009) Mitigating urban heat island effect by urban design: forms and materials, Proceedings of the 5th urban research symposium, cities and climate change: responding to an urgent agenda, Marseille, pp 28–30

  • Buyantuyev A, Wu J (2010) Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecol 25:17–33

    Article  Google Scholar 

  • Cerdà A, Rodrigo-Comino J, Giménez-Morera A, Keesstra SD (2018) Hydrological and erosional impact and farmer’s perception on catch crops and weeds in citrus organic farming in Canyoles river watershed, Eastern Spain. Agric Ecosyst Environ 258:49–58

    Article  Google Scholar 

  • Chan CK, Yao X (2008) Air pollution in mega cities in China. Atmospheric Environ 42:1–42

    Article  CAS  Google Scholar 

  • Changnon SA, Kunkel KE, Reinke BC (1996) Impacts and responses to the 1995 heat wave: a call to action. Bull Am Meteorological Soc 77:1497–1506

    Article  Google Scholar 

  • Choudhury D, Das K, Das A (2019) Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. Egypt J Remote Sensing Space Sci 22:203–218

    Google Scholar 

  • Cox PM, Betts RA, Jones CD, Spall SA, Totterdell IJ (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408:184–118

    Article  CAS  Google Scholar 

  • Deng JS, Wang K, Hong Y, Qi JG (2009) Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape Urban Plan 92:187–198

    Article  Google Scholar 

  • Gartland LM (2012) Heat islands: understanding and mitigating heat in urban areas. Routledge

  • Ghanavati N, Nazarpour A, De Vivo B (2019) Ecological and human health risk assessment of toxic metals in street dusts and surface soils in Ahvaz, Iran. Environ Geochem Health 41:875–891

    Article  CAS  Google Scholar 

  • Guha S, Govil H, Dey A, Gill N (2018) Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. Eur J Remote Sensing 51:667–678

    Article  Google Scholar 

  • Huang Q, Huang J, Yang X, Fang C, Liang Y (2019) Quantifying the seasonal contribution of coupling urban land use types on Urban Heat Island using Land Contribution Index: a case study in Wuhan, China. Sustainable Cities Society 44:666–675

    Article  Google Scholar 

  • Jain S, Sannigrahi S, Sen S, Bhatt S, Chakraborti S, Rahmat S (2020) Urban heat island intensity and its mitigation strategies in the fast-growing urban area. J Urban Manag 9:54–66

    Article  Google Scholar 

  • Jiang J, Tian G (2010) Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environ Sci 2:571–575

    Article  Google Scholar 

  • Keesstra S, Mol G, De Leeuw J, Okx J, De Cleen M, Visser S (2018) Soil-related sustainable development goals: four concepts to make land degradation neutrality and restoration work. Land 7:133

    Article  Google Scholar 

  • Keesstra SD, Bouma J, Wallinga J, Tittonell P, Smith P, Cerdà A, Montanarella L, Quinton JN, Pachepsky Y, Van Der Putten WH (2016) The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil 2:111–128

    Article  Google Scholar 

  • Konopacki S, Akbari H (2002): Energy savings for heat-island reduction strategies in Chicago and Houston (including updates for Baton Rouge, Sacramento, and Salt Lake City).

  • Lal R (2004) Soil carbon sequestration impacts on global climate change and food security. Science 304:1623–1627

    Article  CAS  Google Scholar 

  • Li H, Meier F, Lee X, Chakraborty T, Liu J, Schaap M, Sodoudi S (2018) Interaction between urban heat island and urban pollution island during summer in Berlin. Sci Total Environ. 636:818–828

    Article  CAS  Google Scholar 

  • Li W, Ouyang Z, Zhou W, Chen Q (2011) Effects of spatial resolution of remotely sensed data on estimating urban impervious surfaces. J Environ Sci 23:1375–1383

    Article  Google Scholar 

  • Liu L, Zhang Y (2011) Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing 3:1535–1552

    Article  Google Scholar 

  • Liu Y, Kuang Y-q, Wu Z-f (2006) Impact of land use on urban land surface temperature-a case study of Dongguan, Guangdong Province. Scientia Geographica Sinica 26:597

    Google Scholar 

  • Luck M, Wu J (2002) A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landscape Ecol 17:327–339

