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Impact of Land-Use/Land-Cover Change on Land Surface Temperature Using Satellite Data: A Case Study of Rajarhat Block, North 24-Parganas District, West Bengal

  • Rohit Basu Dhar
  • Surajit Chakraborty
  • Rajib Chattopadhyay
  • Pradip K. SikdarEmail author
Research Article
  • 49 Downloads

Abstract

Increase in land surface temperature (LST) of growing urban areas in the current global warming scenario is a cause of concern for city planners. This study discusses the impact of land-use/land-cover (LULC) change on LST of the area in and around Rajarhat block, North 24-Parganas District, West Bengal, covering an area of 165 km2. Multi-spectral and multi-temporal satellite data from Landsat 5 TM (1990), Landsat 8 OLI (2016) and Sentinel 2A (2016) are used for the LULC mapping, and thermal infrared data from Landsat 5 TM and Landsat 8 TIRS (2016) are used for estimating the LST of 1990 and 2016. Results show that land-use pattern in November has changed in Rajarhat from 1990 to 2016: 13 km2 of vegetation cover lost due to urbanization; 9.3 km2 of open land converted to agricultural land and open fields/parks; 1.4 km2 of aquaculture ponds converted to tree cover/scrublands and 1.45 km2 of lakes/ponds filled up. Loss of vegetation (scrubland and tree) cover resulted in LST rise by about 1.5 °C. Aquaculture ponds have the ability to resist the rise in LST since the increase in temperature of this class is only 0.24 °C due to increase in its area. This change in land-use pattern over 26 years has increased the LST by 0.94 °C. The urban-heat-island (UHI) phenomenon has also increased. The area of the ‘strongest’ heat-island phenomenon, as per UTVFI classification scheme, has increased by 20.1 km2. Positive correlation is observed between NDBI and LST’s of urban areas (r = 0.002 for 1990 and r = 0.047 for 2016) which suggests that urbanization is responsible for the rise in LST. The NCEP NOAA surface temperature model suggests that the long-term trends in the rise in maximum LST over Rajarhat is about 1 °C from January 1990 to November 2016 with 90% confidence level validating the extracted LST data from satellites. Sustainable urban planning is required to arrest the rise in LST which includes urban forestry, construction of water bodies and fountains, preserving existing aquaculture ponds and reducing construction activities.

Keywords

Land use/land cover (LULC) Land surface temperature (LST) Urban heat island (UHI) Remote sensing GIS 

Notes

Acknowledgements

The authors acknowledge the Director, IISWBM, for providing the infrastructural facilities for this research work. The first author thanks P. Chaudhuri and A. Mukhopadhyay of the Department of Environmental Science, University of Calcutta. The fourth author acknowledges the Director, IITM-Pune.

