Understanding Urbanization and Pollution in Lahore Using Tasseled Cap Transformation

SUBMITTED BY: ATIQA IJAZ KHAN 0
Using Tasseled Cap Transformation Technique to
Study the Urban Environment, and its effect on
Pollution, in Lahore, Pakistan
Tuesday, May 06, 2014
Submitted To:
Dr. Arifa Lodhi
Subject:
Environmental Modeling and
Spatial Simulation
Submitted By:
Atiqa Ijaz Khan
Roll No.
Geom-02
Session:
2013-2015

,
Table of Contents
1. Chapter No. 01: Introduction 03
a. Urbanization 03
b. Pollution 03
c. Climate Change 04
d. Study Area and Location 05
i. Climatic Conditions 06
ii. Pollution 06
e. Objective and Scope of Project 08
f. Software Used 08
2. Chapter No. 02: Literature Review 09
a. Background Concepts 09
b. Tasseled Cap Transformation 09
3. Chapter No. 03: Datasets and Methodology 12
a. For Landsat ETM + 12
b. Datasets Used 18
c. Mathematical Procedures 18
4. Chapter No. 04: Results and Conclusions 20
a. Results 20
b. Analysis 24
c. Conclusions 25
5. Chapter No. 05: Problems and Recommendations 27
a. Problems Faced 27
b. Recommendations 27
6. References 28

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List of Figures
1. Fig 01: Urbanization of 1980 and 1995 03
2. Fig 02: Atmospheric Composition 04
3. Fig 03: Example of Pollution 05
4. Fig 04: Study Area 06
5. Fig 05: Pollution Concentration Lahore (1998) 07
6. Fig 06: Sources of Pollution in Pakistan 07
7. Fig 07: Lahore TCT 2000 20
8. Fig 08: Lahore TCT 2010 21
9. Fig 09: Lahore CVA (up), & Urbanization Trend (down) in 2000 & 2010 22
10. Fig 10: Lahore Agricultural Trend (up), & Wetness (down) in 2000 & 2010 23
11. Fig 11: Lahore GW Comparison 24
12. Fig 12: Lahore Achieve 2000 & 2010 25
13. Fig 13: Temperature Variations in 2000 & 2010 26
List of Tables
1. Tab 01: TCT values for Landsat MSS 10
2. Tab 02: TCT values for Landsat 5 TM 11
3. Tab 03: TCT values for Landsat ETM + 11
4. Tab 04: Conversion of DN5 to DN7 12
5. Tab 05: Conversion of DN7 to Radiance 13
6. Tab 06: Earth-sun distance as Function of Day 15
7. Tab 07: TCT ETM+ values 16
8. Tab 08: Lahore TCT Results 24
9. Tab 09: TCT Results (in Literature) 25

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Introduction
Urbanization:
Urbanization is a process of relative growth in a country’s urban population accompanied by an even
faster increase in the economic, political, and cultural importance of cities relative to rural areas. There
is a worldwide trend toward urbanization. In most countries it is a natural consequence and stimulus
of economic development based on industrialization and post-industrialization. Thus the level of
urbanization, as measured by the share of a country’s urban population in its total population, is
highest in the most developed, high-income countries and lowest in the least developed, low income
countries. At the same time, urbanization is progressing much faster in developing countries than in
developed countries.
Fig 01: Urban Population as part of total population, 1980 and 1995
Pollution:
Many forms of atmospheric pollution affect human health and the environment at levels from local
to global. These contaminants are emitted from diverse sources, and some of them react together to

,
form new compounds in the air. Industrialized nations have made important progress toward
controlling some pollutants in recent decades, but air quality is much worse in many developing
countries, and global circulation patterns can transport some types of pollution rapidly around the
world.
Fig 02: Process related to Atmospheric Composition
Climate Change:
Air pollutants are major contributors to climate change. This connection is well known to scientists,
although it has not yet permeated environmental policy. Global climate change has the potential to
magnify air pollution problems by raising Earth's temperature (contributing to tropospheric ozone
formation) and increasing the frequency of stagnation events. Climate change is also expected to cause
more forest fires and dust storms, which can cause severe air quality problems.

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Fig 03: Example of Pollution
Study Area:
Lahore is the 2nd
largest city of the Pakistan, and the capital city of the Province Punjab. It is located
between 31°15´-31°45Ń and 74°01ánd 74°39É it covers an area of 1014 km2
. It is bounded by
Sheikhupura, Wagah, and Kasur on north-west, east, and south, respectively. While River Ravi flows
on the northern side of the Lahore.

