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Critical analysis of spatial-temporal morphological characteristic of urban landscape

  • Anugya ShuklaEmail author
  • Kamal Jain
Original Paper
  • 28 Downloads

Abstract

Remote sensing and Geographical Information System (GIS) data have been used widely to analyze and study the patterns of urban expansions. Urban expansions are intricate dynamic process associated with landscape transformation and its driving factors. Previous studies mainly focused only on identifying urban change; therefore, in this study, we have developed a spatial-temporal morphological model to identify the pattern of urbanization and driving factors contributing the growth pattern. The primary objective of this study is to identify and analyze the urban sprawl of Lucknow city, India, as a pattern and process. Quantification of urban landscape is performed using remotely sensed temporal satellite images of 1990, 1999, 2009, and 2016 over a period of 26 years. An interlink between spatial metrics, gradient analysis, and density index has been developed to analyze the directional growth of the city. Gradient modeling is performed using moving window analysis on a single grid for quantification of the landscape structure. Multi Ring Buffer (MRB) approach has been deployed to measure the extent and trends of urbanization. The quantification of MRB is performed using Shannon’s entropy estimations. The analysis of spatial data is then carried out by splitting the study area into five circular zones of 2 km each in increasing order of radius. The higher value of Shannon’s entropy index shows a highly coalesced urban center with dispersed growth towards the outskirts. Urban gradient analysis is performed to model the landscape parameters and urban growth morphology in 16 different directions. Total 257 sample points from the city center at the interval of 500 m are overlaid on temporal dataset up to 8 km in 16 different directions. To compute the compactness of urban sprawl for the present scenario, density index is evaluated. The outcome from the study indicates an integrated approach for modeling the urban morphology which illustrates the extent of influencing drivers of urbanization in various directions.

Keywords

Multi Ring Buffer (MRB) Urban morphology Shannon’s entropy Gradient analysis Landscape Spatial metrics 

