The document presents an M.Phil. thesis defense on the application of the Tasseled Cap Transformation (TCT) for remote sensing change detection in Lahore District, Pakistan. The study aims to assess the accuracy of TCT in a highly populated area and evaluates its correlation with various indices such as NDVI and BI over different years. Key findings indicate that TCT shows high accuracy for NDVI, but has limited differentiation between soil and urban areas, with recommendations for improved analysis through high-resolution imagery and larger datasets.

1. Tuesday, May 10, 2016CENTER OF EARTH AND ENVIRONMENTAL SCIENCES, UNIVERSITY OF THE PUNJAB 1
Presenter: Atiqa Ijaz Khan
Advisor: Prof. Dr. Sajid Rashid Ahmed
M.Sc. (Pb), M.Sc. (Canada), Ph.D. (Canada)
Co-supervisor: Dr. M.Hassan Ali Baig
M.Sc. (China) Ph. D (China)
Presented To: M.Phil. Geomatics Thesis Committee, CEES,
University of the Punjab.
Dated: Tuesday, May 10, 2016

2. M.PHIL. GEOMATICS DEFENSE
Application of TCT as a Remote Sensing
Change Detection Technique: A
Temporal Case Study of Lahore District -
Pakistan

3. AGENDA
• Objectives Of This Study
• What Others Have Done?
• Study Area For Research
• Material and Choice of Technology
• Methodology
• Results and Major Findings
• Recommendations
• References
Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 3

4. Description Of Problem That Lead To
Research
 Not a single research has been conducted on this topic in Pakistan
 Used as an initial input for many advance techniques like machine
learning. Including:
 SVM (Support Vector Machine)
 RF Classifiers (Radom Forest)
 ANN (Artificial Neural Network)
 Also along with PCA (Principal Component Analysis) and CVA (Change
Vector Analysis).
Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 4

5. Objectives Of This Study
 My aim is to check the accuracy of Tasseled Cap over a “Highly
Populated” area using its counter techniques, like:
1. Greenness component with NDVI (Normalized Difference
Vegetation Index)
2. Brightness component with BI (Bare Soil Index)
3. And to find any relation between Brightness component with
urbanization trend.
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 5

6. What Others Have Done?
 Developed by Kauth and Thomas in
1976 (Kauth & Thomas, 1976). And
it was tested on agricultural field to
study the plant growth using Landsat
MSS imagery.
 Since then it is widely used. Although
it is a senor dependent technique.
Now it has been applied on many
satellite imagery. And more are likely
to originate.
Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 6
Tasseled-like Cap formation, hence, its
name.
Maturity Level
Initial Stage
Old Age

7. Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 7
APPLICATIONS RESEARCHER, YEAR
Agriculture (Fiorella & Ripple, 1993)
Forest Classification (Horler & Ahern, 1986)
Sea Shore (Joseph et al., 2003)
Water Indices (Gao, 1996)
Spectral Enhancement Technique (Yarbrough et al., 2005)
Vegetation Indices (Cohen, 1991; Huete, 1988)
Urban Environment (Bauer et al., 2005; DiGirolamo, 2006)

8. Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 8
SATELLITE/SENSORS RESOLUTION RESEARCHER, YEAR
Landsat MSS Low (Kauth & Thomas, 1976)
Landsat TM Moderate (Crist & Cicone, 1984)
Landsat ETM+ Moderate (Huang et al., 2002)
IKONOS Very High (Horne, 2003)
QuickBird Very High (Yarbrough et al., 2005)
ASTER Moderate (Wang & Sun, 2005)
MODIS Low (Lobser & Cohen, 2007)
SPOT High (Ivits et al., 2008)
Worldview Very High (Ramdani, 2013)
Landsat-8 Moderate (Baig et al., 2014)

