TITLE:
Predicting of Land Surface Temperature Distribution in Freetown City, Sierra Leone by Using Polynomial Curve Fitting Model
AUTHORS:
Elhadi K. Mustafa, Guoxiang Liu, Abubakr Hassan, Mohamed A. Damos, Musa Tarawally
KEYWORDS:
Global Warming, Land Surface Temperature, Polynomial Curve Fitting, Land Cover Indices
JOURNAL NAME:
Journal of Geographic Information System,
Vol.12 No.5,
October
30,
2020
ABSTRACT: Global warming has attracted much concern about the worldwide organization, civil
society groups, researchers, and so forth because the worldwide surface
temperature has been expanding. This investigation intends to assess and
compare the ability of a combination of land cover indices to predict the
future distribution of land surface temperatures in Freetown using the
Polynomial model analysis. Landsat satellite images of 1988, 1998, 2000, 2010, and
2018 of the Freetown Metropolitan zone were utilized for analysis. The
investigation had adopted two land covers indices, Modification of normalized
difference water index and Urban Index (UI) (e.g., MNDWI and UI) and applied a
multi regression equation for forecasting the future LST. The stimulation
results propose that the development will be accompanied by surface temperature
increases, especially in Freetown’s western urban area. The temperature
prevailing in the west of the metropolitan area may increase in the city
somewhere in the range from 1988 to 2018. Additionally, the results of the LST prediction show that
the model is perfect. Our discoveries can be represented as a helpful device
for policymakers and community awareness by giving a scientific basis for
sustainable urban planning and management.