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Fig 04: Study Area
Climate of Lahore: The climate here is hot, semi-arid, with long hot summers, and short dry winters.
Sources of Pollution: Regular monitoring of ambient air quality is still not systematic in Pakistan. All
the available information is based on random and short term sampling conducted to assess the
concentrations of various pollutants. Many such studies have reported the ambient concentration of
air pollutants in various urban and rural centers of Pakistan, including Karachi, Hyderabad, Jamshoro,
Lakhra, Multan, Dera Ghazi Khan, Faisalabad, Lahore, Gujranwala, Pind Dadan Khan, Sargodha,
Fateh Jang, Khewra, Sialkot, Rawalpindi and Peshawar.

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Fig 05: Pollution Concentration in Lahore (1998)
The major sources of air pollution that are needed to be addressed are:
1. Emission from vehicles
2. Emission from industry
3. Natural dust
4. Burning of Solid Waste
Fig 06: Sources of Pollution in Pakistan

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Objectives and Scope of the Project:
The main objectives of this study are:
1. Extent of urbanization in Lahore city, since 2000;
2. Land cover and land use change detection for past decade (2000-2010);
3. Estimation of degradation of Green-cover;
4. Understanding the relationship between urbanization and climate changes, especially temperature.
5. Using the technique of Tasseled Cap Transformation for the urban change in Lahore (Hopefully
for the 1st
time).
This will helps in the assessment of potential accuracy of Tasseled Cap technique especially in urban
area. And put one step towards the understanding of PCA (principal component technique).
It will also help to understand the relationship between, urbanization and pollution. And also its effect
on local level of the vegetation production, and its relation with temperature profiles.
Software Used:
For this project, the software used are:
1. ArcGIS 10.1
2. ERDAS 13
3. Microsoft Word 2013
4. Microsoft Excel 2013

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Literature Review
Background Concepts:
The concept of “tasseled cap” transformation was introduced in 1976 by R.J. Kauth and G.S. Thomas
from the Environmental Research Institute of Michigan in the article "The tasseled Cap --A Graphic
Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat"
published in the paper “Proceedings of the Symposium on Machine Processing of Remotely Sensed
Data” (Indiana, Purdue University of West Lafayette). In this article the authors have set forth the
reasoning underlying the model proposed for Landsat data on agricultural lands and illustrated the
spectral behavior of agricultural crops, depending on their stage of development.
Originally constructed for understanding important phenomena of crop development in spectral
space, the transformation has potential applications in revealing key forest attributes such as species,
age and structure (e.g. Cohen et al. 1995).
Essentially, two tasseled cap transformations have been developed based on Landsat Thematic
Mapper (TM), based on:
1. Digital number (Crist and Cicone 1984);
2. Reflectance factor (Crist 1985).
Tasseled cap coefficients are calculated for the TOA reflectance data from the Landsat 7 ETM+
sensor by Huang et al (2002). These coefficients are directly applicable for Landsat 7 ETM+ TOA
reflectance data, and can be used with Landsat 5 TM data using a further transformation described in
Vogelmann et al. (2001).
Tasseled Cap Transformation:
In theory, the “tasseled cap” transformation is a work procedure facilitating the in-depth interpretation
and study of satellite data, aiming at reducing the amount of data layers (dimensionality). In essence,
the procedure uses mathematical equations to transform a number of multispectral bands (n) into a
new n-dimensional space.
In practice, the procedure is based on a linear transformation of data from the original image into
three new axes which become features of the transformation and may be described as follows:

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1. Brightness: capable of changing the total reflectance and the physical processes affecting it, so
that the new image shows surfaces with little or no vegetation;
a. According to Jensen (2000) the brightness band in Tasseled Cap Transformation is used
to identify urbanization areas which are particularly evident in this band.
2. Greenness: responsible for the enhanced absorption in the visible specter caused by plant
pigments including chlorophyll and by the high reflectance in infrared due to the internal structure
of leaves. By way of consequence, areas with vegetation shall obviously show in green.
a. The greenness band is an important source which provides information about vegetation.
(et al. Jensen, 2000)
3. Wetness: defining the “soil plan” and represents the primary feature.
a. Moisture status of the wetland information presents in the wetness band. TCT could be
helpful for use anywhere to disaggregate the quantity of soil brightness, vegetation, and
moisture content in independent pixels in satellite imagery (Jensen 2000).
4. According to Jensen (2000), thermal band calculates the quantity of infrared energy that is released
from the earth’s surface and it is practical for locating geothermal activity. Using the thermal band
as layer data could be helpful to improve classification accuracy by indicating the temperature of
each land use and land cover classes.
The longer infrared bands are the most sensitive to soil and plant moisture; therefore, the contrast of
visible and near-infrared bands with the longer-infrared bands highlights moisture levels within a scene
(Crist and Cicone 1984). Plant moisture is a biophysical parameter that is directly associated with
vegetation stress and biomass reduction (Jensen 2000, 367).
Table No. 01: TCT for Landsat MSS
In the specialty literature this transformation is separately treated for Landsat data taken with different
sensors. Thus, for the data recorded with Landsat MSS, researchers R. J. Kauth and G. S. Thomas

,
(1976), established four components, namely brightness, greenness, yellowness and the “non-such”
component, as well the corresponding percentages
Table No. 02: TCT for Landsat5 TM
Later, Crist and Cicone (1984) have adapted the “tasseled cap” transformation to the six bands of the
Landsat TM data, establishing new percentage values for only three components, i.e. brightness,
greenness and wetness, the last replacing yellowness.
As a rule, the first components contain most of the information, and therefore the four bands of the
Landsat MSS images or the six Landsat TM bands maybe reduced to the first three principal
components. In some specialty works, in the case of Landsat TM data a fourth component, namely
“fog” is introduced and coefficients are different. Unlike many other linear transformations used in
remote sensing, the coefficients used for operating the tasseled cap transformation are predetermined
and not derived from the set of data.
Table No. 03: TCT for Landsat ETM+
Tasseled cap coefficients are calculated for the TOA reflectance data from the Landsat 7 ETM+
sensor by Huang et al (2002).

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Datasets and Methodology
For Landsat ETM + Datasets:
The procedural way to Tasseled Cap Transformation (TCT) described by re-searchers and used in this
project is defined below:
1. The 1st
step is remove the 0-values from the images, or to set them to NO-data values. This can
be done using ERDAS 13. For this, set Clear to NO-data values in the metadata of every band of
each image.
2. In order to be able to use the tasseled cap coefficients developed by Huang et al. (2002) for the
Landsat 5 TM sensor, one must convert the Landsat 5 TM DN data into data that is equivalent to
data recorded by the Landsat 7 ETM+ sensor (because the two sensors have slightly different
calibration). This process is described by Vogelmann et al. (2001) in reverse; that is, they converted
from Landsat 7 ETM+ data to Landsat 5 TM equivalent. To convert from Landsat 5 TM DN
data to Landsat 7 ETM+ DN data, we use the following expression:
Where, DN7 is the Landsat 7 ETM+ equivalent DN data, DN5 is the Landsat 5 TM DN data,
and the slope and intercept are band-specific numbers given by the inverse of those found in
Vogelmann et al. (2001).
The needed values are given in the following table:
Table 04: DN5 to DN7 Conversion Data

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3. Before converting to reflectance data, one must convert the DN data to radiance. This is done
using the following expression:
Where, Lλ is the calculated radiance [in Watts / (sq. meter * µm * ster)], DN7 is the Landsat 7 ETM+
DN data (or the equivalent calculated in step 2), and the gain and bias are band-specific numbers. The
latest gain and bias numbers for the Landsat 7 ETM+ sensor are given in Chander et al. (2009) and
are shown in the following table:
Table No. 05: DN7 to Radiance
4. While radiance is the quantity actually measured by the Landsat sensors, a conversion to
reflectance facilitates better comparison among different scenes. It does this by removing
differences caused by the position of the sun and the differing amounts of energy output by the
sun in each band. The reflectance can be thought of as a “planetary albedo,” or fraction of the
sun’s energy that is reflected by the surface. It can be calculated using the following expression:
Where, Rλ is the reflectance (unit less ratio), Lλ is the radiance, d is the earth-sun distance (in
astronomical units), Esun, λ is the band-specific radiance emitted by the sun, and θSE is the solar
elevation angle.

