MNDWI = GREEN
−
MIR GREEN + MIR
SAVI = 1.5
∗
NIR
−
RED NIR + RED + 0.5
NDBI = MIR
− NIR
MIR + NIR
IBI = NDBI
−
SAVI + MNDWI 2 NDBI + SAVI + MNDWI 2
2.4 Methods
2.4.1 Land use and land cover classification: To identify
dominant land-cover classes in the study area, unsupervised classification method was used. Iterative classification process
was carried out using Landsat TMETM+Landsat-8 and IRS R2 LISS-3 image acquired over the study area. Five dominant
land cover classes were extracted in the region. With the order of highest to the lowest percentage these classes included
built-up, water bodies, crop landfallow, scrubwaste land, vegetation. The in-situ sites were chosen only on five LC
classes due to the issues in finding suitable sites for some of the land cover types.
2.4.2
Surface wetness estimation: Water area accounts for
a small percentage in our study area, however, vegetation abundance and impervious surface posses nearly zero values in
these areas, thus it was advisable to include surface wetness for proper justification. To this end, the modified normalized
difference water index MNDWI proposed by Xu 2006 was utilized, which was expressed by:
1 Where green and middle infrared MIR are the spectral
reflectances of the Landsat 8TMETM+ band 2 and 5 respectively. Eq. 1 generated values in the range from -1 to
+1 where positive values indicate moist or wet area and negative values signify dry area.
2.4.3
Estimation of
greenness: The
heat island
phenomenon is occurring in urban areas where heat builds up through the artificial ground surface cover, the increase in
artificial waste heat, and the extreme advancement of the destruction of natural environment. Incorporating a greenness
index, site specific information can be achieved. In the present study soil adjusted vegetation index SAVI was used to
measure the temporal variation of surface greenness with the advancement of the sub-urban zone. This index is similar to
NDVI, but it suppresses the effects of soil pixels. It uses a canopy background adjustment factor, L, which is a function of
vegetation density and often requires prior knowledge of vegetation amounts. Huete 1988 suggests an optimal value of
L= 0.5 to account for first-order soil background variations. This index is best used in areas with relatively sparse
vegetation where soil is visible through the canopy.
2 Eq. 2 produces values in the range from -1 to +1 where
positive values indicate greenness or wet area and negative values signify less vegetation.
2.4.4 Derivation of impervious surface: Impervious surface is one of the most important land cover types and also a key
indicator of urban expansion and urban heat island effect. To effectively map the dynamics of the impervious surface an
integrated built up extraction approach was taken place. Extraction and classification of settlement areas were quickly
done based on approach to Index-based Built-up Index IBI, as was done by Xu 2007. The development of the Index-based
Built-up Index IBI approach in the study was done by adding multiple indices, which was expressed by:
3 4
Where, NDBI is the normalized difference vegetation index Eq. 3; MIR and NIR are the digital numbers of near and
middle infrared band MNDWI is modified normalized difference water index Eq. 1 and SAVI is soil adjusted
vegetation index Eq. 2. Derived IBI was used to extract impervious surface by classification of settlement areas and
non-settlement area. 2.4.5
Estimation of land surface temperature: Studies
have been done on the relative warmth or “heat island effect” of cities by measuring the air temperature, using
meteorological data. However, this method is time consuming and it leads to problems in spatial interpolation. Hence
satellite sensors can provide quantitative physical data at high spatial and temporal resolutions and repetitive coverage is
capable of measurements of earth surface conditions overtime Owen et al., 1998. A variety of algorithms have been
developed to retrieve land surface temperature from TMETM+ imagery, such as mono-window algorithm Qin et
al., 2001, single-channel algorithm Jimenez-Munoz and Sobrino, 2003 and the method proposed by Artis and
Carnahan 1982. In the present study several image transformation techniques were used to retrieve the land
surface temperature LST. The following equation was used to convert the digital number DN of Landsat5- TM TIR band
to spectral radiance Markham and Barker, 1985: L
λ
= 0.05518DN+1.2378 5
For Landsat-7 ETM+ band 6 is captured twice: once in low- gain e.g. Landsat-8 band 10 and the other in high-gain mode
e.g. Landsat-8 band 11. Low-gain mode is used to image surfaces with high brightness, and high-gain mode is used to
image surfaces with low brightness Ma. Y et al., 2010. In this study the high-gain setting was selected. The radiometric
calibration of thermal band of ETM+ and Landsat 8 was done using following constant:
Landsat TM Landsat ETM+
Landsat 8 K1
607.76 666.09
774.89
K2 1260.56
1282.71 1321.08
Table 2. TM, ETM+ and Landsat 8 thermal infrared band calibration constants
can be expressed by: L
λ
= 0.0370588 - DN + 3.2 6
For Landsat 8 can be expressed by: L
λ
= M
L
Q
CAL
+ A
L
7 Where M
L
is the band specific multiplicative rescalling factor from the metadata RADIANCE_MULT_BAND_DN, A
L
is the band specific additive rescaling factor from the metadata
RADIANCE_ADD_BAND_DN, Q
CAL
is the quantized and
ISPRS Technical Commission VIII Symposium, 09 – 12 December 2014, Hyderabad, India
This contribution has been peer-reviewed. doi:10.5194isprsarchives-XL-8-717-2014
719
T = K
2
ln K
1
L
λ
+ 1
calibrated standard product pixel values DN USGS Landsat 8 product, 2013.
Then the following formula was used to convert the spectral radiance to at-sensor brightness temperature under the
assumption of uniform emissivity Landsat Project Science Office, 2002; Wukelic et al., 1989:
8 Where, T is at-satellite brightness temperature K, L
λ
is TOA spectral radiance Watts m2sradµm, K
2
and K
1
are the prelaunch calibration constants, which are listed in Table 2.
Then the following formula was used to convert the temperature in degree Kelvin to degree Celsius.
C = T – 273
9 Where, C is the brightness temperature in degree Celsius, T is
degrees in Kelvin and Kelvin is 273 degrees lower than Celsius.
3. RESULTS AND DISCUSSION
