ASTER satellite dataset was used in order to effectively identify the spatial distribution characteristics of land cover/land use (LULC) classes and surface temperature for the city of Abha. This study assessed the LULC, normalized difference vegetation index (NDVI) and surface temperature, the spectral radiance value is converted from DN (Digital number) in each pixel by using equation:

where, UCC is unit conversion coefficients from HDF file.

The unit conversion coefficients that are used for different bands and for different gain settings are given in the ASTER user handbook. Thereafter, the satellite image has been geometrically rectified to a common UTM WGS84 coordinate system.

Considering the objectives of the present study, the following classification scheme used for interpreting land use based on Anderson et al., [27] system: built-up land; water bodies; agricultural cropland; dense vegetation (forest); sparse vegetation; fallow land; baresoil/wasteland; bushes and scrubland and rock outcroplands. An extensive field survey was done to identify sample points in the imagery for different LU/LC classes at various locations using navigation system (GPS). Based on the collected sample sets for respective LU/LC classes, training sets were selected for digital image classification using Maximum Likelihood classifier (MLC) technique. To validate the classified LULC, an accuracy assessment has been carried out. For the confusion matrix, derived from image map and field data (signature file), as described by Lillesand et al., [3] was generated for the accuracy assessment. The overall accuracy of LULC map was 88.35 and Kappa coefficient was 0.866.

The process of digital elevation model (DEM) creation begins with the scanned, geo-referenced Topographic raster Map (1:50,000). Contour Lines with 20 meters interval, spot elevations, from the raster image are extracted, converted to digital vectors and given elevation values. The Grid-based DEM was generated from the extracted digital contour vector data. The DEM was produced with the “Topo to Raster” interpolation techniques using ArcGIS software. This interpolation technique was specially designed for the creation of hydrologically corrected DEM [28] . The range of elevation of the watershed is found to be from 1951 to 2991 meters. The maximum height is located in the western part of study area whereas it gradually decreases towards the eastern part.

NDVI is the vegetation index used by researchers for extracting vegetation density from remotely sensed data. In essence, the algorithm isolates the dramatic increase in reflectance over the visible red to near infrared wavelengths, and normalizes it by dividing by the overall brightness of each pixel in those wavelengths as shown in the equation:

The values in either band have been converted from raw DN values to reflectance of solar electromagnetic radiation. The result of this algorithm is a single band data, the values ranges from −1 to +1, where values close to +1 signify greater vegetation cover. The NDVI values are estimated in the range of −0.041 to 0.487, having a mean value of 0.113 with a standard deviation of 0.046.

In this study, land surface temperature (LST) was estimated from the thermal infrared bands of ASTER satellite dataset using a temperature emissivity separation (TES) model [29] . TES attempts to compensate for reflected down welling irradiance and estimates the absolute spectral emissivity. The additional constraint to overcome the under-determination comes from the regression of the minimum emissivity of spectral contrast (calculated from laboratory spectra), used to equalize the number of unknowns and measurements, so that the set of Planck’s equations for the measured thermal radiances can be inverted. ASTER has 14 spectral bands, out of which 5 thermal bands (10 - 14) operate between 8 - 12 mm. In this study, five emissivity and one surface temperature maps were produced using the TES algorithm. It not only estimates the temperature of homogeneous areas of known emissivity, such as water bodies, but also for heterogeneous areas of unknown emissivity. The spectral radiance of thermal bands at the sensor is calculated using Equation (2).

(2).

where,

= spectral radiance observed by the sensor,

= surface emissivity at wavelength j,

= spectral radiance from a blackbody at surface temperature T,

= spectral radiance incident upon the surface from the atmosphere (down welling), from MODTRAN,

= spectral radiance emitted by the atmosphere (upwelling), from MODTRAN

= spectral atmospheric transmission, from MODTRAN.

At-sensor radiance data were corrected for atmospheric effects to obtain the radiance emitted by the surface (L_j), using the MODTRAN radiative transfer model. The standard atmospheric parameter (tropical climate) has been used. The output parameters obtained have been used to estimate the radiance using Equations (3) & (4).

where,

Hence,

(4).

where,

= First radiation constant = 3.74151 × 10⁻¹⁶

= First radiation constant = 0.0143879 (mk),

= Wavelength of channel j, (m),

= Temperature.