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a. One needs three pieces of information in order to calculate the reflectance. The first is the
band-specific radiance emitted by the sun. These values are given in Chander et al. (2009) and
are repeated in the following table:
Table No. 06: Landsat Band Specific Radiance Emitted by Sun
b. The second information required is, θSE, the solar elevation angle. The solar elevation angle
and the day of year are listed in the header file for each scene. This file is included with the
data and ends with “_MTL.txt”. Search the file for the solar elevation angle labeled
“SUN_ELEVATION” and the day of the year labeled
“DATE_HOUR_CONTACT_PERIOD”. The solar elevation angle is given in degrees and
the date is in the format “YYDDDHH” where the 3 “D” digits denote the day of the year.
For example, “0624117” means the 241st
day of 2006 at 17 UTC.
c. And the third piece of information is d, the earth-sun distance. Once the day of the year is
acquired, use the table reproduced from Chander et al. (2009) to find the earth sun distance.
For example, for day 241, the earth-sun distance is 1.00992 astronomical units.
5. Keep in mind that the sine function within Arc Map requires the solar elevation angle to be in
radians instead of degrees. Convert from degrees to radians using:

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Table No. 06: Earth-sun Distance as Function of Day
6. During the conversion from DN data to reflectance, it is possible to create small negative
reflectance. These values are not physical and should be set to zero. It should be noted that only
very small negative numbers should be produced with this procedure. If large negative numbers
are calculated, this may signify a problem with the implementation of this procedure. Use raster
calculator to check for negative values and replace them with zero.

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7. Now finally calculate the TCTs using following formula:
Where, tas. capi is the calculated tasseled cap index for brightness, greenness, or wetness depending
on the coefficients used, the bands are the TOA reflectance calculated in this tutorial, and the
coefficients are given by Huang et al. (2002): (Ref: Fig 07-07)
Table No. 07: TCT for ETM+
8. Clip the final images of both years to the boundary limit of Lahore. As we were dealing with the
path row of 14938, so out focus was on the upper portion of the Lahore.
a. Extract by Mask, to clip the images to the Lahore boundary.
b. Use Raster to Polygon to mark out the clipped boundary of the Lahore.
9. The magnitude of vectors was calculated from the Euclidean Distance between the difference in
positions of the same pixel from different data-takes within the space generated by the axes
Greenness and Brightness as follows: (Ref: Fig 09 UP)

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Datasets Used:
The datasets used for this transformation are (Bands, 1-5, & 7):
Serial
No.
Satellite
and
Sensor
Names
Pathrow Date of
Acquisition
(DD-MM-
YYYY)
Total
Days
Earth-sun
Distance
(astronomical
unit)
Solar
Elevation
(Degrees)
01. Landsat 5
(TM)
14938 19-03-2000 79 0.99584 49.69202028
02. Landsat 7
(ETM+)
14938 02-03-2010 61 0.99108 34.945302925
Table No. 08: Datasets Information
Mathematical Procedure:
All the equations are performed in Raster Calculator, from Spatial Analyst tool.
The equations used for the conversion of Landsat 7 DN data to useful TCT are as follows:
1. Rad1 = (0.778740 * "LE71490382000079SGS01_B1.TIF" ) - 6.98
2. Ref1 = (3.141592654 * "Rad1" * Square(0.99584))/(1997 *
Sin(49.69202028*3.141592654/180))
3. Rc1 = Con("Ref1" < 0.0, 0.0, "Ref1")
4. Rad2 = (0.798819 *"LE71490382000079SGS01_B2.TIF" ) - 7.20
5. Ref2 = (3.141592654 * "Rad2" * Square(0.99584))/(1812 *
Sin(49.69202028*3.141592654/180))
6. Rc2 = Con("Ref2" < 0.0, 0.0, "Ref2")
7. Rad3 = (0.621654 *"LE71490382000079SGS01_B3.TIF" ) - 5.62
8. Ref3 = (3.141592654 * "Rad2" * Square(0.99584))/(1533 *
Sin(49.69202028*3.141592654/180))
9. Rc3 = Con("Ref1" < 0.0, 0.0, "Ref3")
10. Rad4 = (0.639764 *"LE71490382000079SGS01_B4.TIF" ) - 5.74

