Tasseled Cap transformation Technique in ArcGIS

Using Tasseled Cap Transformation Technique
To Study The Urban Environment, And Its
Effect On Pollution, In Lahore, Pakistan
Submitted To: Dr.Arifa
Lodhi
Submitted By: Atiqa
Ijaz Khan
Roll No. : Geom-02
Institute of Geology,
University of the Punjab

GREEN FIEND
GREENFIENDSlide No. 2,Tuesday, May 06, 2014
Table of Contents
Urbanization 03
Pollution and Climate Change
05
Study Area and its Specs
06
Objective and Scope of the Project
08
Software Used
09
Background Concepts of TCT
11
Tasseled Cap Transformation Technique Procedure
12
Datasets (Landsat ETM+)
23
Results
26

GREEN FIEND
INTRODUCTION

4,Tuesday, May 06, 2014
Urbanization
1. 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.
2. There is a worldwide trend toward urbanization.
3. In most countries it is a natural consequence.
4. At the same time, urbanization is progressing much
faster in developing countries than in developed countries.

Pollution and Climate
Change1. Many forms of atmospheric pollution affect human health and the environment at
levels from local to global.
2. These contaminants are emitted from diverse sources, and some of them react
together to form new compounds in the air.
3. Air pollutants are major contributors to climate change.
4. 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.

Study Area and its Specs
1. Lahore is the 2nd largest city of the Pakistan, and the capital city of the Province
Punjab. It is located between 31 15 -31 45 N and 74 01 and 74 39 E it covers an
area of 1014 km2 .
2. The climate here is hot, semi-arid, with long hot summers, and short dry winters.
3. Regular monitoring of ambient air quality is still not systematic in Pakistan.
4. 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.
5. The major sources of air pollution that are needed to be addressed are:
1. Emission from vehicles
2. Emission from industry

Objective and Scope of
the ProjectThe 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) and CVA (Change Vector Analysis).

Software Used
For this project, the software used are:
1. ArcGIS 10.1
2. ERDAS 13
3. Microsoft Word 2013
4. Microsoft Excel 2013

GREEN FIEND
LITERARTURE REVIEW

Background Concept
1. The concept of “tasseled cap” transformation was introduced in 1976 by R.J.
Kauth and G.S. Thomas.
2. 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).
3. 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).
4. Tasseled cap coefficients are calculated for the TOA reflectance data from the
Landsat 7 ETM+ sensor by Huang et al (2002).
5. 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).

Con’t
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:
1. Brightness (Urbanization)
2. Greenness (Agriculture and Forestation)
3. Wetness (Moisture)
4. Fog
5. Others Landsat Bands

Con’t
1. Brightness:
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:
The greenness band is an important source which provides information about vegetation. (et al.
Jensen, 2000)
3. Wetness:
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. Thermal Band:
According to Jensen (2000), thermal band calculates the quantity of infrared energy

LANDSAT MSS:
LANDSAT 5 TM

GREEN FIEND
METHODOLOGY

Procedure Step by Step
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. All the following steps are performed in ArcGIS 10.1 Raster Calculator.
3. Convert Landsat 5 DN Values into Landsat 7 DN Values:

4. 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:

5. While radiance is the quantity actually measured by the Landsat sensors, a
conversion to reflectance facilitates better comparison among different scenes.
6. 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.
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.
ArcGIS Raster Calculator takes angular measurements in radians, so must convert
them, as:

7. The solar elevation angle and the day of year are listed in the header file for each
scene.
8. This file is included with the data and ends with “_MTL.txt”.
9. Search the file for the solar elevation angle labeled “SUN_ELEVATION” and the
day of the year labeled “DATE_HOUR_CONTACT_PERIOD”.
10. 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.

Earth-sun Distance in
Astronomical Units as Function of
Days

11. During the conversion from DN data to reflectance, it is possible to create small
negative reflectance.
12. Now finally calculate the TCTs using following formula:

13. 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: (Optional Step)
14. Clip the final images of both years to the boundary limit of Lahore.

GREEN FIEND
DATASETS
(TM & ETM+)

1. The datasets used for this transformation are: ( including All Bands, 1-5, & 7)
Serial No.
Satellite
and
Sensor
Names
Pathro
w
Date of
Acquisition
(DD-MM-
YYYY)
Total
Days
Earth-sun
Distance
(astronomical
unit)
Solar
Elevation
(Degrees)
01.
Landsat
5 (TM)
1493
8
19-03-
2000
79 0.99584
49.692020
28
02.
Landsat
7
(ETM+)
1493
8
02-03-
2010
61 0.99108
34.945302
925

GREEN FIEND
RESULTS & CONCLUSIONS

Results

CVA Result

Urbanization trend 2000
& 2010

Agricultural Trend 2000
& 2010

Moisture Trend 2000 &
2010

Greenness and Wetness
Comparison

Google Earth Archieve
Lahore 2000 Lahore 2010

Analysis
Analysis of Lahore TCT Images:
Analysis and Results available from
Literature:
Serial No. Urbanization Greenness Wetness
Yr. 2000 Relatively
Less
Relatively
High
Moderate
Yr. 2010 Increment Decrement Moderate
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

Temperature variation of
Lahore in 2000 & 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)©

Conclusions
1. 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.
2. 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.
3. But, wetness graph shows a moderate or less variation since 2000, on the basis
of satellite data.

GREEN FIEND
PROBLEMS FACED &
RECOMMENDATIONS

Problems
Problems Faced:
1. Just a few problems faced, and they are as follows:
2. Acquisition of Landsat data along with Sensor information
3. Validity of Tasseled Cap Transformation methods.
4. Computationally lengthy procedure.
5. Much lengthier to perform on ArcGIS.

Recommendations
Recommendations:
1. Few of the recommendations regarding this technique are:
2. It’s better to perform on ERDAS or ENVI, than ArcGIS.
3. The Landsat should have proper sensor information regarding scenes.
4. 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).

GREEN FIEND
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. HealthAnd Environment:Lead Pollution In Karachi
Is A Serious Health Hazard. Karachi, Pakistan, University of
Karachi Environs Institute of Environmental Studies, 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

Tasseled Cap transformation Technique in ArcGIS

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