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Smart city article details
| Title | Insights To 11 Smart Cities Of Uttar Pradesh, India Through Spatial Pattern Analysis Of Land Use/ Land Cover |
|---|---|
| ID_Doc | 31772 |
| Authors | Verma R.; Zawadzka J.; Garg P.K. |
| Year | 2022 |
| Published | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 4/W3-2022 |
| DOI | http://dx.doi.org/10.5194/isprs-annals-X-4-W3-2022-253-2022 |
| Abstract | Urban planning of a smart city needs to be done in conjunction to Urban Green Spaces (UGS). Landscape Metrics are one of the efficient ways to analyze the patterns of Land use/Land cover (LU/LC) in a study area. Spatio-temporal change in the urban dynamics of the 11 smart cities of Uttar Pradesh state of India namely "Aligarh", "Jhansi", "Kanpur", "Lucknow", "Moradabad", "Prayagraj", "Rampur", "Saharanpur"and "Varanasi"are studied using Landscape Metrics with the help of publically available classified data such as, for years 1985, 1995 and 2005, Decadal Land use data of India and for year 2015, Copernicus Global Land service Dynamic Land Cover layers (CGLS-LC100 products). Landscape Shape Index (LSI), Largest Patch Index (LPI), Mean Euclidean Nearest Neighbor Distance (ENN_MN) and Aggregation Index (AI) are 4 general metrics used to map LU/LC patterns. Results indicate high positive relationship between LSI and AI values but negligible relationship between LPI and ENN_MN values of built-up and vegetation patches in study area. LPI and LSI show increase in values over the years with LSI showing more steep change in duration, but values of ENN_MN and AI show gradual decreasing trend over the years. Among 11 smart cities, only "Kanpur", "Agra"and "Moradabad"are most similar in values at higher, average and lower side of metrics with "Lucknow"showing highest complexity for both classes. In general, a heterogenous growth of patches appear in the study area with "Rampur"being the most consistent in metrics while "Jhansi"and "Saharanpur"being most inconsistent. © Author(s) 2022. |
| Author Keywords | Landscape Metrics; LU/LC; Open-source Data; Smart Cities; Spatio-temporal |
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