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Investigating and predicting Land Surface Temperature (LST) based on remotely sensed data during 1987–2030 (A case study of Reykjavik city, Iceland)

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Abstract

Observing the state and changes of land cover (LC) is critical for assessing the status and changes of ecosystems such as urban development. Remote sensing can extract useful data from a large region regularly and at the same time. The present study has used Landsat 5 and 8 images to derive LC classification and Land Surface Temperature (LST) maps of Reykjavik city for four years 1987, 1995, 2006, and 2019. Satellite images were classified into four major land-cover classes using the Artificial Neural Network classification (ANN) algorithm. Then, calculations including the LST and the distance from the water were calculated for each study period. Finally, using the Markov chain, the prediction of land cover classes and LST classes for 2020 was obtained. The results have shown an average of 21.5% (8.2 km2), 71% (32.9 km2), -62.9% (43.7 km2), and 3.4% (2.6 km2) growth for Urban Areas (UA), Vegetation Land (VL), Bare Lands (BL), and Water Lands (WL) of Reykjavik evaluated LC classes. Reykjavik LC forecasting shows that UA will have had a 38.5% (8.2 km2), VL a -43% (34.1 km2), BL a 14.7% (3.8 km2), and WL a 14.8% (11.9 km2) by 2030. The mean of LST in this area has risen by 67.5% (7.7 °C) during this period. This is also supported by the findings of the analysis of the NDVI and LST connection. According to the research, the LST and the NDVI value of 0.1 to 0.7 have a positive relationship in this area. However, there is a negative correlation between LST with NDVI values of 0.8 and 0.9. The results showed that the mean of LST was also influenced by the distance from WL water bodies. The LST has grown by a mean of 0.7 of correlation as the distance from WL has increased. The results of this research will be useful in order to get an overview of the past and present situation and plan for the control of heat islands and land cover changes in Reykjavik.

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Contact to irousta@yazd.ac.ir

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Acknowledgements

Iman Rousta is deeply grateful to his supervisor (Haraldur Olafsson, Professor of Atmospheric Sciences, Institute for Atmospheric Sciences-Weather and Climate, and Department of Physics, University of Iceland, and Icelandic Meteorological Office (IMO)), for his great support, kind guidance, and encouragement.

Funding

This work was supported by Vedurfelagid, Rannis and Rannsoknastofa i vedurfraedi.

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Authors and Affiliations

  1. Center for Remote Sensing and GIS Researches, Shahid Beheshti University, Tehran, Iran

    Mohammad Mansourmoghaddam

  2. Department of Geography, Yazd University, Yazd, 8915818411, Iran

    Iman Rousta

  3. Institute for Atmospheric Sciences-Weather and Climate, the University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108, Reykjavik, Iceland

    Iman Rousta

  4. Department of Mathematics, Yazd University, Yazd, 8915818411, Iran

    Mohammadsadegh Zamani

  5. Institute for Atmospheric Sciences-Weather and Climate, and Department of Physics, the University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108, Reykjavik, Iceland

    Haraldur Olafsson

Authors
  1. Mohammad Mansourmoghaddam
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  2. Iman Rousta
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  3. Mohammadsadegh Zamani
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  4. Haraldur Olafsson
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Contributions

I.R., and H.O. proposed the topic, I.R., M.M.M, M.S.Z., and H.O. commanded the data processing, analysis, and wrote the manuscript., I.R., M.M.M, M.S.Z., and H.O. helped to enhance the research design, analysis, interpretation, and manuscript writing. H.O. finalized the manuscript.

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Correspondence to Iman Rousta or Haraldur Olafsson.

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Mansourmoghaddam, M., Rousta, I., Zamani, M. et al. Investigating and predicting Land Surface Temperature (LST) based on remotely sensed data during 1987–2030 (A case study of Reykjavik city, Iceland). Urban Ecosyst 26, 337–359 (2023). https://doi.org/10.1007/s11252-023-01337-9

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  • DOI: https://doi.org/10.1007/s11252-023-01337-9

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