9. Study Area For Research
 A district of 9.3 million souls by the end of
Dec, 2014. With 7.7 million (82%) resides
under the urban domain (Government of the
Punjab, 2014).
 68% of population increases in urban
population within 1972 – 2009 (Riaz, 2013).
 If this rate continues, the remaining 52% of
urban greenery will be vanished by 2030
(Baloch, 2011).
 A region marked with 04 seasons, but mostly
have the semi-arid climatic conditions
(Chaudhry et al., 2004).
Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 9
[·
74°40'0"E
74°40'0"E
74°30'0"E
74°30'0"E
74°20'0"E
74°20'0"E
74°10'0"E
74°10'0"E
74°0'0"E
74°0'0"E
31°40'0"N
31°40'0"N
31°30'0"N
31°30'0"N
31°20'0"N
31°20'0"N
STUDYAREA: DISTRICT LAHORE
Balochistan
Fata
KPK
Sindh
AJK
Disputed
Territory
Punjab
Source:
Punjab Development Statistics, 2014
Data Sources:
ESRI Online Imagery
Nespak (pvt) Ltd.
Open Source Data
μ
LEGEND
[· Allama Iqbal International Airport
Major Road
Trunk Road
Railway Track
River Ravi
District Lahore
International Boundary
LEGEND
District Lahore
Federal Capital Territory
Province Punjab
Disputed Territory
Pakistan Provincial Boundary
International Boundary
INDIA0 4 8 12 16 20
km
0 100 200 300 400 500
km
Province Punjab Overview
Pakistan Overview
N
AFGHANISTAN
INDIA
CHINA
Prepared By: Atiqa Ijaz Khan
μNAME AREA POPULATION
(sq. km) (000' persons)
Pakistan 796100 17956
Punjab 205345 99794
District Lahore 1772 9253
Districts 36
Tehsils 141
Union Councils 3646
Cantonment Boards 20
Police Stations 708
PUNJAB STATITICS (2014)
Source:
Punjab Development Statistics, 2014

10. Material and Choice of Technology
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 10
Raster
Dataset
Path Row
(dd-mm-yyyy) Cloud (%)
MTL File
Format
SLC Status
(WRS-2)
Landsat
7 (ETM+)
149 38 19-03-2000 20 .txt OFF
149 38 02-04-2005 20 .txt OFF
149 38 15-03-2010 20 .txt OFF
149 38 25-02-2015 20 .txt OFF
• The major software tools that helped are:
1. ENVI version 5.2
2. ERDAS version 2013
3. MATLAB version 2013b
4. ArcGIS version 10.1
Remote Sensing Software
GIS Software

11. METHODOLOGY
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 11

12. Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 12
Stacking Gap Fill Unstacking
Synchronization
of Datasets
Radiance
Conversion
Gain and Offset
Adjustment
At-sensor
Conversion
Tasseled Cap
Data Subsetting
Output
Formatting
Metadata
NDVI (Vegetation
Index)
BI (Bare Soil Index)
OTSU
Classification
TGC
TBC
NDVI
BI
Accuracy
Assessment
Overall
Accuracy
Confusion
Matrix
Regression
Analysis
R-Square Correlation RMSE
1. Data Pre-Processing
4. Accuracy Assessment
3.Classification
2. Indices

13. Methodology Used
 Data Pre-Processing: is performed in ERDAS Model Maker, as it involves:
 Stacking the visible bands of Landsat (Band: 1 – 5, & 7)
 Filling the gaps using (USGS, 2013)
 Unstacking these bands.
Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 13
Unfilled
Filled

14. Tuesday, May 10, 2016CENTER OF EARTH AND ENVIRONMENTAL SCIENCES, UNIVERSITY OF THE PUNJAB 14

15.  Synchronization of Datasets: Renaming previously unstacked individual
bands as sated in the MTL (metadata) file.
 A necessary step to proceed.
 DN - Radiance – TOA (Top of Atmosphere) Reflectance Conversion: It was
performed in ENVI. using these formulas:
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 15
Raw Imagery (DN = Q)
Radiance (Lλ):
Lλ G ∗ Q B
At-Sensor Reflectance ( ):
∗ ∗
∗

