Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local to global scales, 15 and it is one of the indicators of environmental quality. Evaluation of the surface temperature distribution and its relation with existing land use types are very important to investigate the urban microclimate. In the arid and semi-arid regions, understanding the role of land use changes in the formation of urban heat islands is necessary to provide urban planning to control or reduce surface temperature. Internal factors and environmental conditions of Yazd city have important role in the formation of special thermal conditions in Iran. In this paper, we used the Temperature Emissivity Separation (TES) algorithm for LST retrieving from 20 TIRS data of the Landsat Thematic Mapper (TM). The Root Mean-Square Error (RMSE) and Coefficient of Determination (R) were used for validation of retrieved LST values. The RMSE of 0.9°C and 0.87°C and R of 0.98 and 0.99 were obtained for 1998 and 2009 images, respectively. Land use types of the city of Yazd were identified and relationships between land use types, land surface temperature and NDVI index were analyzed. The Kappa coefficient and overall accuracy were calculated for accuracy assessment of the land use classification. The Kappa coefficient values are 0.96 and 0.95 and the overall accuracy values are 0.97 25 and 0.95 for 1998 and 2009 classified images, respectively. The results of this study showed that optical and thermal remote sensing methodologies can be used to research urban environmental parameters. Finally, it was found that special thermal conditions in Yazd were formed by land use changes. Increasing the area of asphalt roads, residential, commercial and industrial land use types and decreasing the area of the parks, green spaces and fallow lands types of land uses in Yazd caused a rise in surface temperature during the 11-year period. The results showed an increase of 1.45 degrees Celsius of the average surface temperature. 30

of solar radiation may be changed by anthropogenic changes. The study of surface temperature in the cities located in the arid and semi-arid areas is necessary, because high temperature leads to energy consumption for cooling buildings, which is economically very costly especially in the warm months of year.
Remote sensing instruments are key players to study and map land surface temperature (LST) at temporal and spatial scales (André et al., 2015). The LST indicator shows effects of different types of phenomena and features in the electromagnetic energy dispatch 5 (Bingwei Tian et al., 2015).Remote sensing methodology requires less time and lower cost than field methods to investigate various phenomena on the land surface (Niu et al., 2015). The advantages of using remote sensing methodology are: the repetitive and consistent coverage, high resolution and evaluation of land surface characteristics (Owen et al., 1998). Thermal infrared (TIR) data in the remote sensing can help us obtain quantitative information of surface temperature. Landsat imagery can be applied for monitoring different types of land use in arid and semi-arid regions (Baojuan et al., 2015). The Landsat TM and ETM+ sensors 10 images can be used to study relation between surface temperature and land use types using thermal quantitative indicators (Weng 2003, Streutker, 2003. Land surface temperature can be retrieved using data from NOAA-11 AVHRR channels 4 and 5 by emissivity calculation (France et al., 1994).
The using of LST values which vary according to the surface characteristics is a new method for investigating the effects of different land surface features on the surface temperature especially in urban areas (Guanhua et al., 2015). Derivation of land 15 surface temperature (LST) from medium to high spatial resolution data of remote sensing is very important to study climate change and environment (Juan et al., 2014). In the several studies the relative warmth of cities was estimated by knowing air temperature and land use changes. The LST index provides important information about climate and the surface physical characteristics. Land use changes and anthropogenic activities are affecting the environment and land surface temperature (Dehua et al., 2012;Weng and Schubring, 2004). The estimation of the LST from the radiative transfer equation, the mono-window and single-channel 20 algorithms can be used to retrieve the land surface temperature (LST) from thermal infrared data of the Thematic Mapper (TM) sensor (José et al., 2004).
The Normalized Difference Vegetation Index (NDVI) is a good indicator for identifying long term changes in the vegetation covers and their status (Baihua and Isabela, 2015). The NDVI calculation method using surface emissivity can be applied to areas with different soil and vegetation types and where the vegetation cover changes (Valor et al., 1996). Therefore, analysis of spatial 25 variability of NDVI, surface temperature and relation between these parameters is essential in the environmental studies. Combined study of NDVI, surface temperature and temporal relation these two parameters with land use changes can be used for investigating climate change and global warming (Schultz and Halpert, 1993). Vegetation cover change is the main factor which causes surface temperature changes. It should be noted that increasing surface temperature may increase vegetation cover density in the area, of course in areas where there are sufficient water resources (Weixin et al., 2011). Different types of vegetation cover have different 30 spatial responses to climate changes (Dehua et al., 2012). In the environmental studies, researchers have investigated land surface temperature using vegetation indices (Wei et al., 2015). Analysis of NDVI and LST of the different times (days, months, seasons and years) can be used to detect land use changes, which were formed because of deforestation, forest fires, mining activities, urban expansion and grassland regeneration (Sandra et al., 2015). Changes in land use and land cover can be evaluated by analysis of the vegetation cover and NDVI trends. The vegetation phenology was detected using Terra MODIS NDVI data by Gong Z. et 35 al. (2015). Land use changes, vegetation cover and soil moisture have strong effects on the land surface temperature; therefore, surface temperature can be applied to study land use changes, urbanization and desertification. Surface emissivity calculation is important to estimate surface temperature. In the several studies, laboratory measurements of the emissivity data were used for estimation of land surface temperature (Salisbury and D'Aria, 1992;Salisbury and D'Aria, 1994).
In the present study, heterogeneous surface temperature and NDVI of Yazd in the Iran were calculated using Landsat TM sensor data. Surface temperature variation over different land use types in the Yazd are investigated, and analyzed the relation between NDVI index and land surface temperature. The aims of this study are to apply Temperature Emissivity Separation algorithm (TES) for LST retrieving from Landsat TM thermal data and to analyze the NDVI index, different land use types and their roles in the surface temperature change. The main advantages of this study are TES algorithm (calculating emissivity) for retrieving LST, full statistical analysis for results validation and simultaneous analysis of NDVI, LST and land use. The main difference between this study and previous studies is to investigate the relationship between NDVI, LST and land use changes, and analysis of the effect of the land use changes on land surface temperature.

