m.phil geomatics defense (10may2016)

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Tuesday, May 10, 2016 CENTER 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

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Page 1: M.Phil Geomatics Defense (10May2016)

Tuesday, May 10, 2016CENTER OF EARTH AND ENVIRONMENTAL SCIENCES, UNIVERSITY OF THE PUNJAB 1

Presenter: Atiqa Ijaz Khan

Advisor: Prof. Dr. Sajid Rashid AhmedM.Sc. (Pb), M.Sc. (Canada), Ph.D. (Canada)

Co-supervisor: Dr. M.Hassan Ali BaigM.Sc. (China) Ph. D (China)

Presented To: M.Phil. Geomatics Thesis Committee, CEES, University of the Punjab.

Dated: Tuesday, May 10, 2016

Page 2: M.Phil Geomatics Defense (10May2016)

M.PHIL. GEOMATICS DEFENSE

Application of TCT as a Remote Sensing Change Detection Technique: A

Temporal Case Study of Lahore District -Pakistan

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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

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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 machinelearning. Including:

SVM (Support Vector Machine)

RF Classifiers (Radom Forest)

ANN (Artificial Neural Network)

Also along with PCA (Principal Component Analysis) and CVA (ChangeVector Analysis).

Tuesday, May 10, 2016Center of Earth and Environmental Sciences, University of the Punjab 4

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Objectives Of This Study

My aim is to check the accuracy of Tasseled Cap over a “HighlyPopulated” area using its counter techniques, like:

1. Greenness component with NDVI (Normalized DifferenceVegetation Index)

2. Brightness component with BI (Bare Soil Index)

3. And to find any relation between Brightness component withurbanization trend.

Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 5

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What Others Have Done?

Developed by Kauth and Thomas in1976 (Kauth & Thomas, 1976). Andit was tested on agricultural field tostudy the plant growth using LandsatMSS imagery.

Since then it is widely used. Althoughit is a senor dependent technique.Now it has been applied on manysatellite imagery. And more are likelyto 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

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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)

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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)

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Study Area For Research

A district of 9.3 million souls by the end ofDec, 2014. With 7.7 million (82%) residesunder the urban domain (Government of thePunjab, 2014).

68% of population increases in urbanpopulation within 1972 – 2009 (Riaz, 2013).

If this rate continues, the remaining 52% ofurban greenery will be vanished by 2030(Baloch, 2011).

A region marked with 04 seasons, but mostlyhave 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°4

0'0"

N

31°4

0'0"

N

31°3

0'0"

N

31°3

0'0"

N

31°2

0'0"

N

31°2

0'0"

N

STUDY AREA: DISTRICT LAHORE

Balochistan

Fata

KPK

Sindh

AJKDisputedTerritory

Punjab

Source:Punjab Development Statistics, 2014

Data Sources: ESRI Online ImageryNespak (pvt) Ltd.Open Source Data

μ

LEGEND

[· Allama Iqbal International Airport

Major Road

Trunk Road

Railway Track

River Ravi

District Lahore

International Boundary

LEGENDDistrict Lahore

Federa l Capital Territory

Province Punjab

Disputed Territory

Pakistan Provincial Boundary

International Boundary

INDIA0 4 8 12 16 20km

0 100 200 300 400 500km

Province Punjab Overview

Pakistan Overview

N

AFGHANISTAN

INDIA

CH

INA

Prepared By: Atiqa Ijaz Khan

μNAME AREA POPULATION(sq. km) (000' persons)

Pakistan 796100 17956Punjab 205345 99794District Lahore 1772 9253

Districts 36Tehsils 141Union Councils 3646Cantonment Boards 20Police Stations 708

PUNJAB STATITICS (2014)

Source:Punjab Development Stat istics , 2014

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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

FormatSLC 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 5.22. ERDAS version 20133. MATLAB version 2013b4. ArcGIS version 10.1

Remote Sensing Software

GIS Software

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METHODOLOGY

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

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Stacking Gap Fill UnstackingSynchronization

of Datasets

Radiance Conversion

Gain and Offset Adjustment

At-sensor Conversion

Tasseled Cap

Data SubsettingOutput

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. C

lass

ifica

tion

2. Indices

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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

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

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Synchronization of Datasets: Renaming previously unstacked individualbands as sated in the MTL (metadata) file.

A necessary step to proceed.

DN - Radiance – TOA (Top of Atmosphere) Reflectance Conversion: It wasperformed 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 ( ): ∗ ∗∗

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Lλ = Spectral Radiance (W m-2 sr-1μm-1).

