![Sample our Geography journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5937/TOC-Subject-Sample-2021-Geography.jpg"})
Abstract
Bare surface index (BSI) and surface vegetation index (SVI) are important spectral indices for land use planning systems. A long-term monthly analysis of BSI and SVI in an urban area is needed for better land use planning. However, a few research works were available on BSI and SVI. The present research work evaluates the mean monthly land surface temperature (LST) and the monthly LST-BSI and LST-SVI correlation in Raipur City of central India using 254 Landsat satellite data from 1988 to 2019. April (37.11 °C) and January (24.11 °C) record the highest mean LST and lowest mean LST, respectively. Karl Pearson’s coefficient of correlation is used to correlate LST with BSI and SVI. Although both the indices develop a positive correlation (moderate) with LST, BSI (0.64) has a better value of correlation coefficient than SVI (0.39). The best LST-BSI correlation is found in August (0.77) followed by September (0.75), October (0.74), and July (0.72). The best LST-SVI correlation is also observed in August (0.50), followed by July (0.49) and September (0.48). The study indicates that a dry bare surface enhances the intensity of LST. The research may be considered a good case study for land use planners.
Acknowledgments
The authors are indebted to Earth Explorer website.
Disclosure statement
The authors declare that they have no competing interests.
Availability of data and material
The used datasets are available at https://earthexplorer.usgs.gov/.