Study area
Yazd has been chosen as the study area, since there are combination of different land use categories and susceptible to dust storms.
Yazd is located in 31˚47'37''-31˚57'56'' north latitude and 54˚13'28''-54˚27'10'' east longitude in the Iran. This city has an altitude of 1230 m and covering an area of 2,491 km 2 ( Figure 1). The study area is located in the arid and semi-arid belt in the northern hemisphere. In the arid and semi-arid areas, vegetation covers are affected by high diurnal and seasonal variations of temperature, low amounts of precipitation and high evaporation (Dehghan, 2011). The predominant features of the territory are residential area, 15 waste land and bare soil. Land surface temperature in the Yazd is affected by warm, arid and semi-arid climate, low precipitation and remoteness of major water resources such as the Caspian Sea, Persian Gulf and Oman Sea.

Satellite data processing and methodology
Landsat TM sensor data of 08 th Aug, 1998 and 06 th Aug, 2009 (day time) were used in the present study. Surface emissivity 20 calculation is the first step of land surface temperature retrieving by Temperature Emissivity Separation (TES) algorithm. The emissivity per pixel was obtained directly from Landsat TM sensor data. Natural surfaces at the resolution of 30 meters are heterogeneous and they differ from each other in their emissivity. In addition, the surface emissivity is affected by surface roughness, vegetation cover and different land use types. In the present study, surface emissivity was evaluated by analysis of NDVI index and the fraction of vegetation cover per pixel. Emissivity is a quantification of the intrinsic ability of a surface in 25 converting heat energy into above surface radiation and depends on the physical properties of the surface and on observation conditions (Sobrino et. al. 2001). Surface emissivity can be extracted using NDVI values of the bare soil, fully vegetated and mixture of bare soil and vegetation (Sobrino et. al. 2004). In this study, the following equation was used to extract land surface emissivity for each pixel: Where, is the LSE, is the proportion of vegetation, is vegetation emissivity (0.99) and is soil emissivity (0.97).The term show geometric distribution effect of natural surfaces and their internal reflection.This term ( ) for our study area was not (2) NDVI index is obtained from spectral reflectance measurements in the visible (RED) and near-infrared regions (NIR) in the ArcGIS environment by: NDVI index was calculated for two different times (1998 and 2009) to investigate the status of vegetation cover of the study area.

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High values of NDVI indicate dense and healthy vegetation. This method needs elementary knowledge of emissivity and NDVI of the different features and land use types.
The at-sensor spectral radiance is the amount of energy received by the satellite sensor. Calculation of spectral radiance is the fundamental step in converting satellite image data into a physically radiometric scale. Radiometric calibration of the Landsat TM sensor involves rescaling the raw digital numbers of the satellite image to calibrated digital numbers. The pixel values of 10 unprocessed image data were converted to spectral radiance by radiometric calibration.
Spectral Radiance ( ) at the sensor's aperture in watts/(meter squared*ster*µm) is provided by following equation: Spectral Radiance is also expressed as: Where, QCAL is the quantized calibrated pixel value in DN, Grescale is band-specific rescaling gain factor in (watts/(meter squared*ster*µm))/DN, Brescale is band-specific rescaling bias factor in watts/(meter squared*ster*µm), LMIN is the spectral radiance that is scaled to QCALMIN in watts/(meter squared*ster*µm), LMAX is the spectral radiance that is scaled to QCALMAX in watts/(meter squared*ster*µm), QCALMIN is the minimum quantized calibrated pixel value (corresponding to LMIN) in DN and QCALMAX is the maximum quantized calibrated pixel value (corresponding to LMAX) in DN.

