Advertisement

Monitoring spatio-temporal dynamics of urban and peri-urban land transitions using ensemble of remote sensing spectral indices—a case study of Chennai Metropolitan Area, India

  • Mathan M.Email author
  • Krishnaveni M.
Article
  • 125 Downloads

Abstract

Land-use/land-cover change is the most vulnerable factor in any developing urban environment. Increased infrastructure and population density tend to alter the land features which in turn will have an impact on climate change and will increase the impervious layer. Study of trends in land-use/land-cover change is required for analyzing the possible ways of managing the natural system. In this study, the spatial and temporal changes of the urban and peri-urban landscape of the Chennai Metropolitan Area (CMA), Tamil Nadu, India, were analyzed using satellite images. Imageries from Landsat 5 (TM) and Landsat 8 (OLI/TIRS) sensors were taken for the years 1988, 1997, 2006, and 2017. Ensembles of remote sensing spectral indices (NDVI, MNDWI, NDBI, and NDBaI) were calculated for the land-use/land-cover classification. The confusion matrix was used for assessing the accuracy for the year 2017. The overall accuracy of the LULC classification obtained was 91.76% with the kappa coefficient of 0.84. The results show that during the period of February 1988 to February 2017, the agriculture/fallow land, barren/semi-barren, vegetation, and water bodies/wetlands have decreased by 53.62%, 1.45%, 58.99%, and 30.59%, respectively. This decrease has contributed to an increase of 173.83% in built-up area. About 26,881 ha of agriculture/fallow land, 10,482 ha of vegetation land, and 2454 ha of water bodies/wetlands were converted to built-up and other land-use over the period. This essentially meant that CMA has changed from predominantly an agricultural area (42.21%) in 1988 to built-up area (48.72%) in 2017.

