IMPACTS OF LAND USE/LAND COVER CHANGES ON SURFACE URBAN HEAT ISLANDS: A CASE STUDY OF COIMBATORE, INDIA

Urban Heat Island (UHI) is a major urban environmental issue throughout the world. UHI is a climatic phenomenon where anthropogenic modification leads to increased air temperature in urban areas when compared to that of the surrounding rural areas. Over urbanisation leads to an increase in UHI, resulting in the decrease of human health and a healthy environment. Remote sensing plays a major role in mapping the UHI as it can sense the top of the atmosphere radiances. From brightness, temperatures can be derived using Planck’s constant. In this study, UHI of Coimbatore was determined by using the single channel algorithm during winter season. Landsat data of TM, ETM+ and OLI/TIRS were used. Thus, LST helps to identify the increase in heat due to expansion urban areas. Supervised classification with maximum likelihood technique was used to classify the imageries into five landuse classes.Based on this study, the result emphasies that the land use changes was observed to be 14.55 per cent, where as vegetation reduction was 11.6 per cent.Thus, by correlating all these scenario from the year 1990 to 2015 with a five-year interval, the rapid development that took place in the Coimbatore region led to decrease in vegetation and increase in built-up land and temperature. This study reveals that there was an increase of 3.8C in land surface temperature during in the study periods. Also, the result indicates that there is a strong linearly negative correlation between land surface temperature and vegetation.


Introduction
Urbanisation has become a universal problem and the most important social and economic phenomenon which is occurring almost all around the world. One of the most crucial issues of global change in the 21 st century is the rapid urbanisation,taking place in the developing world, will continue to affect the human dimensions (Sui and Zeng, 2001). Surface Heat Island (UHI) (Mallick et al, 2013;Stathopoulou and Cartalis, 2007;Feizizadeh and Blaschke, 2012;Sobrino et al., 2013).
The magnitude of UHI is drastically affected by the changes in land use-land cover pattern, topographical location size and urban sprawl pattern, ecological context and seasonal variation of the temperature. (Singh et al., 2014).
Various main reasons for urban climate change can be industrialisation, population increase and urban growth (Hu and Jia, 2010 Zhang et al., (2011) developed a tool using C++ language for retrieving LST from Landsat TM/ ETM+ data using SC and SW approaches. Sekertekin et al., (2016) studied the spatiotemporal variation of UHI in Zonguldak city from 1986 to 2015, using the Landsat 5 TM and Landsat 8 OLI/TIRS imageries.
In this study, thermal band 10 of Landsat 8 TIRS Journal of Rural Development,Vol.37,No. (2), April-June:2018 are used with the Mono-window algorithm for estimating the LST. A new method called Improved Mono-window (IMW) algorithm for LST extraction from Landsat 8 TIRS band 10 was developed by Wang et al. (2015). From their analysis, it was found that the IMW algorithm showed less errors than the SC algorithm.
Various studies have been conducted by several researchers on the assessment of LST and vegetation correlation. They investigated the use and explored the relationship between land use type and LST magnitudes. Kayet et al. (2016) studied spatial impact of land use/land cover change on surface temperature distribution in Saranda forest, Jharkhand. The study concluded that the decrease in the vegetation areas is mainly due to the growth of rapid mining industrial areas which also significantly increased the surface temperature. Weng et al. (2004)

Study Area
The study area, Coimbatore is the second largest city in the State after Chennai and 16th largest city in India. It is located on the banks of Year 1911 1921 1931 1941 1951 1961 1971 1981 1991

Methodology
Three main steps are involved basically in this study, they are (1) Pre-processing which is atmospheric and radiometric correction (2)    Thus, the area was classified accordingly into five classes: forest, agricultural land, built-up area, water body and wasteland. The classification process accuracy is usually assessed by comparing the results of classification with mentioned data from field visits.

Estimation of LST:
The thermal infrared band (Landsat band 6 for Landsat 5 & 6 and band 10 & 11 for Landsat 8) was used to record the reflectance from the earth surface usually for the wavelength range between 10.4 and 12.5 µm. In an electromagnetic spectrum, this band is referred as the thermal band. Land surface temperature plays an important role in assessing many environmental processes. It can also provide basic primary information on the physical properties taking place on the surface of earth and climatic condition that is evolving. For example, the TM TIR data are very much helpful in observing the temperature difference between urban and nonurban areas in some States of the U.S. (Weng 2001).
In most studies, LST is generated using the image processing software followed by processing in GIS software. In this study, ArcGIS 10.4 software is used to develop an automated tool for analysis of the LST. The overall methodology used to develop the LST mapping is given in Figure 2.

(a) DN to Radiance
The DN values should be converted from Digital Number to radiance values using the equation (3)

(b) Radiance to Brightness Temperature
The Radiance value is now converted to Brightness Temperature using the equation (4) (4)

(c) Proportion of Vegetation
It is used to identify the proportion of vegetation which is used to calculate the emissivity of the data. Thus, the proportion of vegetation is calculated by the equation (5) (5)

(d) Emissivity
The emissivity were based on our land cover classification (Yuan et al., 2005). In this study emissivity values were adopted from Snyder et al. (1998). From the proportion of vegetation, the emissivity of the data could be analysed using the equation (6) (6)

(e) Land Surface Temperature
Thus, by using the Thermal band, Brightness Temperature and Emissivity, the LST can be obtained using the equation (7) (

Results and Discussion
Land cover data help in calculating the     Using the above interface ( Figure 5), the model has been created to simplify the steps involved in deriving the LST within few clicks.
This GUI model with assigned parameter value is presented in Figure 6.  Journal of Rural Development,Vol.37,No. (2), April-June:2018 Relationship between LST and land use/ cover type: The land surface temperature patterns are associated distinctly with that of thermal characteristics of land cover classes (Weng, 2001  Also, the result indicates that there is a strong and linearly negative correlation between land surface temperature and vegetation. From the LST, it is clearly understood that the temperature surrounding the urban land is more than that of its periphery. Moreover, for a clear comparison and discussion the data should be in same month for all the year, preferably same period, but this is not possible as the data available may vary for even 2 or 3 months.