Critical analysis of spatial-temporal morphological characteristic of urban landscape

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.

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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):293–300

    Google 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 177–208

    Google Scholar 

  4. Borana SL, Yadav SK (2017) Urban landscape assessment using spatial metrics: a temporal analysis of Jodhpur City. Int J 5(10)

  5. Cabral P, Augusto G, Tewolde M, Araya Y (2013) Entropy in urban systems. Entropy 15(12):5223–5236

    Article  Google Scholar 

  6. Cardille JA, Turner MG (2017) Understanding landscape metrics. In: Learning landscape ecology. Springer, New York, NY, pp 45–63

    Google 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:1–21

  8. Census of India. (2011). Provisional population totals. Registrar General & Census Commissioner, India, New Delhi, Ministry of Home Affairs, Government of India

  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):317–345

    Article  Google 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):187–198

    Article  Google 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, Hungary

    Google 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):303–318

    Article  Google 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):736–753

    Article  Google 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:1–12

  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):4107–4129

    Article  Google 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 98:156–165

    Article  Google Scholar 

  17. Government of India. (2001). Census of India

  18. Hagen-Zanker A (2006) Map comparison methods that simultaneously address overlap and structure. J Geogr Syst 8(2):165–185

    Article  Google 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)

  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):369–399

    Article  Google 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):991–1001

    Article  Google 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):1443–1458

    Article  Google Scholar 

  23. Jensen JR, Cowen DC (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm Eng Remote Sens 65:611–622

    Google 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):861–879

    Article  Google Scholar 

  25. Khatami R, Mountrakis G, Stehman SV (2017) Mapping per-pixel predicted accuracy of classified remote sensing images. Remote Sens Environ 191:156–167

    Article  Google 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 3–10

    Google 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):862–878

    Article  Google 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):365

    Article  Google Scholar 

  29. Liu M, Hu YM, Li CL (2017a) Landscape metrics for three-dimensional urban building pattern recognition. Appl Geogr 87:66–72

    Article  Google Scholar 

  30. Liu T, Yang X (2015) Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Appl Geogr 56:42–54

    Article  Google 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):327–339

  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 130:277–293

    Article  Google Scholar 

  33. McDonald M (2010) Midwest Mapping Project. George Mason University. In: Department of Public and International Affairs

    Google 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):88

    Article  Google 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):e0196940

    Article  Google 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):163

    Article  Google 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):223–230

  39. Polsby DD, Popper RD (1991) The third criterion: compactness as a procedural safeguard against partisan gerrymandering. Yale Law & Policy Review 9(2):301–353

    Google 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):1

  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):871–888

    Article  Google 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):488–502

    Article  Google 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):113–127

    Google Scholar 

  45. Sudhira HS, Ramachandra TV, Jagadish KS (2004) Urban sprawl: metrics, dynamics and modeling using GIS. Int J Appl Earth Obs Geoinf 5:29–39

    Article  Google 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)

  47. Yeh AGO, Li X (2001) Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogramm Eng Remote Sens 67:83–90

    Google Scholar 

  48. Zhou W, Pickett ST, Cadenasso ML (2017) Shifting concepts of urban spatial heterogeneity and their implications for sustainability. Landsc Ecol 32(1):15–30

    Article  Google Scholar 

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Correspondence to Anugya Shukla.

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Shukla, A., Jain, K. Critical analysis of spatial-temporal morphological characteristic of urban landscape. Arab J Geosci 12, 112 (2019). https://doi.org/10.1007/s12517-019-4270-y

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Keywords

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