Skip to main content

Advertisement

SpringerLink
Log in

Exploring the Relationship Between Spatio-temporal Land Cover Dynamics and Surface Temperature Over Dehradun Urban Agglomeration, India

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

In present study, using artificial neural network (ANN), the land cover maps for three years (i.e. 2000, 2010 and 2019) were derived from Landsat optical data and the decadal spatio-temporal land cover dynamics was analysed. The classes delineated were built-up (urban and suburban), cultivated, vegetation, bare soil and river courses. Subsequently, the land cover change patterns were correlated with the LST values, which were retrieved from Landsat thermal data using mono-widow algorithm. The spatio-temporal clustering of high and low LST values (i.e. LST hot and cold spots) over different land covers, with special emphasis on built-up areas, was carried out. The variation in human thermal comfort levels during the period 2000–2019 was also investigated using thermal field variance index. The domain of the present study was Dehradun urban agglomeration.

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

Source: landsat 87, 23 Feb 2019)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Ahmed, S. (2017). Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sensing and GIS techniques. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 15–25.

    Article  Google Scholar 

  • Alfraihat, R., Mulugeta, G., & Gala, T. S. (2016). Ecological evaluation of urban heat island in Chicago City, USA. Journal of Atmospheric Pollution, 4(1), 23–29.

    Google Scholar 

  • Anderson, J R., Hardy E. E., Roach, J. T., and Witmer, R. E., (1976). A land use and land cover classification system for use with remote sensor. USGS Professional Paper 964; Washington, DC.

  • Arora, M. K., Das Gupta, A. S., & Gupta, R. P. (2004). An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) valley. Himalayas. International Journal of Remote Sensing, 25(3), 559–572.

    Article  Google Scholar 

  • Balew, A., & Korme, T. (2020). Monitoring land surface temperature in Bahir Dar city and its surrounding using Landsat images. The Egyptian Journal of Remote Sensing and Space Science, 3, 371–386.

    Article  Google Scholar 

  • Census of India (2011). Office of the registrar general and census commissioner, New Delhi, India. Available from: https://censusindia.gov.in.

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

    Article  Google Scholar 

  • ESRI (2016). How hot spot analysis (Getis-Ord Gi) works. Accessed April 16, 2020 from http://proarcgis.com/en/pro-app/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gispatial-stati.htm.

  • Gohain, K. J., Mohammad, P., & Goswami, A. (2020). Assessing the impact of land use land cover changes on land surface temperature over Pune city. India: Quaternary International.

    Book  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. European Journal of Remote Sensing, 51(1), 667–678.

    Article  Google Scholar 

  • Guha, S., Govil, H., & Diwan, P. (2019). Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. Journal of Applied Remote Sensing, 13(2), 024518.

    Google Scholar 

  • Guha, S., Govil, H., & Mukherjee, S. (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city India. Journal of Applied Remote Sensing, 11(3), 036020.

    Article  Google Scholar 

  • Guo, L., Liu, R., Men, C., Wang, Q., Miao, Y., & Zhang, Y. (2019). Quantifying and simulating landscape composition and pattern impacts on land surface temperature: A decadal study of the rapidly urbanizing city of Bei Jing, China. Science of The Total Environment, 654, 430–440.

    Article  Google Scholar 

  • Hang, H. T., & Rahman, A. (2018). Characterization of thermal environment over heterogeneous surface of National Capital Region (NCR), India using LANDSAT-8 sensor for regional planning studies. Urban climate, 24, 1–18.

    Article  Google Scholar 

  • Hasan, M. N., Hossain, M. S., Bari, M. A., and Islam, M. R. (2013). Agricultural land availability in Bangladesh. SRDI, Dhaka, Bangladesh, 42 pp. Landsat satellite imagery of path, 136, 5.

  • Hoffmann, E. M., Konerding, V., Nautiyal, S., & Buerkert, A. (2019). Is the push-pull paradigm useful to explain rural-urban migration? A case study in Uttarakhand. India: PloS one. https://doi.org/10.1371/journal.pone.0214511.

    Book  Google Scholar 

  • Hush, D. R., 1989. Classification with neural networks: A performance analysis. In: Proceedings of the IEEE International conference on systems engineering, 24–26 August 1989, Ohio, USA, 277–280.

  • Kakon, A. N., Nobuo, M., Kojima, S., & Yoko, T. (2010). Assessment of thermal comfort in respect to building height in a high-density city in the tropics. American Journal of Engineering and Applied Sciences, 3(3), 545–551.