In the above equation, if the surface emissivity is known, it is possible to correct for the reflected sky radiation. Then the surface temperature may be calculated using Equation (5).

The above equation shows that for radiance measured in “n” spectral channels, there will be “n + 1” unknowns, “n” emissivities and one surface temperature. In TES [29] the estimated kinetic temperature T is taken to be the maximum temperature estimated from the radiance for the five ASTER TIR spectral bands employing equation 5. An assumed emissivity value (typically 0.97) is used in order to be within ±0.03 for typical land surfaces (heterogeneous land). The relative emissivities “β_j” have been computed by the following equations:

(6).

where,

For emissivities between 0.7 - 1.0, the ratios are generally within 0.7 - 1.4.

The maximum-minimum difference between the emissivity ratios is given by. It is observed that MMD ranges from 0.0 to 4.0 at most (Dash et al., 2002). An empirical relation between minimum emissivity and MMD is:

Therefore, the revised emissivity can be computed using the beta spectrum as shown below:

Beta is determined from the measured surface radiance (surface) and new emissivity, to obtained surface temperature. The resultant maps were put into Equation (2) and were processed until equation 11. Thereafter, the process was again repeated so as to arrive at an acceptable emissivity measurement. Based on the above procedures, a detailed model was developed in the ERDAS Imagine to automate the calculation of the surface temperature and five emissivity maps (as the TES algorithm generate) (Figure 2).

Figure 3 shows the most dominant class of LULC in 2012 was the exposed rock (51.29%) followed by bushes and scrubland (10.96%), sparse vegetation (10.71%), and agricultural cropland (4.40%) The built-up area (9.40%) is mainly in the central and southeastern part of the study area. This is due to foot hills, water bodies, wadies (dry ephemeral riverbed) and topographically suitable are for infrastructure.

Figure 4 shows the Ts of ASTER, dated 7th Nov. 2012. The estimated Ts range from 10.68˚C to 34.58˚C with

a mean value of 24.19˚C and standard deviation of 3.63 (Table 3). It is observed that in the image, areas in the eastern and central eastern exhibit maximum Ts range that corresponds to the rock outcroplands and baresoil/ wastelands (27˚C to 34.58˚C) and topographical at lower altitude. Areas in the west, south-west and north- west have lower Ts corresponding to dense and sparse vegetation and topographically high altitude. Water bodies exhibit minimum Ts.

Figure 5 and Table 3 show the estimated Ts values over different land use/land cover categories. Rock outcropland exhibits the highest Ts having mean Ts of 26.48˚C with a standard deviation of 2.85. Scrubs/bare soil values are also high having a mean value of 26.16˚C with a standard deviation of 3.02. These high temperatures vary associated with their thermal capacities and altitudinal effects. On the other hand water bodies show lower Ts having mean Ts of 18.07˚C with a standard deviation of 2.89. The variation in water depth (lake to wadies) is high at different locations caused high std. deviation. The impact of vegetation is clearly seen as low temperature values observed over dense vegetation, sparse vegetation, and agricultural cropland.

Figure 6 shows the linear regression coefficient between Ts with elevation and NDVI with significance levels of 0.01. It is inferred that elevation and NDVI values tend to be negatively correlated with Ts. Coefficient of determination (R²) for NDVI is 0.5154 and (R²) for elevation is 0.6198 (Figure 7). It is observed that coefficient of determination (R²) of Ts with elevation is better as compared with that for NDVI.

We examined the effects of topography on the spatial distribution of Ts. Jabal al Sooda is the highest point (2981 meters), situated in western part of the study area, whereas urban areas are located on the foot hills of Jabal al Sooda (Figure 8). The elevation varies from 1951 to 2991 meters from east to west. Spatial irregularities

in the study area i.e. topography, land use, vegetation and wadies, were thought to affect the spatial distribution of Ts. Figure 9 shows the spatial relationship of Ts and elevation with their histograms. Their pattern reveals the effects of topography on Ts. Table shows the altitudinal effects of different LULC on their Ts. It indicates that at the elevation of 1950-2150 meters, Ts is high over built-up areas, with mean temperature of 25.23˚C (Table 4). With increase in altitude, the mean Ts become low about 16.29˚C. These trends of Ts with altitude are also prevalent in all the LULC classes. The variations in hypsometry derivatives, has strong effect on Ts.