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Analysing Spatio-temporal change in LST over 11 Smart Cities of Uttar Pradesh, India
  • Ravi Verma,
  • Pradeep Kumar Garg
Ravi Verma
Department of Civil Engineering,

Corresponding Author:rverma2@ce.iitr.ac.in

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Pradeep Kumar Garg
Department of Civil Engineering
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Multiplicity of open source remote sensing date platforms help in bringing various opportunities. Spatio-temporal analysis ofa region can help in analysing changes in regional climate over different constituent land use/land cover (LU/LC). This studyderives a pattern of Land Surface Temperature (LST) over a period of 10 years in 11 smart cities of Uttar Pradesh using opensource data and software programs only. Smart cities namely Agra, Aligarh, Bareilly, Jhansi, Kanpur, Lucknow, Moradabad,Prayagraj, Rampur, Saharanpur and Varanasi are studied for LST in year 2010, 2015 and 2019 by using data from BHUVAN,NRSC and Copernicus Global Land Service: Land Cover (CGLS: LC-100) products. Boundary of the smart cities aredigitized form maps of various local authorities. Land use maps are obtained as Annual Landuse Land Cover 250k scaleproducts for year 2010 & 2015 from BHUVAN, NRSC but CGLS: LC-100 products are of resolution 100 m for year 2019.Both the Land use products are having 12 classes in region of smart cities which are reclassified into 5 LU classes of Built-up, Vegetation, Crop land, Barren land and Water. USGS Earth Explore is used to generate LST for year 2010 throughLandsat-5 ETM images by At-Surface Brightness Temperature & for year 2015 and 2019 through Landsat-8 TIRS bandimages by Radiative Transfer equation. Analysis of LST over years and LU classes show that smart cities of Aligarh andJhansi are dominantly warm over other smart cities of Uttar Pradesh. Capital city of Lucknow and Moradabad smart city arerelatively cooler than other smart cities. Rampur and Jhansi are having the lowest and highest standard deviation in LSTrespectively. Difference in LST over smart cities can be in range of 10-15 °C. Barren Land in these smart cities is found to behotter than Built-up land use class and vegetation is having lowest LST in all 11 smart cities. Range between LST values indifferent years over different LU classes vary between 28-35 °C. In Year 2019 LST statistics seem to be cooled down afteryear 2015 being worst in terms of LST range, maximum value and standard deviation of 6.12 °C. Percentage of vegetationhelping in reducing LST is surely a motivation to apply concept of Urban Green Space (UGS) in these 11 smart cities.