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11. Ref4 = (3.141592654 * "Rad4" * Square(0.99584))/(1039 *
Sin(49.69202028*3.141592654/180))
12. Rc4 = Con("Ref4" < 0.0, 0.0, "Ref4")
13. Rad5 = (0.126220 *"LE71490382000079SGS01_B5.TIF" ) - 1.13
14. Ref5 = (3.141592654 * "Rad5" * Square(0.99584))/(230.8 *
Sin(49.69202028*3.141592654/180))
15. Rc5 = Con("Ref5" < 0.0, 0.0, "Ref5")
16. Rad7 = (0.0438989 *"LE71490382000079SGS01_B7.TIF" ) - 0.39
17. Ref7 = (3.141592654 * "Rad7" * Square(0.99584))/(84.9 *
Sin(49.69202028*3.141592654/180))
18. Rc7 = Con("Ref7" < 0.0, 0.0, "Ref7")
For TCT Conversions
19. TCT_B = ("Rc1" * 0.3561) + ("Rc2" * 0.3972) + ("Rc3" * 0.3904) + ("Rc4" * 0.6966) +
("Rc5" * 0.2286) + ("Rc7" * 0.1596)
20. TCT_G = -("Rc1" * 0.3344) - ("Rc2" * 0.3354) - ("Rc3" * 0.4556) + ("Rc4" * 0.6966) - ("Rc5"
* 0.0242) - ("Rc7" * 0.2630)
21. TCT_W = ("Rc1" * 0.2626) + ("Rc2" * 0.2141) + ("Rc3" * 0.0926) + ("Rc4" * 0.0656) - ("Rc5"
* 0.7629) - ("Rc7" * 0.5388)
And, for the conversion of Landsat 5 DN to Landsat 7 DN data, the equations used are:
1. L7_1 = (0.943 * "LT51490382010034KHC00_B1.TIF") + 4.21
2. L7_2 = (1.776 * "LT51490382010034KHC00_B2.TIF") + 2.58
3. L7_3 = (1.538 * "LT51490382010034KHC00_B3.TIF") + 2.50
4. L7_4 = (1.427 * "LT51490382010034KHC00_B4.TIF") + 4.80
5. L7_5 = (0.984 * "LT51490382010034KHC00_B5.TIF") + 6.96
6. L7_7 = (1.304 * "LT51490382010034KHC00_B7.TIF") + 5.76

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Then the further procedure with converted data for TCT transformation,
1. Rad1 = (0.778740 * "L7_1" ) - 6.98
2. Ref1 = (3.141592654 * "Rad1" * Square(0.99108))/(1997 *
Sin(34.94530295*3.141592654/180))
3. Rc1 = Con("Ref1" < 0.0, 0.0, "Ref1")
4. Rad2 = (0.798819 * "L7_2" ) - 7.20
5. Ref2 = (3.141592654 * "Rad2" * Square(0.99108))/(1812 *
Sin(34.94530295*3.141592654/180))
6. Rc2 = Con("Ref2" < 0.0, 0.0, "Ref2")
7. Rad3 = (0.62165 * "L7_3" ) - 5.62
8. Ref3 = (3.141592654 * "Rad3" * Square(0.99108))/(1533 *
Sin(34.94530295*3.141592654/180))
9. Rc3 = Con("Ref3" < 0.0, 0.0, "Ref3")
10. Rad4 = (0.639764 * "L7_4" ) - 5.74
11. Ref4 = (3.141592654 * "Rad4" * Square(0.99108))/(1039 *
Sin(34.94530295*3.141592654/180))
12. Rc4 = Con("Ref4" < 0.0, 0.0, "Ref4")
13. Rad5 = (0.126220 * "L7_5" ) - 1.13
14. Ref5 = (3.141592654 * "Rad5" * Square(0.99108))/(230.8 *
Sin(34.94530295*3.141592654/180))
15. Rc5 = Con("Ref5" < 0.0, 0.0, "Ref5")
16. Rad7 = (0.043898 * "L7_7" ) - 0.39
17. Ref7 = (3.141592654 * "Rad7" * Square(0.99108))/(84.9 *
Sin(34.94530295*3.141592654/180))
18. Rc7 = Con("Ref7" < 0.0, 0.0, "Ref7")
At the end, repeat the TCT transformation equations as in Landsat 7 step.

