Decoding Patterns of Urban Dynamics in Class-1 City of Khammam, Telangana State, India

Research Article
  • 33 Downloads

Abstract

Quantitative analysis of urban sprawl enables to understand the status, growth, direction, spatial pattern of urban growth. Spatio-temporal change analysis is essential to monitor and understand LULC dynamics. Present study illustrate use of IRS LISS4 and LANDSAT-5 (MSS) remote sensing data of 1991–2015, for change detection of LULC by quadrant and spatial metrics analysis in Class-1 town of Khammam in Telangana in south central India. Study revealed that extent of built-up area grew over 3.78–22.43 km2 in 15 years by > 500% pushing beyond municipal limits and onto peri urban area. Development of urban sprawl into peri urban area has been around 8 km2 during 2001–2015. Six landscape metrics were used to analyze to understand landscape characteristics in relation to urban growth size, shape, pattern; LecoS tool of QuantumGIS was used to compute Number of Patches, Patch Density, Edge Length, Fractal Dimension Index, Shannon’s Diversity Index and Shannon’s Equitable index. Results indicate that fragmentation of LULC increased due to pressure of anthropogenic activity. Core area of Khammam town is dense with single patch and compact shape, whereas, new patches of built up area of town are fragmented. Agriculture area is dominant patch with higher edge length. SDI value: 1.39 and SEI: 0.66 in 2015, show high diversity and low evenness among classes; dispersed fragmented outgrowth of urban sprawl, mostly towards NE and Eastern directions in a ‘leap-frog pattern’.

Keywords

Urban pattern dynamics Spatial metrics Entropy LecoS Landscape analysis Leap-frog pattern 

Notes

Acknowledgements

The authors are thankful to Principal Scientist, GIS Lab and Director CRIDA for their encouragement and guidance. Thanks are due to Director, NRSC/ISRO and Deputy Director, RSAA for their kind support. The authors expresses gratitude to Director, DTCP, Hyderabad and his team for assistance during field visit and for providing maps/data of Khammam town. The authors express thanks to Head of Department of CSIT, and Chairperson BOS, JNTU Hyderabad for support and encouragement. Thanks to USGS–NASA Science Team for making available the LANDSAT-5 Data.

Funding

This work is a part of the authors Ph.D. Program

Compliance with ethical standards

Conflict of interest

No potential conflict of interest was reported by the author.

Supplementary material

12524_2017_718_MOESM1_ESM.tiff (348 kb)
Fig. 1 Shannon’s Diversity Index vs. Shannon’s Equitable Index (TIFF 348 kb)

