Smart City Gnosys
Smart city article details
| Title | A Study Of Urban-Landscape Characteristics Of Bhopal City (India) In A Geo-Spatial Environment |
|---|---|
| ID_Doc | 5000 |
| Authors | Tiwari A.; Mishra P.K. |
| Year | 2019 |
| Published | Urban Book Series |
| DOI | http://dx.doi.org/10.1007/978-3-319-94932-1_15 |
| Abstract | Bhopal was shortlisted as an aspirant in the smart-cities challenge by the Ministry of Urban Development, Government of India. The Indian government’s Smart Cities model is an innovative sustainable urban-development solution that uses information and communication technologies and other means to improve quality of life, efficiency of urban operation and services, and competitiveness while ensuring that it meets the needs of present and future generations with respect to economic, social, and environmental aspects. To provide a set of strategic and operational research methodologies and systems solutions that cater to the needs of the Bhopal developing sectors, current trends of urbanization with their impact on the health of the city must be studied. This chapter aims to quantify the spatio-temporal patterns of urban expansion and their relationships with land-surface temperature (LST) as a prime indicator of city health in Bhopal. The process was studied using LST and the urban land–cover pattern derived from Landsat TM/ETM satellite data for two decades (1995–2015). In this study, the four major land-cover classes mapped include (i) built-up areas, (ii) water, (iii) vegetation, and (iv) others. Three spectral indices were used to characterize three foremost urban land-use classes: (1) a normalized difference built-up index (NDBI) to characterize built-up areas; (2) a modified normalized difference water index (MNDWI) to signify open water; and (3) a soil-adjusted vegetation index (SAVI) to symbolize green vegetation. Land-use and land-cover (LULC) maps prepared using the NDBI, MNDWI, and SAVI had, respectively, an overall accuracy of 90, 88, and 86% and kappa coefficient of 0.8726, 0.8455, and 0.8212 for 1989, 2006, and 2010. These changes, when attributed in increasing surface temperature in the study region, show a positive correlation between LST and NDBI, a negative correlation between LST and SAVI, and a perfectly negative correlation between NDBI and MNDWI. © 2019, Springer Nature Switzerland AG. |
| Author Keywords | Land-surface temperature; Land-use/cover change; MNDWI; Modified normalized difference water index; NDBI; Normalized difference built-up index; SAVI; Soil-adjusted vegetation index |
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