References

  1. Al Mashagbah AF (2016) The use of GIS, remote sensing and Shannon’s entropy statistical techniques to analyze and monitor the spatial and temporal patterns of urbanization and sprawl in Zarqa City, Jordan. J Geogr Inf Syst 8(02):293–300Google Scholar
  2. Angel S, Parent J, Civco D (2007) Urban sprawl metris: an analysis of global urban ex-pansion using GIS ASPRS 2007 Annual Conference Tampa, Florida May 7–11, 2007 URL: http://clear.uconn.edu/publications/research/tech papers/Angel et al ASPRS2007.pdf
  3. Antrop M, Van Eetvelde V (2017) Analysing landscape patterns. In: Landscape perspectives. Springer, Dordrecht, pp 177–208Google Scholar
  4. Borana SL, Yadav SK (2017) Urban landscape assessment using spatial metrics: a temporal analysis of Jodhpur City. Int J 5(10)Google Scholar
  5. Cabral P, Augusto G, Tewolde M, Araya Y (2013) Entropy in urban systems. Entropy 15(12):5223–5236Google Scholar
  6. Cardille JA, Turner MG (2017) Understanding landscape metrics. In: Learning landscape ecology. Springer, New York, NY, pp 45–63Google Scholar
  7. Cegielska K, Kukulska-Kozieł A, Salata T, Piotrowski P, Szylar M (2018) Shannon entropy as a peri-urban landscape metric: concentration of anthropogenic land cover element. J Spat Sci:1–21Google Scholar
  8. Census of India. (2011). Provisional population totals. Registrar General & Census Commissioner, India, New Delhi, Ministry of Home Affairs, Government of IndiaGoogle Scholar
  9. Debbage N, Bereitschaft B, Shepherd JM (2017) Quantifying the spatiotemporal trends of urban sprawl among large US metropolitan areas via spatial metrics. Applied Spatial Analysis and Policy 10(3):317–345Google Scholar
  10. Deng JS, Wang K, Hong Y, Qi JG (2009) Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landsc Urban Plan 92(3):187–198Google Scholar
  11. Dutta V (2012) War on the dream–how land use dynamics and Peri-urban growth characteristics of a Sprawling City devour the master plan and urban suitability? In: 13thAnnual global development conference, Budapest, HungaryGoogle Scholar
  12. Effat HA, El Shobaky MA (2015) Modeling and mapping of urban sprawl pattern in Cairo using multi-temporal landsat images, and Shannon’s entropy. Advances in Remote Sensing 4(04):303–318Google Scholar
  13. Fan C, Li W, Wolf LJ, Myint SW (2015) A spatiotemporal compactness pattern analysis of congressional districts to assess partisan gerrymandering: a case study with California and North Carolina. Ann Assoc Am Geogr 105(4):736–753Google Scholar
  14. Felt C, Fragkias M, Larson D, Liao H, Lohse KA, Lybecker D (2018) A comparative study of urban fragmentation patterns in small and mid-sized cities of Idaho. Urban Ecosystems:1–12Google Scholar
  15. Fenta AA, Yasuda H, Haregeweyn N, Belay AS, Hadush Z, Gebremedhin MA, Mekonnen G (2017) The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia. Int J Remote Sens 38(14):4107–4129Google Scholar
  16. Gupta S, Islam S, Hasan MM (2018) Analysis of impervious land-cover expansion using remote sensing and GIS: a case study of Sylhet sadar upazila. Appl Geogr 98:156–165Google Scholar
  17. Government of India. (2001). Census of IndiaGoogle Scholar
  18. Hagen-Zanker A (2006) Map comparison methods that simultaneously address overlap and structure. J Geogr Syst 8(2):165–185Google Scholar
  19. Herold, M., Hemphill, J., Dietzel, C., & Clarke, K. C. (2005b, March). Remote sensing derived mapping to support urban growth theory. In 3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas (URBAN 2005) and 5th International Symposium Remote Sensing of Urban Areas (URS 2005)Google Scholar
  20. Herold M, Couclelis H, Clarke KC (2005a) The role of spatial metrics in the analysis and modeling of urban land use change. Comput Environ Urban Syst 29(4):369–399Google Scholar
  21. Herold M, Liu X, Clarke KC (2003) Spatial metrics and image texture for mapping urban land use. Photogramm Eng Remote Sens 69(9):991–1001Google Scholar
  22. Herold M, Scepan J, Clarke KC (2002) The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ Plan A 34(8):1443–1458Google Scholar
  23. Jensen JR, Cowen DC (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm Eng Remote Sens 65:611–622Google Scholar
  24. Ji W, Ma J, Twibell RW, Underhill K (2006) Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics. Comput Environ Urban Syst 30(6):861–879Google Scholar
  25. Khatami R, Mountrakis G, Stehman SV (2017) Mapping per-pixel predicted accuracy of classified remote sensing images. Remote Sens Environ 191:156–167Google Scholar
  26. Kamusoko C (2017) Importance of remote sensing and land change modeling for urbanization studies. In: Urban development in Asia and Africa. Springer, Singapore, pp 3–10Google Scholar
  27. Lamine S, Petropoulos GP, Singh SK, Szabó S, Bachari NEI, Srivastava PK, Suman S (2018) Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®. Geocarto International 33(8):862–878Google Scholar
  28. Liu H, Huang X, Wen D, Li J (2017b) The use of landscape metrics and transfer learning to explore urban villages in China. Remote Sens 9(4):365Google Scholar
  29. Liu M, Hu YM, Li CL (2017a) Landscape metrics for three-dimensional urban building pattern recognition. Appl Geogr 87:66–72Google Scholar
  30. Liu T, Yang X (2015) Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Appl Geogr 56:42–54Google Scholar
  31. Luck M, Wu J (2002) A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landsc Ecol 17(4):327–339Google Scholar
  32. Ma L, Li M, Ma X, Cheng L, Du P, Liu Y (2017) A review of supervised object-based land-cover image classification. ISPRS J Photogramm Remote Sens 130:277–293Google Scholar
  33. McDonald M (2010) Midwest Mapping Project. George Mason University. In: Department of Public and International AffairsGoogle Scholar
  34. McGarigal K, Cushman SA, & Ene E (2012) FRAGSTATS v4: Spatial pattern analysis program for categorical and continuous maps. Amherst: University of Massachusetts. http://www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed 14 Aug 2012
  35. Momeni R, Aplin P, Boyd DS (2016) Mapping complex urban land cover from spaceborne imagery: the influence of spatial resolution, spectral band set and classification approach. Remote Sens 8(2):88Google Scholar
  36. Nong DH, Lepczyk CA, Miura T, Fox JM (2018) Quantifying urban growth patterns in Hanoi using landscape expansion modes and time series spatial metrics. PLoS One 13(5):e0196940Google Scholar
  37. Padmanaban R, Bhowmik AK, Cabral P, Zamyatin A, Almegdadi O, Wang S (2017) Modeling urban sprawl using remotely sensed data: a case study of Chennai city, Tamilnadu. Entropy 19(4):163Google Scholar
  38. Pham HM, Yamaguchi Y, Bui TQ (2011) A case study on the relation between city planning and urban growth using remote sensing and spatial metrics. Landsc Urban Plan 100(3):223–230Google Scholar
  39. Polsby DD, Popper RD (1991) The third criterion: compactness as a procedural safeguard against partisan gerrymandering. Yale Law & Policy Review 9(2):301–353Google Scholar
  40. Rahimi A (2016) A methodological approach to urban land-use change modeling using infill development pattern—a case study in Tabriz, Iran. Ecol Process 5(1):1Google Scholar
  41. Seto KC, Fragkias M (2005) Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landsc Ecol 20(7):871–888Google Scholar
  42. Shen G, Ibrahim Abdoul N, Zhu Y, Wang Z, Gong J (2017) Remote sensing of urban growth and landscape pattern changes in response to the expansion of Chongming Island in Shanghai, China. Geocarto International 32(5):488–502Google Scholar
  43. Shukla A, Jain K (2018) Modeling urban growth trajectories and spatiotemporal pattern: a case study of Lucknow City, India. J Indian Soc Remote Sensing:1–14.  https://doi.org/10.1007/s12524-018-0880-1
  44. Singh SK, Srivastava PK, Szabó S, Petropoulos GP, Gupta M, Islam T (2017) Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using earth observation data-sets. Geocarto international 32(2):113–127Google Scholar
  45. Sudhira HS, Ramachandra TV, Jagadish KS (2004) Urban sprawl: metrics, dynamics and modeling using GIS. Int J Appl Earth Obs Geoinf 5:29–39Google Scholar
  46. Taubenböck, H., Wegmann, M., Roth, A., Mehl, H., & Dech, S. (2009). Urbanization in India–Spatiotemporal analysis using remote sensing data. Computers, Environment and Urban Systems, 33(3), 17 Herold, M., Couclelis, H., & Clarke, K. C. (2005)Google Scholar
  47. Yeh AGO, Li X (2001) Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogramm Eng Remote Sens 67:83–90Google Scholar
  48. Zhou W, Pickett ST, Cadenasso ML (2017) Shifting concepts of urban spatial heterogeneity and their implications for sustainability. Landsc Ecol 32(1):15–30Google Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology RoorkeeUttarakhandIndia

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