    Article  Google Scholar 

  • Kanellopoulos, I., & Wilkinson, G. G. (1997). Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 18(4), 711–725.

    Article  Google Scholar 

  • Kanungo, D. P., et al. (2006). A comparative study of conventional ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85, 347–366.

    Article  Google Scholar 

  • Kavzoglu, T., & Mather, P. M. (2003). The use of back propagation artificial neural networks in land cover classification. International Journal of Remote Sensing, 24(23), 4097–4938.

    Article  Google Scholar 

  • Li, J. (2006). Estimating Land Surface Temperature from Landsat-5 TM. Remote Sensing Technology and Application, 21(4), 322–326.

    Google Scholar 

  • Maithani, S. (2014). Neural networks-based simulation of land cover scenarios in Doon valley. India. Geocarto International, 30(2), 163–185.

    Google Scholar 

  • Maithani, S., Begum, A., Kumar, P., & Kumar, A. S. (2017). Simulation of peri-urban growth dynamics using weights of evidence approach. Geocarto International, 33(9), 957–976.

    Article  Google Scholar 

  • Matzarakis, A., Mayer, H., & Iziomon, M. G. (1999). Applications of a universal thermal index: Physiological equivalent temperature. International journal of biometeorology, 43(2), 76–84.

    Article  Google Scholar 

  • Mukherjee, S., Joshi, P. K., & Garg, R. D. (2017). Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data. Geocarto International, 6049, 1–19. https://doi.org/10.1080/10106049.2016.1222634.

    Article  Google Scholar 

  • Paola, J. D. (1994). Neural network classification of multispectral imagery. MSc thesis, The University of Arizona, Tucson, USA.

  • Qin, Z., Karnieli, A., & Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International journal of remote sensing, 22(18), 3719–3746.

    Article  Google Scholar 

  • Roy, S., Pandit, S., Eva, E. A., Bagmar, M. S. H., Papia, M., Banik, L., et al. (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.

    Article  Google Scholar 

  • Rumelhart, D. E., Hinton, G. E., & Williams, . (1986). Learning representations by Back-propagating errors. Nature, 23, 533–536.

    Article  Google Scholar 

  • Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment, 90(4), 434–440.

    Article  Google Scholar 

  • Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119–132.

    Article  Google Scholar 

  • United Nations, Department of economic and social affairs, population division (2019). World urbanization prospects: The 2018 revision (ST/ESA/SER.A/420). New York: United Nations.

  • Van de Griend, A. A., & Owe, M. (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of remote sensing, 14(6), 1119–1131.

    Article  Google Scholar 

  • Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote sensing of environment, 86(3), 370–384.

    Article  Google Scholar 

  • Wang, F. (1994). The use of artificial neural networks in geographical information system for agricultural land-suitability assessment. Environment and Planning A, 26, 265–284.

    Article  Google Scholar 

  • Willett, K. M., & Sherwood, S. (2012). Exceedance of heat index thresholds for 15 regions under a warming climate using the wet-bulb globe temperature. International Journal of Climatology, 32(2), 161–177.

    Article  Google Scholar 

  • Yang, J., & Qiu, J. (1996). The empirical expressions of the relation between precipitable water and ground water vapor pressure for some areas in China. Scientia Atmospherica Sinica, 20, 620–626.

    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(10), 9829–9852.

    Article  Google Scholar 

  • Zhang, Y. (2006). Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect. Journal of remote sensing, 10, 789797.

    Google Scholar 

  • Zhang, Z., He, G., Wang, M., Long, T., Wang, G., Zhang, X., & Jiao, W. (2016). Towards an operational method for land surface temperature retrieval from Landsat 8 data. Remote sensing letters, 7(3), 279–288.

    Article  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge Department of Science and Technology, Government of India for providing support through the INSPIRE fellowship to Ms. Garima Nautiyal (first author), Doon University.

Author information

Authors and Affiliations

  1. School of Environment and Natural Resources, Doon University, Dehradun, India

    Garima Nautiyal & Archana Sharma

  2. Urban and Regional Studies Department, Indian Institute of Remote Sensing, Dehradun, India

    Sandeep Maithani

Authors
  1. Garima Nautiyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandeep Maithani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Archana Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sandeep Maithani.

Additional information

Publisher's Note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nautiyal, G., Maithani, S. & Sharma, A. Exploring the Relationship Between Spatio-temporal Land Cover Dynamics and Surface Temperature Over Dehradun Urban Agglomeration, India. J Indian Soc Remote Sens 49, 1307–1318 (2021). https://doi.org/10.1007/s12524-021-01323-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-021-01323-8

Keywords

Search

Navigation