A Remote Sensing Analysis of the Temporal and Spatial Changes of Land Surface Temperature in Calabar Metropolis, Nigeria

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.

balance between the Earth and the atmosphere. Studies on LST are important to environmental studies and management of the Earth's resources due to its ability to determine the effective radiating temperature of the Earth's surface. Also, LST is a major factor of determining the partition of the available energy into sensible and latent heat flux. For example, the rate of change of LST is sensitive to the characteristics of the land surface such as soil moisture, land use and vegetation [3].
With the advent of thermal remote sensing technology, observation of LST has become possible using Satellite and Aircraft platforms [4], which has provided the new direction for the observations of thermal reflectance and the study of their effects through the combination of thermal remote sensing and urban micrometeorology [5]. Various satellites, and methods such as Landsat TM Bands are available that can be used to examine the LST [6] [7] [8] [9]. Landsat TM Bands are the data which is most widely used for these studies [6] [7] [8] [9].
Previous studies have demonstrated that the LST product retrieved from thermal infrared (TIR) sensors can be used to monitor the distribution of temperature over an area [5] [10] [11] [12] [13] [14]. Several researchers have estimated air temperatures using Landsat TM Imageries [7] [15]- [20]. This paper assesses the temporal and spatial changes of LST over Calabar Metropolis, Nigeria.

Study Area
Calabar Metropolis, the study area, is the capital of Cross River State, Nigeria, Calabar falls within tropical equatorial (Af) climate of high temperature, high relative humidity and abundant annual rainfall [21]. The annual rainfall is 2750 mm and the mean annual average temperature is 26.1˚C. The study area has witnessed a tremendous increase in the population of 10,000 estimated at the pre-colonial, to 99,352 in 1993; 328,876, in 1991. The last census in 2006 put the population to 371,022 [22]. The population growth of Calabar has been followed by the expansion of its physical boundaries. This increase in the physical boundaries implies a corresponding loss of vegetation and land in the area thereby a direct impact on the micro-climate [22].

Image and Pre-Processing
Landsat cloud-free imageries were acquired from the NASA web site which M. E. Awuh et al.

Retrieving of Land Surface Temperature
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 [23] where, L MAX = the spectral radiance that is scaled to Q CALMAX in W/(m 2 * sr * μm) L MIN = the spectral radiance that is scaled to Q CALMIN in W/(m 2 * sr * μm) Q CALMAX = the maximum quantized calibrated pixel value (corresponding to L MAX ) in DN = 255 Q CALMIN = the minimum quantized calibrated pixel value (corresponding to L MIN ) in DN = 1.
Conversion from Spectral Radiance to At-Satellite Brightness Temperature [23] where, T = At-satellite brightness temperature, L l = Spectral radiance (gotten from equations -and -), K 1 = Band specific thermal conversion constant from the metadata, x is the thermal band number), K 2 = Band specific thermal conversion constant from the metadata, −273.15 = Constant for conversion from Kelvin to Degrees Celsius as shown in [23].
Correcting for Land Surface Emissivity (LSE) [23] 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)) 0.004 0.986 where, e = Land Surface Emissivity, 0.004 & 0.986 = Constants for emissivity estimation, P V = Proportion of vegetation [23] given by the equation where, NDVI = Normalized Differential Vegetation Index as computed with Equation (1) for each of the years, NDVI min = Minimum value of NDVI for that year, NDVI max = Maximum value of NDVI for that year [23].

Land Surface Temperature (LST) Analysis
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 ( Figure 2 and Table 1). The result of the LST maps revealed a great variation in the surface radiant temperature for the selected periods ( Figure 2 and Table 1). Comparing radiant temperature for the selected periods we observed that, in 2002, the radiant temperature is confined within the range of 14.

Conclusion
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 should be protected to reduce potential future UHIS around the urban fringe transits through a peri-urban heating system.

Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this paper.