References

  1. Artis, D. A., & Carnahan, W. H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, 12(4), 313–329.CrossRefGoogle Scholar
  2. Barsi, J. A., Schott, J. R., Hook, S. J., Raqueno, N. G., Markham, B. L., & Radocinski, R. G. (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), 11607–11626.CrossRefGoogle Scholar
  3. Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1035.Google Scholar
  4. Congalton, R. G., & Green, K. (1999). Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton: Lewis Publishers.Google Scholar
  5. Congedo, L. (2013). Semi-automatic classification plugin for QGIS. Rome: Sapienza University.Google Scholar
  6. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201.CrossRefGoogle Scholar
  7. Grover, A., & Singh, R. B. (2015). Analysis of urban heat island (UHI) in relation to normalized difference vegetation index (NDVI): A comparative study of Delhi and Mumbai. Environments, 2(2), 125–138.CrossRefGoogle Scholar
  8. Hay, A. (1988). The derivation of global estimates from a confusion matrix. International Journal of Remote Sensing, 9(8), 1395–1398.CrossRefGoogle Scholar
  9. Humes, K., Kustas, W., Moran, M., Nichols, W., & Weltz, M. (1994). Variability of emissivity and surface temperature over a sparsely vegetated surface. Water Resources Research, 30(5), 1299–1310.CrossRefGoogle Scholar
  10. Isaya Ndossi, M., & Avdan, U. (2016). Application of open source coding technologies in the production of land surface temperature (LST) maps from Landsat: A PYQGIS plugin. Remote Sensing, 8(5), 413.CrossRefGoogle Scholar
  11. Jalan, S., & Sharma, K. (2014). Spatio-temporal assessment of land use/land cover dynamics and urban heat island of Jaipur city using satellite data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 767.CrossRefGoogle Scholar
  12. Jiménez-Muñoz, J. C., Sobrino, J. A., Skoković, D., Mattar, C., & Cristóbal, J. (2014). Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, 11(10), 1840–1843.CrossRefGoogle Scholar
  13. Kant, Y., Bharath, B., Mallick, J., Atzberger, C., & Kerle, N. (2009). Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi, India. Journal of the Indian Society of Remote Sensing, 37(2), 201–214.CrossRefGoogle Scholar
  14. Khorram, S. (Ed.). (1999). Accuracy Assessment of Remote Sensing-Derived Change Detection. Bethesda, MD: American Society for Photogrammetry and Remote Sensing.Google Scholar
  15. Kruse, F., Lefkoff, A., Boardman, J., Heidebrecht, K., Shapiro, A., Barloon, P., et al. (1993). The spectral image processing system (SIPS)—Interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44(2–3), 145–163.CrossRefGoogle Scholar
  16. Lilly Rose, A., & Devadas, M. D. (2009). Analysis of land surface temperature and land use/land cover types using remote sensing imagery—A case in Chennai City, India. In The seventh international conference on urban clim held on (Vol. 29).Google Scholar
  17. 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(7), 1535–1552.CrossRefGoogle Scholar
  18. Lucas, I., & van der Wel, F. J. M. (1994). Accuracy assessment of satellite derived landcover data: A review. Photogrammetric Engineering & Remote Sensing, 60(4), 419–426.Google Scholar
  19. Lunetta, R. S., Iiames, J., Knight, J., Congalton, R. G., & Mace, T. H. (2001). An assessment of reference data variability using a “virtual field reference database”. Photogrammetric Engineering and Remote Sensing, 63, 707–715.Google Scholar
  20. Ma, Z., & Redmond, R. L. (1995). Tau coefficients for accuracy assessment of classification of remote sensing data. Photogrammetric Engineering and Remote Sensing, 61(4), 435–439.Google Scholar
  21. Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. Journal of Indian Geophysical Union, 12(3), 131–140.Google Scholar
  22. Mallick, J., Rahman, A., & Singh, C. K. (2013). Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India. Advances in Space Research, 52(4), 639–655.CrossRefGoogle Scholar
  23. Monserud, R. A., & Leemans, R. (1992). Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62(4), 275–293.CrossRefGoogle Scholar
  24. Moran, M. S., Jackson, R. D., Slater, P. N., & Teillet, P. M. (1992). Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote Sensing of Environment, 41(2–3), 169–184.CrossRefGoogle Scholar
  25. NASA, U. (2016). Using Landsat-8 product.Google Scholar
  26. Niclòs, R., Caselles, V., Coll, C., Valor, E., & Rubio, E. (2004). Autonomous measurements of sea surface temperature using in situ thermal infrared data. Journal of Atmospheric and Oceanic Technology, 21(4), 683–692.CrossRefGoogle Scholar
  27. Pal, S., & Ziaul, S. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 125–145.CrossRefGoogle Scholar
  28. QGIS, D. T. (2012). QGIS Geographic Information System. Open Source Geospatial Foundation Project.Google Scholar
  29. Rahman, A., Netzband, M., Singh, A., & Mallick, J. (2009). An assessment of urban environmental issues using remote sensing and GIS techniques: An integrated approach. A case study, Delhi, India. Urban Population-Environment Dynamics in the Developing World: Case Studies an Lessons Learned, International Cooperation in National Research in Demography (CICRED), Paris (pp. 181–211).Google Scholar
  30. Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434–440.CrossRefGoogle Scholar
  31. Stehman, S. V., & Czaplewski, R. L. (1998). Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment, 64(3), 331–344.CrossRefGoogle Scholar
  32. Story, M., & Congalton, R. G. (1986). Accuracy assessment: A user’s perspective. Photogrammetric Engineering and Remote Sensing, 52, 397–399.Google Scholar
  33. Van de Griend, 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.CrossRefGoogle Scholar
  34. Van Deusen, P. C. (1996). Unbiased estimates of class proportions from thematic maps. Photogrammetric Engineering and Remote Sensing, 62, 409–416.Google Scholar
  35. Van Rossum, G. (2007). Python programming language. In USENIX annual technical conference, 2007 (Vol. 31, p. 36).Google Scholar
  36. Vörösmarty, C. J., Green, P., Salisbury, J., & Lammers, R. B. (2000). Global water resources: Vulnerability from climate change and population growth. Science, 289(5477), 284–288.CrossRefGoogle Scholar
  37. Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483.CrossRefGoogle Scholar
  38. Yuan, D. (1997). A simulation comparison of three marginal area estimators for image classification. Photogrammetric Engineering and Remote Sensing, 53(4).Google Scholar
  39. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.CrossRefGoogle Scholar
  40. Zhang, J., Wang, Y., & Li, Y. (2006). A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers & Geosciences, 32(10), 1796–1805.CrossRefGoogle Scholar
  41. Zhou, Q., Robson, M., & Pilesjo, P. (1998). On the ground estimation of vegetation cover in Australian rangelands. International Journal of Remote Sensing, 9, 1815–1820.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  • Rohit Basu Dhar
    • 1
    • 2
  • Surajit Chakraborty
    • 1
  • Rajib Chattopadhyay
    • 3
  • Pradip K. Sikdar
    • 1
    Email author
  1. 1.Department of Environment ManagementIISWBMKolkataIndia
  2. 2.Department of Environmental ScienceUniversity of CalcuttaKolkataIndia
  3. 3.Indian Institute of Tropical MeteorologyPuneIndia

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