16.  Lλ = Spectral Radiance (W m-2 sr-1 μm-1).
 G = Rescaled Gain (W m-2 sr-1 μm-1) = λ – λ
–
 B = Rescaled Bias (W m-2 sr-1 μm-1) = Offset = Lλmin
 Q = Quantized calibrated pixel value (DN Values, 0 - 255)
 = Unitless TOA Reflectance
 = At-sensor radiance (W m-2 sr-1 μm-1)
 = Earth-Sun distance in astronomical units
 = Solar irradiance (W m-2 μm-1)
 = Sun zenith angle (degree)
 Π = 3.14159 (mathematical constant)
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 16

17.  Tasseled Cap Transformation: TOA directly used as input for T-cap. And
‘ll get 6 images against each year - group. Performed in ENVI.
 Data Sub-set: And then finally subset to Lahore District at:
 These have to be constant throughout the process of subsetting.
 Indices: NDVI and BI are performed in ENVI, with BI having formula of:
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 17
From To Total (Pixels)
Column 3759 6212 2454 Samples
Row 3023 5675 2536 Lines

18.  Classification: Initial values were estimated through OTSU algorithm in
MATLAB. These estimated values were then tested visually against each
other in 10 pairs of range.
 Accuracy Assessment: Accuracy was assessed by confusion matrix. It
was performed in ENVI.
 Regression Analysis: It includes Co-efficient of determination (R-
Square), RMSE (Root Mean Square Error), and Correlation. It was
performed in MATLAB.
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 18

19. RESULTS AND MAJOR FINDINGS
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 19

20. TGC 2000
TBC 2000
NDVI 2000
BI 2000
High : 0.173
Low : -0.083
High : 0.419
Low : 0.049
High : 0.602
Low : -0.546
High : 0.269
Low : -0.356
YEAR 2000
Ü
By:Atiqa Ijaz Khan
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 20
In year of 2000,
the soil and
urban land is
not properly
differentiated in
case of Tasseled
Cap Brightness
Component
(TBC).

21. TGC 2005
TBC 2005
NDVI 2005
BI 2005
High : 0.173
Low : -0.083
High : 0.587
Low : 0.054
High : 0.692
Low : -0.425
High : 0.217
Low : -0.5
YEAR 2005
Ü
By:Atiqa Ijaz Khan
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 21
Generally
saying, NDVI
and BI exhibit
reverse relation.
Where there is
high value of BI,
NDVI shows
lowest values.

22. TGC 2010
TBC 2010
NDVI 2010
BI 2010
High : 0.161
Low : -0.095
High : 0.587
Low : 0.054
High : 0.679
Low : -0.401
High : 0.229
Low : -0.477
YEAR 2010
Ü
By:Atiqa Ijaz Khan
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 22
Year 2010,
displays a high
level of
agreement
between NDVI
and Tasseled
Cap Greenness
Component
(TGC).

23. TGC 2015
TBC 2015
NDVI 2015
High : 0.160
Low : -0.098
High : 0.570
Low : 0.039
High : 0.765
Low : -0.224
High : 0.141
Low : -0.606
YEAR 2015
Ü
By:Atiqa Ijaz Khan
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 23
And the similar
trend continues
for the year
2015.

24. Results
Tuesday, May 10, 2016CENTER OF EARTH AND ENVIRONMENTAL SCIENCES, UNIVERSITY OF THE PUNJAB 24
VS Year Months R^2 Correlation Confusion Matrix Kappa Coefficient
TGC vs NDVI 2000 March 0.9814 0.9907 75.771% 0.5602
TGC vs NDVI 2005 April 0.9671 0.9834 73.645% 0.4112
TGC vs NDVI 2010 March 0.9774 0.9886 79.266% 0.6128
TGC vs NDVI 2015 Feb 0.9606 0.9802 76.681% 0.5822
VS Year Months R^2 Correlation Confusion Matrix Kappa Coefficient
TBC vs BI 2000 March 0.0539 0.2326 61.847% 0.2542
TBC vs BI 2005 April 0.0143 0.1196 65.883% 0.0469
TBC vs BI 2010 March 0.1196 0.3487 72.120% 0.1755
TBC vs BI 2015 Feb 0.0223 -0.1477 67.933% 0.0360

