Materials and Methods
Survey of India (SOI) toposheets of 1:50000 and 1:250000 scales were used to generate base
layers. Field data were collected with a handheld GPS. Remote sensing data used for the study
are: Landsat MSS (1973), Landsat TM (1992), Landsat ETM+ (2000 and 2009) [Landsat
data downloaded from http://glcf.umiacs.umd.edu/data/], IRS (Indian Remote Sensing) LISS
(Linear Imaging Self Scanner)III of (1999 and 2006), MODIS (Moderate Resolution Imaging
Spectroradiometer) Surface Reﬂectance 7 bands product [downloaded from http://edcdaac.
usgs.gov/main.asp] of 2002, MODIS Land Surface Temperature/Emissivity 8Day L3 Global
and Daily L3 Global (V004 product) [http://lpdaac.usgs.gov/modis/dataproducts.asp#mod11].
Google Earth data (http://earth.google.com) served in pre and post classiﬁcation process and
validation of the results. Latest data for 2010 (IRS – Indian remote Sensing) was procured
from the National remote Sensing Centre (http://www.nrsc.gov.in), Hyderabad. The methods
adopted in the analysis involved:
 Georeferencing of acquired remote sensing data to latitudelongitude coordinate system
with Evrst 56 datum: Landsat bands, IRS LISSIII MSS bands, MODIS bands 1 and 2
(spatial resolution 250 m) and bands 3 to 7 (spatial resolution 500 m) were geocorrected
with the known ground control points (GCP’s) and projected to Polyconic with Evrst 1956
as the datum, followed by masking and cropping of the study area.
 Band 1, 2, 3 and 4 of Landsat 1973 data to 79 m.
 Band 1, 2, 3 and 4 of Landsat TM of 1992 to 30 m.
 Band 1, 2, 3, 4, 5 and 7 of Lansat ETM+ to 30 m.
 MODIS bands 1 to 7 to 250 m.
 IRS LISSIII band 1, 2 and 3 to 23.5 m.
 Thermal band of TM (resampled to 120m), ETM+ (to 60m) and MODIS (to 1 km)
and Panchromatic bands of ETM+ (resampled to 15 m).
 Supervised Classiﬁcation using Bayesian Classiﬁer: In supervised classiﬁcation, the pixel
categorisation process is done by specifying the numerical descriptors of the various LC
types present in a scene. It involves (i) training, (ii) classiﬁcation and (iii) output.
 Accuracy assessment: Accuracy assessments were done with ﬁeld knowledge, visual
interpretation and also referring Google Earth (http://earth.google.com).
 Computation of Normalised Difference Vegetation Index (NDVI): It separates green
vegetation from its background soil brightness and retains the ability to minimize
topographic effects while producing a measurement scale ranging from –1 to +1 with
NDVIvalues < 0 representing no vegetation.
Derivation of Land Surface Temperature (LST)
LST from Landsat TM: The TIR band 6 of Landsat5 TM was used to calculate the surface
temperature of the area. The digital number (DN) was ﬁrst converted into radiance LTM using
L_{TM} = 0.124 + 0.00563 * DN ….. (Equation 1)
The radiance was converted to equivalent blackbody temperature TTMSurface at the satellite using
T_{TMSurface} = K_{2}/(K_{1} – lnLTM¬) – 273 ….. (Equation 2)
The coefﬁcients K_{1} and K_{2} depend on the range of blackbody temperatures. In the blackbody
temperature range 260300K the default values (Singh, S. M., 1988) for Landsat TM are
K_{1} = 4.127 and K_{2} = 1274.7. Brightness temperature is the temperature that a blackbody
would obtain in order to produce the same radiance at the same wavelength (λ = 11.5 μm).
Therefore, additional correction for spectral emissivity (ε) is required to account for the
nonuniform emissivity of the land surface. Spectral emissivity for all objects are very close to 1,
yet for more accurate temperature derivation emissivity of each LC class is considered separately.
Emissivity correction is carried out using surface emissivities for the speciﬁed LC (table 1) derived
from the methodology described in Snyder et al., (1998) and Stathopoulou et al. (2006).
Table 1: Surface emissivity values by LC type
The procedure involves combining surface emissivity maps obtained from the Normalized
Difference Vegetation Index Thresholds Method (NDVITHM) (Sobrino and Raissouni, 2000)
with LC information. The emmissivity corrected land surface temperature (Ts) were ﬁnally
computed as follows (Artis and Carnhan, 1 982)
............(3)
where, λ is the wavelength of emitted radiance for which the peak response and the average
of the limiting wavelengths (λ = 11.5 μm) were used, ρ = h x c/σ (1.438 x 10^{2} mK), σ =
Stefan Bolzmann’s constant (5.67 x 10^{8} Wm^{2}K^{4} = 1.38 x 10^{23} J/K), h = Planck’s constant
(6.626 x 10^{34} Jsec), c = velocity of light (2.998 x 10^{8} m/sec), and ε is spectral emissivity.
LST from Landsat ETM+: The TIR image (band 6) was converted to a surface temperature
map according to the following procedure (Weng et al., 2004). The DN of Landsat ETM+
was ﬁrst converted into spectral radiance LETM using equation 4, and then converted to atsatellite brightness temperature (i.e., black body temperature, T_{ETMSurface}), under the assumption
of uniform emissivity (ε ≈ 1) using equation 5 (Landsat Project Science Ofﬁce, 2002):
L_{ETM} = 0.0370588 x DN + 3.2 ….. (Equation 4)
T_{ETMSurface} = K2/ln (K1/ LETM + 1) ….. (Equation 5)
where, TETMSurface is the effective atsatellite temperature in Kelvin, LETM is spectral
radiance in watts/(meters squared x ster x μm); and K2 and K2 are prelaunch calibration
constants. For Landsat7 ETM+, K2 = 1282.71 K and K1 = 666.09 mWcm^{2}sr1μm^{1} were
used(http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter11/chapter11.
html). The emissivity corrected land surface temperatures Ts were ﬁnally computed by
equation 3.