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Results and Conclusions
Fig 07: Lahore TCT, 2000

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Fig 08: Lahore TCT, 2010

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Fig 09: Lahore CVA Magnitude 2000 – 2010 (UP) and Urban Trend (DOWN)

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Fig 10: Lahore Agricultural Trend 2000 & 2010 (UP) and Lahore Wetness Trend (DOWN)

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Analysis:
Few of the procedural steps taken to make the final ground for the analysis are:
1. Use Cut-fill from 3D Analyst tool, between the Lahore TCT images of 2000 and 2010, to find
out the trend of changes.
2. Used separately on the images of Brightness, Greenness, and Wetness. (Ref: Fig 09-10)
3. To find out the trend of changes in greenness and wetness, again use cut fill tool.
Fig 11: Trend in Wetness and Greenness
Serial No. Urbanization Greenness Wetness
Yr. 2000 Relatively
Less
Relatively
High
Moderate
Yr. 2010 Increment Decrement Moderate
Table No. 08 Comparison of Lahore TCT Results
The results shows that: Where there is increase in Brightness values, there’s always a decrease in
Greenness and Wetness values, and vice versa. So, in this way, Urbanization has inverse relation
with Greenness. While, Greenness comes in direct relation with Wetness.

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Fig 12: Lahore 2000 (UP), 2010 (DOWN)

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Other results, verified from the literature are:
Serial No. Brightness Greenness Results
01. High High Biomass Loss or Moisture Reduction
02. High Low Urbanization and De-forestation
03. Low High Re-forestation
04. Low Low Burning Loss or high Moisture content
Table 08: Results of TCT (literature)
Conclusions:
It can be concluded from the results of this technique, that overall, the Greenness content of Lahore
shows decline since 2000. It also shows that, with the passage of time, the agricultural growth
decreases. That has a major effect on the economy.
On the other hand, the increase in Urbanization plays a part for the support of the economy. But, it
leads to the pollution content. That has a serious effect on the human health and climate of the Lahore
over the past decade. As by increased in the content of air pollution, industrial waste, and dust. This
then directly effects the temperature. Which also shows increment from 2000 to 2010, and so on.
But, wetness graph shows a moderate or less variation since 2000, on the basis of satellite data.
Fig 13: Temperature variations of Lahore in 2000 and 2010
jan feb march april may jun jul aug sep oct nov dec
Temp(2000) © 8 16 22 32 34 30 28 32 32 28 22 8
Mon (2010) 0 0 0 0 0 0 0 0 0 0 0 0
Temp (2010)© 8 16 24 24 32 34 32 32 32 30 24 18
0
5
10
15
20
25
30
35
40
TEMPREATURE
MONTHS OF YEARS
Temperature Variation in 2000 & 2010
Temp(2000) © Mon (2010) Temp (2010)©

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Problems and Recommendations
Problems Faced:
Just a few problems faced, and they are as follows:
1. Acquisition of Landsat data along with Sensor information
2. Validity of Tasseled Cap Transformation methods.
3. Computationally lengthy procedure.
4. Much lengthier to perform on ArcGIS.
Recommendations:
Few of the recommendations regarding this technique are:
1. It’s better to perform on ERDAS or ENVI, than ArcGIS.
2. The Landsat should have proper sensor information regarding scenes.
3. For more accurate results, TCT technique should be used with CVA (Change Vector Analysis)
and PCA (Principal Component Analysis), or at-least with NDVI (or others).

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References
1. A Change Vector Analysis Technique To Monitor Land Use/Land Cover In Sw Brazilian
Amazon: Acre State, by, Rodrigo Borrego Lorena, João Roberto Dos Santos, Yosio Edemir
Shimabukuro, Irving Foster Brown, and, Hermann Johann Heinrich Kux,
2. A Comparison Of Forest Change Detection Methods And Implications For Forest Management,
By Ronnie D. Lea, Dr. C. Mark Cowell, Thesis Supervisor December 2005
3. Calculating Vegetation Indices From Landsat 5 Tm And Landsat 7 Etm+ Data, By Colorado State
University
4. Shams Zi. Health And Environment: Lead Pollution In Karachi Is A
Serious Health Hazard. Karachi, Pakistan, University of Karachi Environs
Institute of EnvironmentalStudies,1998.
5. Use of the “Tasseled Cap” Transformation for the Interpretation of Satellite Images Iosif
Vorovencii, Conf. Dr. Ing. Ec. – Universitatea “Transilvania” Din Braşov, Icatop@Yahoo.Com
6. Using Remote Sensing And Gis To Study Land-Use And Land-Cover Change In Alachua County,
Florida From 1993 To 2003, by, Muhammad Almatar, 2008
7. www.learner.org

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