    Article  Google Scholar 

  • Maimaitiyiming M, Ghulam A, Tiyip T, Pla F, Latorre-Carmona P, Halik Ü, Sawut M, Caetano M (2014) Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS J Photogrammetry Remote Sensing 89:59–66

    Article  Google Scholar 

  • Mantero P, Moser G, Serpico SB (2005) Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Trans Geosci Remote Sensing 43:559–570

    Article  Google Scholar 

  • Morais MV, EDd F, Marciotto ER, Urbina Guerrero VV, Martins LD, Martins JA (2018) Implementation of observed sky-view factor in a mesoscale model for sensitivity studies of the urban meteorology. Sustainability 10:2183

    Article  Google Scholar 

  • Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogrammetry Remote Sensing 66:247–259

    Article  Google Scholar 

  • Nassar AK, Blackburn GA, Whyatt JD (2016) Dynamics and controls of urban heat sink and island phenomena in a desert city: development of a local climate zone scheme using remotely-sensed inputs. Int J Appl Earth Observation Geoinform 51:76–90

    Article  Google Scholar 

  • Oke T (1995) The heat island of the urban boundary layer: characteristics, causes and effects, Wind climate in cities. Springer, pp 81–107

  • Oke TR, Kalanda B, Steyn D (1981) Parameterization of heat storage in urban areas. Urban Ecology 5:45–54

    Article  Google Scholar 

  • Oliveira S, Andrade H, Vaz T (2011) The cooling effect of green spaces as a contribution to the mitigation of urban heat: a case study in Lisbon. Building Environ 46:2186–2194

    Article  Google Scholar 

  • Otukei JR, Blaschke T (2010) Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int J Appl Earth Observ Geoinformation 12:S27–S31

    Article  Google Scholar 

  • Portela CI, Massi KG, Rodrigues T, Alcântara E (2020) Impact of urban and industrial features on land surface temperature: Evidences from satellite thermal indices. Sustainable Cities Society 56:102100

    Article  Google Scholar 

  • Rosenfeld AH, Akbari H, Bretz S, Fishman BL, Kurn DM, Sailor D, Taha H (1995) Mitigation of urban heat islands: materials, utility programs, updates. Energy Buildings 22:255–265

    Article  Google Scholar 

  • Schatz J, Kucharik CJ (2014) Seasonality of the urban heat island effect in Madison, Wisconsin. J Appl Meteorology Climatology 53:2371–2386

    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. GIScience Remote Sensing 57:483–496

    Article  Google Scholar 

  • Shafri HZM, Ramle FSH (2009) A comparison of support vector machine and decision tree classifications using satellite data of Langkawi island. Inf Technol J 8(1):64–70

    Article  Google Scholar 

  • Shao M, Tang X, Zhang Y, Li W (2006) City clusters in China: air and surface water pollution. Front Ecol Environ 4:353–361

    Article  Google Scholar 

  • Shi J, Jiang L, Zhang L, Chen K-S, Wigneron J-P, Chanzy A, Jackson TJ (2006) Physically based estimation of bare-surface soil moisture with the passive radiometers. IEEE Trans Geoscience Remote Sensing 44:3145–3153

    Article  Google Scholar 

  • Shojaei P, Gheysari M, Myers B, Eslamian S, Shafieiyoun E, Esmaeili H (2017) Effect of different land cover/use types on canopy layer air temperature in an urban area with a dry climate. Building Environ 125:451–463

    Article  Google Scholar 

  • Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing Environ 90:434–440

    Article  Google Scholar 

  • Sodoudi S, Zhang H, Chi X, Müller F, Li H (2018) The influence of spatial configuration of green areas on microclimate and thermal comfort. Urban Forestry Urban Greening 34:85–96

    Article  Google Scholar 

  • Song J, Wang Z-H (2015) Interfacing the urban land–atmosphere system through coupled urban canopy and atmospheric models. Boundary-layer Meteorology 154:427–448

    Article  Google Scholar 

  • Su Y-F, Foody GM, Cheng K-S (2012) Spatial non-stationarity in the relationships between land cover and surface temperature in an urban heat island and its impacts on thermally sensitive populations. Landscape Urban Plann 107:172–180

