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Abstract
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.
Highlights
Urbanisation may be called event, which upon unfolding creates more diversification in land use of city’s landscape (Liu et al 2016)
For years 1985, 1995 and 2005, Decadal Land use data of India is used as classified maps for study area (Roy et al 2016) and Copernicus Global Land service Dynamic Land Cover classified map at 100m resolution known as CGLS-LC100 product, (Buchhorn et al 2020) has been used for year 2015
This study successfully shows the use of publically available data for analysing relation between urbanisation and its different variables and properties over a change duration of study
Summary
Urbanisation may be called event, which upon unfolding creates more diversification in land use of city’s landscape (Liu et al 2016) These unchecked forms of urbanisation go on to deteriorate regional climate and health of environment (Wu et al 2016). Characterisation of long-term spatio-temporal urbanisation and understanding of its environmental impacts is a necessary part of urban planning (Li & Gong, 2016). These impacts can be continuously kept on check by available analysis ready data such as Earth Observation (EO) data cubes, and Google Earth Engine (GEE), which has capability of big data analytics in cloud computing environment (Mugiraneza et al 2020). Available datasets can be used by city planners to mitigate any regional phenomenon destabilizing the local environment (Acosta et al 2021)
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