References

  1. Abebe, G. A. (2013). Quantifying urban growth pattern in developing countries using remote sensing and spatial metrics: A case study in Kampala. Ph.D. Thesis: University of Twente, Netherland.Google Scholar
  2. Aithal, B. H., Setturu, B., Sreekantha, S., Sanna Durgappa, D., & Ramachandra, T. V. (2012). Spatial patterns of urbanization in Mysore: Emerging Tier II City in Karnataka. In Proceedings of NRSC user interaction meet2012, 16th and 17th February 2012, Hyderabad.Google Scholar
  3. Angel, S., Parent, J., Civco, D. (2007). Urban sprawl metrics: An analysis of global urban expansion using GIS. In Proceedings of ASPRS 2007 annual conference, Tampa, Florida May 7–11. http://clear.uconn.edu/publications/research/techpapers/AngeletalASPRS2007.pdf.
  4. Bogaert, J., Biloso, A., Vranken, I., & André, M. (2015). Peri-urban dynamics: Landscape ecology perspectives. Territoires Periurbains. Developpement, Enjeuxet Perspectives Dans Les Pays Du Sud., pp. 59–69. Belgique: Les presses agronomiques de gembloux.Google Scholar
  5. Choudhary, K., Singh, M., & Kupriyanov, A. (2017). Landscape analysis through remote sensing and GIS techniques: A case study of Astrakhan, Russia. In Eighth international conference on graphic and image processing (ICGIP. 10225 (Icgip 2016), 1–6.  https://doi.org/10.1117/12.2266245.
  6. Dikshit, J. K. (2011). The urban fringe of Indian cities (pp. 1–279). Jaipur, India: Rawat Publications.Google Scholar
  7. Gibert, K., & Sanchez-Marre, M. (2011). Outcomes from the iEMSs data mining in the environmental sciences work shop series. Environmental Modelingand Software, 26(7), 983–985.CrossRefGoogle Scholar
  8. Gustafson, E. J. (1998). Quantifying landscape spatial pattern: What is the state of the art? Ecosystems, 1, 143–156.CrossRefGoogle Scholar
  9. Jenerette, G. D., & Potere, D. (2010). Global analysis and simulation of land-use change associated with urbanization. Landscape Ecology, 25(5), 657–670.CrossRefGoogle Scholar
  10. Khammam Master Plan report. (2009) Govt. of Andhra Pradesh, India.Google Scholar
  11. Herold, M. (2003). The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 86, 286–302.CrossRefGoogle Scholar
  12. Huang, S. L., Wang, S. H., & Budd, W. W. (2009). Sprawl in Taipei′s peri-urban zone: Responses to spatial planning and implications for adapting global environmental change. Landscape and Urban Planning, 90, 20–32.  https://doi.org/10.1016/j.landurbplan.2008.10.010.CrossRefGoogle Scholar
  13. Kim, K. H., & Pauleit, S. (2007). Landscape character, biodiversity and land use planning: The case of Kwangju City Region, South Korea. Land Use Policy, 24(1), 264–274.CrossRefGoogle Scholar
  14. Lin, Y. P., Hong, N. M., Wu, P. J., Wu, C. F., & Verburg, P. H. (2007). Impacts of land use change scenarios on hydrology and land use patterns in the Wu–Tu watershed in Northern Taiwan. Landscape and Urban Planning, 80(1–2), 111–126.CrossRefGoogle Scholar
  15. McGarigal, K., Cushman, S. A., Neel, M. C., & Ene, E. (2002). FRAGSTATS v3: Spatial pattern analysis program for categorical maps. http://www.umass.edu/landeco/research/fragstats/fragstats.html.
  16. NRSA. (2006). Mapping using multi-temporal satellite data. Manual of National Land Use Land Cover, pp. 1–125. Hyderabad: NRSA.Google Scholar
  17. Planning Commission Approach to the 12 th Plan. (2012) http://www.slideshare.net/PlanComIndia/Urbanization-in-india-12th-plan-2012-2017.
  18. Poyil, R. P., & Misra, A. K. (2015). Urban agglomeration impact analysis using remote sensing and GIS techniques in Malegaon city, India. International Journal of Sustainable Built Environment, 4(1), 136–144.  https://doi.org/10.1016/j.ijsbe.2015.02.006.CrossRefGoogle Scholar
  19. Ramachandra, T. V., Aithal, B. H., & Sanna, D. D. (2012). Insights to urban dynamics through landscape spatial pattern analysis. International Journal of Applied Earth Observation and Geoinformation, 18(1), 329–343.  https://doi.org/10.1016/j.jag.2012.03.005.Google Scholar
  20. Report of the Steering Committee on Urbanization, 12FYP (2012–2017), Govt. of India, New Delhi, pp. 1–230.Google Scholar
  21. Rossi, J. P., & van Halder, I. (2010). Towards indicators of butterfly biodiversity based on a multi-scale landscape description. Ecological Indicators, 10, 452–458.CrossRefGoogle Scholar
  22. Turner, M. G. (1989). Landscape ecology: The effect of pattern on process. Annual Review of Ecology and Systematics, 20, 171–197.CrossRefGoogle Scholar
  23. Turner, M. G., Gardner, R. H., & O’Neill, R. V. (2001). Landscape ecology in theory and practice: Pattern and process. Berlin: Springer.Google Scholar
  24. Venugopala, R. K., Ramesh, B., Bhavani, S. V. L., & Kamini, J. (2010). Urban and regional planning. Remote sensing application (pp. 109–132). Hyderabad: NRSC.Google Scholar
  25. Yu, X. J., & Ng, C. N. (2007). Landscape and urban planning. Spatio and temporal dynamics of urban sprawl along two urban–rural transects: a case study of Guamgzhou. China. Landscape and Urban Planning, 79, 96–109.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Centre for Spatial Information TechnologyJawaharlal Nehru Technological UniversityHyderabadIndia
  2. 2.GIS LabCentral Research Institute for Dryland AgricultureHyderabadIndia

Personalised recommendations