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Table1 summarize the TM spectral range, post-calibration dynamic ranges (LMIN and LMAX scaling parameters, the corresponding rescaling gain (Grescale) and rescaling bias (Brescale) values). QCALMIN=0 for data processed using NLAPS and QCALMIN=1 for data processed using LPGS. Table 1 Thermal band data (band 6 on TM) can be converted from spectral radiance to effective brightness temperature. The brightness temperature assumes that the Earth's surface is a black body (spectral emissivity of the black body is 1).Thermal radiance values were converted from spectral radiance to brightness temperature using the thermal constants by following equation: Where, = Satellite brightness temperature (Kelvin), = TOA spectral radiance, 1 = Calibration constant 1 from the metadata, Table 2 Since brightness temperature (T) is a blackbody temperature, the final step is the spectral emissivity according to the nature of the surface by temperature correction (Weng et. al. 2004): Where, = Satellite rightness temperature (Kelvin), = Wavelength of emitted radiance (11.5 ), = Land surface emissivity, = ℎ * ⁄ = 1.438*10 -2 mK ( = Boltzmann constant= 1.38*10 -23 J/K, h= Planckʼs constant= 6.626*10 -34 Js, c= velocity of light= 2.998*10 8 m/s).
Finally, derived land surface temperature in Kelvin was converted to Celsius by subtracting from 273.15.
For processing satellite data and database building, Gauss-Krueger coordinate system was selected. The False Colour Composite imagery of Landsat TM data (08 th Aug, 1998 and 06 th Aug, 2009) covered the study area was produced in ArcGIS environment ( Figure 2). Land use classes were selected in the False Colour Composite imagery for supervised classification of image. Different land use types were classified using Maximum Likelihood classification in ArcGIS environment ( Figure 3). Following land classification was accepted using the Landsat TM data: asphalt road, park and green spaces, waste land and bare soil, fallow land, residential (urban), commercial and industrial (Javed Mallick et al., 2008). Classification was performed on Landsat TM for 10 spectral separability of the land use classes existing in the study area. Finally, the relationship between land use classes, surface temperature and NDVI in Yazd was analyzed in detail.

Calibration and validation of Landsat TM LST and land use classification
The validation of Land Surface Temperature (LST) of satellite products is performed by using ground-based measurements of surface temperature. In the present study, ground-based data of land surface temperature received from Yazd Meteorological

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Bureau were used to validate LST of Landsat TM sensor. Measuring land surface temperature was performed using thermometers with SMT160 temperature sensor. The thermometers have a temperature range of -45 to 130 ° C. The most important characteristics of the SMT160 temperature sensor include absolute accuracy ∓0.7 ° C and measurement range 175 ° C. Geographical characteristics of the measurement points can be seen in Table 3. Table 3 20 Validation of the obtained temperatures from 08 th Aug, 1998 and 06 th Aug, 2009 images was performed using the Root-Mean-Square Error (RMSE) and Coefficient of Determination (R 2 ) between the measured (ground-based) and predicted (by satellite data) land surface temperatures (Xiaolei Yu, 2014).
The Kappa coefficient and overall accuracy were used to validate the land use classification. The error matrix is used as an analytical statistical technique. The simplest descriptive statistical indicator is overall accuracy which can be calculated using error 25 matrix. Producer's accuracy is the probability that a pixel in the classified image is placed in the same class on land. In the calculation, correct pixels total in each class is divided by the pixels total of that class as derived from the reference data. The producer's accuracy indicates the probability of a reference pixel being correctly classified. In the user's accuracy the correct pixels total in a land use class is divided by the total number of pixels that were classified in that class. The user's accuracy is indicative of the probability that a pixel classified on the image actually represents that category on the ground (Story and Congalton, 1986).
User's accuracy is the probability that a specific class of land was classified in the same class on the classified image (Figuers 4, 5 and Tables 5, 6).