Keywords

Spectral indices Land-use/land-cover Urban sprawl Remote sensing GIS CMA 

Notes

References

  1. Aithal, B. H., & Ramachandra, T. V. (2016). Visualization of urban growth pattern in Chennai using geoinformatics and spatial metrics. Journal of the Indian Society of Remote Sensing, 44(4), 617–633.  https://doi.org/10.1007/s12524-015-0482-0.CrossRefGoogle Scholar
  2. Akbar, T. A., Hassan, Q. K., Ishaq, S., Batool, M., Butt, H. J., & Jabbar, H. (2019). Investigative spatial distribution and modelling of existing and future urban land changes and its impact on urbanization and economy. Remote Sensing, 11(2).  https://doi.org/10.3390/rs11020105.CrossRefGoogle Scholar
  3. Appiah, D., Schröder, D., Forkuo, E., & Bugri, J. (2015). Application of geo-information techniques in land-use and/land-cover change analysis in a peri-urban district of Ghana. ISPRS International Journal of Geo-Information, 4, 1265–1289.  https://doi.org/10.1021/acscatal.7b00844.CrossRefGoogle Scholar
  4. Aronoff, S. (1989). Geographic information systems: a management perspective. Geocarto International, 4(4).  https://doi.org/10.1080/10106048909354237.CrossRefGoogle Scholar
  5. Bakr, N., Weindorf, D. C., Bahnassy, M. H., Marei, S. M., & El-Badawi, M. M. (2010). Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography, 30(4).  https://doi.org/10.1016/j.apgeog.2009.10.008.CrossRefGoogle Scholar
  6. Banzhaf, E., Grescho, V., & Kindler, A. (2009). Monitoring urban to peri-urban development with integrated remote sensing and GIS information: a Leipzig, Germany case study. International Journal of Remote Sensing, 30(7), 1675–1696.  https://doi.org/10.1080/01431160802642297.CrossRefGoogle Scholar
  7. Bouzekri, S., Lasbet, A. A., & Lachehab, A. (2015). A new spectral index for extraction of built-up area using Landsat-8 data. Journal of the Indian Society of Remote Sensing, 43(4), 867–873.  https://doi.org/10.1007/s12524-015-0460-6.CrossRefGoogle Scholar
  8. Campbell, M., Congalton, R. G., Hartter, J., & Ducey, M. (2015). Optimal land cover mapping and change analysis in northeastern oregon using Landsat imagery. Photogrammetric Engineering and Remote Sensing, 81(1), 37–47.  https://doi.org/10.14358/PERS.81.1.37.CrossRefGoogle Scholar
  9. Census of India, 2011. (2011). Census of India 2011. State of Literacy. Google Scholar
  10. Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land-use/land-cover changes. Remote Sensing of Environment, 104(2), 133–146.  https://doi.org/10.1016/j.rse.2005.11.016.CrossRefGoogle Scholar
  11. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1).  https://doi.org/10.1016/0034-4257(91)90048-B.CrossRefGoogle Scholar
  12. Datta, P. (2007). Urbanisation in India urbanisation in India full paper introduction. Africa. doi: https://doi.org/10.1121/1.2021445.CrossRefGoogle Scholar
  13. Dekolo, S., & Olayinka, D. (2013). Monitoring peri-urban land-use change with multi-temporal Landsat imagery. Urban and Regional Data Management, (May), 145–159.  https://doi.org/10.1201/b14914-18.CrossRefGoogle Scholar
  14. Du, X., Jin, X., Yang, X., Yang, X., & Zhou, Y. (2014). Spatial pattern of land-use change and its driving force in Jiangsu province. International Journal of Environmental Research and Public Health, 11(3), 3215–3232.  https://doi.org/10.3390/ijerph110303215.CrossRefGoogle Scholar
  15. Ettehadi Osgouei, P., & Kaya, S. (2017). Analysis of land cover/use changes using Landsat 5 TM data and indices. Environmental Monitoring and Assessment, 189(4), 136.  https://doi.org/10.1007/s10661-017-5818-5.CrossRefGoogle Scholar
  16. Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). NDVI: vegetation change detection using remote sensing and GIS - a case study of Vellore district. Procedia Computer Science, 57, 1199–1210.  https://doi.org/10.1016/j.procs.2015.07.415.CrossRefGoogle Scholar
  17. Imam, A. U. K., & Banerjee, U. K. (2016). Urbanisation and greening of Indian cities: problems, practices, and policies. Ambio, 45(4), 442–457.  https://doi.org/10.1007/s13280-015-0763-4.CrossRefGoogle Scholar
  18. Lakshmi, S. V., & Thomas, S. (2018). Mapping of land use and land cover changes in Chennai using GIS and remote sensing. International Journal of Pure and Applied Mathematics, 119(17), 11–21.Google Scholar
  19. Mistowakapuja, F., Liwa, E., & Kashaigili, J. (2013). Usage of indices for extraction of built-up areas and vegetation features from Landsat TM image: a case of Dar Es Salaam and Kisarawe peri-urban areas. Tanzania, 3(7), 273–283.  https://doi.org/10.5923/j.ijaf.20130307.04.CrossRefGoogle Scholar
  20. Mohajane, M., Essahlaoui, A., Oudija, F., El Hafyani, M., El Hmaidi, A., El Ouali, A., et al. (2018). Land-use/land-cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou forest, in the central middle Atlas of Morocco. Environments, 5(12), 131.  https://doi.org/10.3390/environments5120131.CrossRefGoogle Scholar
