This study investigated the temporal and spatial changes of land surface temperature (LST) over Calabar Metropolis, Nigeria (2002 to 2016). The LST over Calabar metropolis was studied from the analysis of Landsat imageries of the investigated years (2002, 2006, 2008, 2010, 2012, 2014 and 2016). The results of the LST imagery were classified using standard deviation. GIS was further applied to extract the coverage ratio of each land use in the context of Land surface temperature (LST) pixels and results were presented in degree Celsius. The result of this study revealed a great variation in the mean LST for the selected period. The highest mean LST of 25.38 °C was observed in 2016, followed by 2002 with mean LST of 25.32 °C whereas, the least LST was observed in 2010. This study has shown that, the changing land use pattern of the area is capable of affecting certain characteristics of the environment such as surface temperature. The study recommends that effort should be made by the government to increase urban vegetation in order to reduce potential future increased in LST.
Land surface temperature (LST) which is controlled by the surface energy balance, atmospheric state and thermal properties of the surface/subsurface rocks is one of the important parameters in several environmental models [
With the advent of thermal remote sensing technology, observation of LST has become possible using Satellite and Aircraft platforms [
Calabar Metropolis, the study area, is the capital of Cross River State, Nigeria, located at the Southern part of the State. It encompasses of Calabar Municipality and Calabar South Local Government Areas and lies between latitudes 4˚50'N and 5˚10'N and longitudes 8˚17'E and 8˚20'E; bounded to the north by Odukpani Local Government Area (LGA) and to the East by Akpabuyo LGA. Calabar Metropolis is sandwiched between the Great Kwa River to the East and the Calabar River to the West. The present of urban area is on the eastern bank of the Calabar River, its growth of the southern part is hindered by the mangrove swamps. It covers an estimated land area of about 274.593 km2 (
Calabar falls within tropical equatorial (Af) climate of high temperature, high relative humidity and abundant annual rainfall [
Landsat cloud-free imageries were acquired from the NASA web site which
comprised of the Thematic Mapper (TM), Enhance Thematic Mapper plus (ETM+) image and the operational land Imager (OLI) to determine LST within Calabar Metropolis between “2002 and 2016”. The imageries downloaded covered a period of 15 years at an interval of 2 years. The software employed for desktop analysis was ArcGIS. Identifying the study area was the first step of this research which was achieved with the use of an administrative map of Calabar, showing Calabar Municipality and Calabar South LGA.
Land surface temperature was estimated using various procedures which range from radiometric calibration, conversion of DN to radiance, correction for atmospheric absorption, re-emission and surface emissivity which has been used in [
Conversion of Digital Numbers (DN) of the bands to Spectral Radiance
L λ = ⌊ L MAX − L MIN Q C a l m a x − Q C A L M I N ⌋ × ( D N − 1 ) + L MIN (1)
where,
LMAX = the spectral radiance that is scaled to QCALMAX in W/(m2 * sr * μm) LMIN = the spectral radiance that is scaled to QCALMIN in W/(m2 * sr * μm) QCALMAX = the maximum quantized calibrated pixel value (corresponding to LMAX) in DN = 255 QCALMIN = the minimum quantized calibrated pixel value (corresponding to LMIN) in DN = 1.
Conversion from Spectral Radiance to At-Satellite Brightness Temperature [
T = K 2 I n ( K 1 L λ + 1 ) − 273.15 (2)
where, T = At-satellite brightness temperature, Ll = Spectral radiance (gotten from equations - and -), K1 = Band specific thermal conversion constant from the metadata, x is the thermal band number), K2 = Band specific thermal conversion constant from the metadata, −273.15 = Constant for conversion from Kelvin to Degrees Celsius as shown in [
Correcting for Land Surface Emissivity (LSE) [
The temperature values obtained using Equation (2) are reference to a blackbody. Therefore, corrections for spectral emissivity (ε) became necessary according to the nature of land cover (Equation (3))
e = 0.004 P V + 0.986 (3)
where, e = Land Surface Emissivity, 0.004 & 0.986 = Constants for emissivity estimation, PV = Proportion of vegetation [
P V ( N D V I − N D V I min N D V I max − N D V I min ) (4)
where, NDVI = Normalized Differential Vegetation Index as computed with Equation (1) for each of the years, NDVImin = Minimum value of NDVI for that year, NDVImax = Maximum value of NDVI for that year [
Estimation of the Land Surface Temperature [
L S T = B T 1 + W × B T P × I n ( ∑ ) (5)
where, LST= Land Surface Temperature, BT = At-satellite brightness temperature, W = Wavelength of emitted radiance (µm) [
P = h × c s ( 1.438 × 10 − 2 m K ) = 14380 (6)
h = Planck’s constant ( 6.626 × 10 − 34 Js ), S = Boltzmann constant ( 1.38 × 10 − 23 J / K ), C = Velocity of light ( 2.998 × 10 8 m / s ), e = LSE.