Analysis of Land use:
By classifying land uses of study area, six land-use types for study area were considered: asphalt roads, parks and green spaces, waste land and bare soil, fallow land, residential, commercial and industrial areas. The land use distribution in Yazd is described in table 4. By comparing area percentage values of different land use classes between images, it can be concluded that land use types of study area were significantly converted in the 11-year period (Table 4). Table 4 shows that the asphalt roads, commercial, industrial and residential (Urban) classes of land use were increased. However, parks, green spaces, fallow lands, waste lands and 5 bare soils were decreased during the time. The study results show that residential areas increased most compared to the other classes of land use. The main changes include conversion of parks, green spaces, bare soils and fallow lands to residential areas.   Table 4 10 Table 5 Figure 4 For example, in1998 image by comparing the reference and classified data in the able 4, it was observed that although 100% of the asphalt roads were being correctly identified as asphalt roads, only 90.9% of the areas called asphalt roads are actually asphalt roads. And in the2009 image by comparing the reference and classified data, it was observed that 95.24% of the asphalt roads were 15 being correctly identified as asphalt roads, while 86.96% of the areas called asphalt roads are actually asphalt roads ( Table 6).The Kappa coefficient values of the1998 and2009 classified images were 0.96 and 0.95, respectively. The error matrixes indicate an overall accuracy of 0.97 and 0.95 for1998 and2009 classified images, respectively. These values obtained from analysis of the referenced data (ground-based data) and the classification output (Tables 5 and 6). 20 Figure 5

Analysis of vegetation situation and NDVI index
The spatial distribution of NDVI values from the Landsat TM image can be seen in figure

Land surface temperature analysis
In the present study, land surface temperature was retrieved by Temperature Emissivity Separation ( Table 7). Table 7 For the investigated area, results show that the land surface temperatures retrieved from TES algorithm using Landsat TM sensor data have the high accuracy with RMSE 0.9°C and 0.87°C for1998 and2009 images, respectively. The Coefficient of Determination 20 (R 2 ) between the measured and predicted temperatures is 0.98 and 0.99 for1998 and2009, respectively (Figure 8 and Table8).

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In the present study, strong relation was observed between NDVI index, surface temperature and land use types changes (Figure 9 and Table9, 10). The spatial variations of surface temperature are affected by the conversion of land for human-dominated use (land use change) and vegetation cover. There is a linear regression between surface temperature and NDVI (Figure 9).

Figure 9
Table 9 30 The significant differences less than 0.01 between NDVI values and temperatures of land use types for1998 and2009, cases were obtained by statistical analysis of NDVI values and surface temperatures. The significant differences less than 0.01 indicate that with the probability of 99 % temperature increasing (was caused by Land use changes) were causing a decrease in the NDVI values (Table 10).  industrial and residential areas increased, and these changes caused a rise in temperature. Other studies also confirm that the LST and NDVI changes are due to changes of the vegetation cover and residential areas (Gong Z. et al., 2015;Sandra E. et al., 2015;Valor E. et al., 1996;Wei L. et al., 2015 andJaved Mallick et al., 2008).

Conclusions
The study results show that LST, NDVI and surface emissivity can be estimated using Landsat TM sensor imagery with high accuracy. Calculate surface temperature and NDVI is important in the earth studies including global environmental change, urban climate change and urbanization. Different land use types of urban areas can be studied by estimating NDVI index and land surface temperature values. This paper explored the spatial and temporal relationship between NDVI, LST and land use types. It was 5 found, that in the Yazd city combination of vegetation cover decreasing, residential areas increasing and other changes in land use was directly causing surface temperature increasing. By comparing two different times (1998 and 2009), we concluded that the average surface temperature of the Yazd city has risen 1.45 degrees Celsius. Considering the impacts of land use types changes and vegetation cover decreasing on the rising surface temperature, the role of human activities becomes more and more evident in climate change. According to the results, simultaneous analysis of the NDVI, LST and land use types changes is ideal for the study 10 of urban environment and climate change, because of dealing directly with vegetation cover and surface temperature. It should be noted that surface temperature increase is also affected by climate change and rising Earth's annual temperature, especially in regions that are located in a belt of arid and semi-arid areas in the northern Hemisphere. Based on the study results, the highest percentage of Yazd area is residential areas, fallow lands, waste land and bare soil, and this is directly related to climate situation (arid and semi-arid climate) and human activities.  , 19, 1663-1681, 1998. 282−289, 2003 Mapping land surface emissivity from NDVI. Application to European, African and South American areas, Remote Sensing of Environment, 57, 167-184, 1996. Wei, L., Jean-Daniel,M. S., Thomas W. G.: A comparison of the economic benefits of urban green spaces estimated with NDVI and with high-resolution land cover data. Landscape and Urban Planning, 133, 105-117, 2015.