  21. Muthamilselvan, A., Srimadhi, K., Ramalingam, N., & Pavithra, P. (2016). Urbanization and its related environmental problem in Srirangam island, Tiruchirappalli district of Tamil Nadu, India-thermal remote sensing and GIS approach. Environmental Earth Sciences, 75(9), 1–13.  https://doi.org/10.1007/s12665-016-5457-0.CrossRefGoogle Scholar
  22. Patel, S. K., Verma, P., & Shankar Singh, G. (2019). Agricultural growth and land-use land-cover change in peri-urban India. Environmental Monitoring and Assessment, 191(9), 1–17.  https://doi.org/10.1007/s10661-019-7736-1.CrossRefGoogle Scholar
  23. Prabu, P., & Dar, M. A. (2018). Land-use/cover change in Coimbatore urban area (Tamil Nadu, India)—a remote sensing and GIS-based study. Environmental Monitoring and Assessment, 190(8), 445.  https://doi.org/10.1007/s10661-018-6807-z.CrossRefGoogle Scholar
  24. Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) symposium, 1, 309–317 doi:citeulike-article-id:12009708.Google Scholar
  25. Schneider, A. (2012). Remote sensing of environment monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 124, 689–704.  https://doi.org/10.1016/j.rse.2012.06.006.CrossRefGoogle Scholar
  26. Sekar, S. P., & Kanchanamala, S. (2011). An analysis of growth dynamics in Chennai Metropolitan area. Institute of Town Planners, j8-4, 31–57.Google Scholar
  27. Şen, G., Güngör, E., & Şevik, H. (2018). Defining the effects of urban expansion on land-use/land-cover change: a case study in Kastamonu, Turkey. Environmental Monitoring and Assessment, 190(8), 454.  https://doi.org/10.1007/s10661-018-6831-z.CrossRefGoogle Scholar
  28. Sengupta, T., Remote, M., Application, S., Road, S. A., Sharma, S., & Science, C. (2019). Innovative use of band ratioing for delineation of urban areas. International Journal for Innovative Research in Science & Technology, 5(12), 1–5.Google Scholar
  29. Singh, R. P., Mukherjee, S., Singh, S., & Singh, N. (2016). Normalized difference vegetation index (NDVI) based classification to assess the change in land-use/land-cover (LULC) in Lower Assam, India. International Journal of Advanced Remote Sensing and GIS, 5(10).  https://doi.org/10.23953/cloud.ijarsg.74.CrossRefGoogle Scholar
  30. Sinha, P., & Verma, N. K. (2016). Urban built-up area extraction and change detection of Adama municipal area using time-series Landsat Images. International Journal of Advanced Remote Sensing and GIS, 5(8), 1886–1895.  https://doi.org/10.23953/cloud.ijarsg.67.CrossRefGoogle Scholar
  31. Song, Y., & Ma, M. (2011). A statistical analysis of the relationship between climatic factors and the normalized difference vegetation index in China. International Journal of Remote Sensing, 32(14), 3947–3965.  https://doi.org/10.1080/01431161003801336.CrossRefGoogle Scholar
  32. UN. (2014). World urbanization prospects: the 2014 revision. Department of Economic and Social Affairs.  https://doi.org/10.4054/DemRes.2005.12.9.CrossRefGoogle Scholar
  33. United Nations. (2012). World population prospects: the 2012 revision, high lights and advance tables. Department of Economic and Social Affairs, Population Division.  https://doi.org/10.1111/j.1728-4457.2010.00357.x.CrossRefGoogle Scholar
  34. Valbuena, D., Verburg, P. H., Bregt, A. K., & Ligtenberg, A. (2010). An agent-based approach to model land-use change at a regional scale. Landscape Ecology, 25(2).  https://doi.org/10.1007/s10980-009-9380-6.CrossRefGoogle Scholar
  35. Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2006). Evaluating urban expansion and land-use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape and Urban Planning, 75(1–2), 69–80.  https://doi.org/10.1016/j.landurbplan.2004.12.005.CrossRefGoogle Scholar
  36. Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033.  https://doi.org/10.1080/01431160600589179.CrossRefGoogle Scholar
  37. Xu, H. (2008). A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 29(14), 4269–4276.  https://doi.org/10.1080/01431160802039957.CrossRefGoogle Scholar
  38. 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.  https://doi.org/10.1080/01431160304987.CrossRefGoogle Scholar
  39. Zhao, H., & Chen, X. (2005). Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+, (December), 1666–1668.  https://doi.org/10.1109/igarss.2005.1526319.
  40. Zhibin, R., Haifeng, Z., Xingyuan, H., Dan, Z., & Xingyang, Y. (2015). Estimation of the relationship between urban vegetation configuration and land surface temperature with remote sensing. Journal of the Indian Society of Remote Sensing, 43(1), 89–100.  https://doi.org/10.1007/s12524-014-0373-9.CrossRefGoogle Scholar
  41. Zhou, Y., Yang, G., Wang, S., Wang, L., Wang, F., & Liu, X. (2014). A new index for mapping built-up and bare land areas from Landsat-8 OLI data. Remote Sensing Letters, 5(10), 862–871.  https://doi.org/10.1080/2150704X.2014.973996.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Water ResourcesAnna UniversityChennaiIndia

Personalised recommendations