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Articles | Volume XLVI-4/W5-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W5-2021, 557–564, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-557-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W5-2021, 557–564, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-557-2021

  23 Dec 2021

23 Dec 2021

ANALYSIS OF SPATIO-TEMPORAL URBAN DYNAMICS IN 11 SMART CITIES OF UTTAR PRADESH, INDIA

R. Verma and P. K. Garg R. Verma and P. K. Garg
  • Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarakhand, India

Keywords: Smart Cities, Spatio-temporal, Land use change, Landscape Metrics, Shannon's entropy

Abstract. Urban planning in smart cities needs to be done in a “Smart” way. One way is to analyze the urbanisation pattern by spatio-temporal change detection techniques. Classified data such as, for years 1985, 1995 and 2005 Decadal Land use data for India and for year 2015, Copernicus Global Land service Dynamic Land Cover layers (CGLS-LC100 products) are used to perform multi-temporal analysis of the 11 smart cities of Uttar Pradesh state of India namely "Agra", "Aligarh", "Bareilly", "Jhansi", "Kanpur", "Lucknow", "Moradabad", "Prayagraj", "Rampur", "Saharanpur" and "Varanasi". Dynamics of Urban expansion are studied utilizing concepts of Landscape Metrics calculated by FRAGSTATS and also Shannon’s Entropy Values (Hn) over the 11 smart cities. Largest Patch Index (LPI), Landscape Shape Index (LSI), Aggregation Index (AI) and Mean Euclidean Nearest Neighbor Distance (ENN_MN) are metrics used to characterize urbanisation. Results indicate rise in value of LSI over the years from 1985 and with sudden increase in year 2015 for Built-up patches, corroborating more complexity in shapes of Built-up patches in all 11 cities. Kanpur, showing large values of LPI indicates the sudden increase of Built-up land use class over the years. The decreasing value of ENN_MN over the years indicates less centrality for built-up pixels in urbanisation. AI is unchanged for Built-up patches for 1985–1995 but decrease in year 2015 indicates less compactness which is due to dispersion of built-up pixels. High values of Hn over the years indicating dispersion of urbanisation in all 11 smart cities except Agra, also validates results.