    Article  Google Scholar 

  • Sun J, Zhou G (2010) Review of advances in measurements and effects of diffuse radiation on terrestrial ecosystem productivity. J Plant Ecol (Chinese Version) 34:452–461

    Google Scholar 

  • Taati A, Sarmadian F, Mousavi A, Pour CTH, Shahir AHE (2015) Land use classification using support vector machine and maximum likelihood algorithms by Landsat 5 TM images. Walailak J Sci Technol (WJST) 12(8):681–687

    Google Scholar 

  • Tong H, Walton A, Sang J, Chan JC (2005) Numerical simulation of the urban boundary layer over the complex terrain of Hong Kong. Atmos Environ. 39:3549–3563

    Article  CAS  Google Scholar 

  • Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Networks 10:988–999

    Article  CAS  Google Scholar 

  • Visser S, Keesstra S, Maas G, De Cleen M (2019) Soil as a basis to create enabling conditions for transitions towards sustainable land management as a key to achieve the SDGs by 2030. Sustainability 11:6792

    Article  Google Scholar 

  • Wang C, Myint SW, Wang Z, Song J (2016) Spatio-temporal modeling of the urban heat island in the Phoenix metropolitan area: land use change implications. Remote Sensing 8:185

    Article  Google Scholar 

  • Wang R, Hou H, Murayama Y, Derdouri A (2020) Spatiotemporal analysis of land use/cover patterns and their relationship with land surface temperature in Nanjing, China. Remote Sensing 12:440

    Article  Google Scholar 

  • Weng Q (2001) A remote sensing? GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. Int J Remote Sensing 22:1999–2014

    Google Scholar 

  • Xu L, Xie X, Li S (2013) Correlation analysis of the urban heat island effect and the spatial and temporal distribution of atmospheric particulates using TM images in Beijing. Environ Pollut 178:102–114

    Article  CAS  Google Scholar 

  • Yang J, Wang Z-H (2015) Optimizing urban irrigation schemes for the trade-off between energy and water consumption. Energy Buildings 107:335–344

    Article  Google Scholar 

  • Yang Y, Xie J, Sheng H, Chen G, Li X, Yang Z (2009) The impact of land use/cover change on storage and quality of soil organic carbon in midsubtropical mountainous area of southern China. J Geograph Sci 19:49–57

    Article  Google Scholar 

  • Zarasvandi A, Carranza E, Moore F, Rastmanesh F (2011) Spatio-temporal occurrences and mineralogical–geochemical characteristics of airborne dusts in Khuzestan Province (southwestern Iran). J Geochemical Exploration 111:138–151

    Article  CAS  Google Scholar 

  • Zhang Y, Yu T, Gu XF, Zhang YX, Chen LF (2006) Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect. J Remote Sensing 10:789

    Google Scholar 

  • Zhao W, He J, Wu Y, Xiong D, Wen F, Li A (2019) An analysis of land surface temperature trends in the central Himalayan region based on MODIS products. Remote Sensing 11:900

    Article  Google Scholar 

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Funding

This work was supported by the College of Agriculture, Shiraz University (Grant No. 98GRC1M271143).

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Authors and Affiliations

  1. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Atiyeh Amindin, Soheila Pouyan & Hamid Reza Pourghasemi

  2. Soil Conservation and Watershed Management Research Department, Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, AREEO, Shahrekord, Iran

    Saleh Yousefi

  3. Department of Geography, Texas State University, San Marcos, TX, USA

    John P. Tiefenbacher

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  1. Atiyeh Amindin
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  2. Soheila Pouyan
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  3. Hamid Reza Pourghasemi
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  4. Saleh Yousefi
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  5. John P. Tiefenbacher
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Contributions

AA, SP, HRP, SY, and JPT designed the experiments, ran models, analyzed the results, and wrote and reviewed the manuscript. The author(s) read and approved the final manuscript.

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Correspondence to Hamid Reza Pourghasemi.

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Amindin, A., Pouyan, S., Pourghasemi, H.R. et al. Spatial and temporal analysis of urban heat island using Landsat satellite images. Environ Sci Pollut Res 28, 41439–41450 (2021). https://doi.org/10.1007/s11356-021-13693-0

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