25. T-cap for Lahore District
Tuesday, May 10, 2016CENTER OF EARTH AND ENVIRONMENTAL SCIENCES, UNIVERSITY OF THE PUNJAB 25

26. Major Findings
 In the case of highly populated area, the results are:
 TGC and NDVI shows more than 90% of accuracy.
 TBC has no direct differentiation between soil and urban areas.
 BI shows inter-mixed ranges of bare soil and urban rooftops.
 Spring season, Month of March, provides with highest accuracy.
Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 26

27. Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 27
Year File Range Indicator
2000 TGC 0.047 - 0.17 Vegetation
< 0.047 Soil + Water
2005 TGC 0.025 - 0.16 Vegetation
< 0.025 Soil + Water
2010 TGC 0.03 - 0.16 Vegetation
< 0.03 Soil + Water
2015 TGC 0.02 - 0.16 Vegetation
< 0.02 Soil + Water
2000 TBC 0.2 - 0.3 Bare Soil
< 0.2 Urban + Water
2005 TBC 0.2 - 0.26 Bare Soil
< 0.2 Urban + Water
2010 TBC 0.18 - 0.32 Bare Soil
< 0.18 Urban + Water
2015 TBC 0.16 - 0.33 Bare Soil
< 0.16 Urban + Water
Physical Interpretation of Resulted Values

28. Year File Range Indicator
2000 NDVI 0.18 - 0.6 Vegetation
2005 NDVI 0.27 - 0.68 Vegetation
2010 NDVI 0.37 - 0.67 Vegetation
2015 NDVI 0.5 - 0.76 Vegetation
2000 BI 0.1 - 0.2 Bare Soil
> 0.2 Urban
2005 BI 0 - 0.19 Bare Soil
> 0.19 Urban
2010 BI 0 - 0.23 Bare Soil
> 0.23 Urban
2015 BI -0.16 - 0.08 Bare Soil
> 0.08 Urban
Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 28

29. Recommendations
 Few of the recommendations are:
 Results can be more accurately verified if provided the high
resolution imagery of a particular area, like of Quickbird.
 Seasonal analysis can be made more detailed by having larger
datasets.
 Leaf on and leaf off analysis can be made out of seasonal studies.
 Different methods of gap fill can be tested, if it effects the accuracy.
Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 29

30. References
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2. Baloch, A. A. (2011). Urbanization of Arable Land in Lahore City in Pakistan: A Case-Study.
Canadian Social Science, 7(4), P58-66.
3. Bauer, M., Loeffelholz, B., & Wilson, B. (2005). Estimation, Mapping and Change Analysis of
Impervious Surface Area by Landsat Remote Sensing. Paper presented at the Proceedings, Pecora 16
Conference.
4. Chaudhry, Q., Mahmood, A., Rasul, G., & Azfal, M. (2004). Agroclimatic Classification of Pakistan.
Science Vision, 9(3-4), 59-66.
5. Cohen, W. B. (1991). Response of Vegetation Indices to Changes in Three Measures of Leaf Water
Stress. Photogrammetric engineering and remote sensing (USA).
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Mapper Data(Transformation for Mss Crop and Soil Imagery). Photogrammetric Engineering and
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31. 7. DiGirolamo, P. A. (2006). A Comparison of Change Detection Methods in an Urban
Environment Using Landsat Tm and Etm+ Satellite Imagery: A Multi-Temporal, Multi-
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32. 14. Huete, A. R. (1988). A Soil-Adjusted Vegetation Index (Savi). Remote Sensing of Environment, 25(3),
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33. 21. Riaz, O. (2013). Urban Change Detection of Lahore (Pakistan) Using a
Time Series of Satellite Images since 1972. Asian journal of natural
and applied sciences, 2(4), 100-104.
22. USGS. (2013). Landsat 7 Slc-Off Gap-Filled Data Sources. Filling the
Gaps to use in Scientific Analysis. 2016, from
http://landsat.usgs.gov/sci_an.php#2
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Transformation Using Gramm-Schmidt Method. Paper presented at
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Tasseled Cap Transform Coefficients: A Comparison of Derivation
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