Based on the LST retrieval algorithm mentioned earlier, seven LST maps at an interval of two years (2002 to 2016) were generated to measure the magnitude and to quantify LST spatially explicit over the whole study area. In order to display the LST map clearly, the density slice function of ArcGIS was used to distinguish the LST zones by different colors. The LST over Calabar metropolis was studied from the analysis of Landsat images of the investigated years (2002, 2006, 2008, 2010, 2012, 2014 and 2016). The results of the LST imagery were classified using standard deviation. LST for the different years was also extracted and presented in degree Celsius (
The result of the LST maps revealed a great variation in the surface radiant temperature for the selected periods (
2002 | 2006 | 2008 | 2010 | 2012 | 2014 | 2016 | |
---|---|---|---|---|---|---|---|
Class_name | LST (˚C) | LST (˚C) | LST (˚C) | LST (˚C) | LST (˚C) | LST (˚C) | LST (˚C) |
Waterbody | 20.71 | 18.74 | 18.92 | 14.19 | 21.12 | 17.64 | 22.26 |
Sparse Vegetation | 24.27 | 22.68 | 23.23 | 17.71 | 24.16 | 21.76 | 24.80 |
Dense Vegetation | 22.40 | 20.63 | 21.13 | 15.70 | 22.64 | 19.70 | 23.72 |
Built-Up | 31.40 | 30.13 | 32.14 | 24.77 | 30.25 | 30.00 | 29.13 |
Barelands | 27.84 | 26.60 | 27.74 | 21.24 | 27.20 | 25.88 | 26.97 |
Mean | 25.32 | 23.76 | 24.63 | 18.72 | 25.07 | 23 | 25.38 |
Furthermore, the result of the LST map of 2012 reveals the highest mean radiant temperature in 2012 to be 30.25˚C, followed by 21.12˚C with a mean of 25.07˚C, higher than that of 2010. In 2014, radiant temperature falls within the range of 17.64˚C to 30.00˚C with a mean of 23˚C (
From the findings of this study, it is evident that, the highest mean radiant of 25.38˚C was observed in 2016, followed by 2002 with mean LST of 25.32˚C, while the least radiant temperature was observed in 2010 (
The findings of this study also demonstrated that, out of the five land use classes identified in the study area (water body, sparse vegetation, dense vegetation, built up area and bare land) [
This study has demonstrated that, Land Surface Temperature (LST) values have grown from the 2002 extent to the 2016 size and their spatial extent is getting larger as urbanization intensifies. The result revealed that the built-up area was the land use category that was significantly linked to high mean and high LST. The study also revealed that the lowest mean LST corresponded to areas covered by water bodies, followed by areas over woodland. The removal of the vegetal cover exposes the land surface to insolation expressed in reflectance that signifies the heating or cool surface systems. The sequence indicates that abundant water is helpful in buffering urban heat islands (UHIS). Vegetation covers should be protected to reduce potential future UHIS around the urban fringe transits through a peri-urban heating system.
The authors declare no conflicts of interest regarding the publication of this paper.
Awuh, M.E., Officha, M.C., Okolie, A.O. and Enete, I.C. (2018) A Remote Sensing Analysis of the Temporal and Spatial Changes of Land Surface Temperature in Calabar Metropolis, Nigeria. Journal of Geographic Information System, 10, 562-572. https://doi.org/10.4236/jgis.2018.105030