G = Rescaled Gain (W m-2 sr-1μm-1) = λ – λ– B = Rescaled Bias (W m-2 sr-1μm-1) = Offset = Lλmin

Q = Quantized calibrated pixel value (DN Values, 0 - 255)

= Unitless TOA Reflectance

= At-sensor radiance (W m-2 sr-1μm-1)

= Earth-Sun distance in astronomical units

= Solar irradiance (W m-2μm-1)

= Sun zenith angle (degree)

Π = 3.14159 (mathematical constant)

Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 16

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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

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Classification: Initial values were estimated through OTSU algorithm inMATLAB. These estimated values were then tested visually against eachother in 10 pairs of range.

Accuracy Assessment: Accuracy was assessed by confusion matrix. Itwas performed in ENVI.

Regression Analysis: It includes Co-efficient of determination (R-Square), RMSE (Root Mean Square Error), and Correlation. It wasperformed in MATLAB.

Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 18

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RESULTS AND MAJOR FINDINGS

Tuesday, May 10, 2016Center Of Earth And Environmental Sciences, University Of The Punjab 19

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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 andurban land isnot properlydifferentiated incase of TasseledCap BrightnessComponent(TBC).

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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

Generallysaying, NDVIand BI exhibitreverse relation.Where there ishigh value of BI,NDVI showslowest values.

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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 highlevel ofagreementbetween NDVIand TasseledCap GreennessComponent(TGC).

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TGC 2015

TBC 2015

NDVI 2015High : 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 similartrend continuesfor the year2015.

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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

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T-cap for Lahore District

Tuesday, May 10, 2016CENTER OF EARTH AND ENVIRONMENTAL SCIENCES, UNIVERSITY OF THE PUNJAB 25

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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

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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

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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

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Recommendations

Few of the recommendations are:

Results can be more accurately verified if provided the highresolution imagery of a particular area, like of Quickbird.

Seasonal analysis can be made more detailed by having largerdatasets.

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

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References1. Baig, M. H. A., Zhang, L., Shuai, T., & Tong, Q. (2014). Derivation of a Tasselled Cap Transformation

Based on Landsat 8 at-Satellite Reflectance. Remote Sensing Letters, 5(5), 423-431.

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 ofImpervious Surface Area by Landsat Remote Sensing. Paper presented at the Proceedings, Pecora 16Conference.

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 WaterStress. Photogrammetric engineering and remote sensing (USA).

6. Crist, E. P., & Cicone, R. C. (1984). Application of the Tasseled Cap Concept to Simulated ThematicMapper Data(Transformation for Mss Crop and Soil Imagery). Photogrammetric Engineering andRemote Sensing, 50, 343-352.

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7. DiGirolamo, P. A. (2006). A Comparison of Change Detection Methods in an UrbanEnvironment Using Landsat Tm and Etm+ Satellite Imagery: A Multi-Temporal, Multi-Spectral Analysis of Gwinnett County, Ga 1991-2000.

8. Fiorella, M., & Ripple, W. J. (1993). Determining Successional Stage of TemperateConiferous Forests with Landsat Satellite Data. Photogrammetric Engineering and RemoteSensing;(United States), 59(2).

9. Gao, B.-C. (1996). Ndwi—a Normalized Difference Water Index for Remote Sensing ofVegetation Liquid Water from Space. Remote Sensing of Environment, 58(3), 257-266.

10. Government of the Punjab. (2014). Punjab Development Statistics. Lahore.

11. Horler, D., & Ahern, F. (1986). Forestry Information Content of Thematic Mapper Data.International Journal of Remote Sensing, 7(3), 405-428.

12. Horne, J. H. (2003). A Tasseled Cap Transformation for Ikonos Images. Paper presented atthe ASPRS 2003 Annual conference proceedings.

13. Huang, C., Wylie, B., Yang, L., Homer, C., & Zylstra, G. (2002). Derivation of a TasselledCap Transformation Based on Landsat 7 at-Satellite Reflectance. International Journal ofRemote Sensing, 23(8), 1741-1748.

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14. Huete, A. R. (1988). A Soil-Adjusted Vegetation Index (Savi). Remote Sensing of Environment, 25(3),295-309.

15. Ivits, E., Lamb, A., Langar, F., Hemphill, S., & Koch, B. (2008). Orthogonal Transformation ofSegmented Spot5 Images. Photogrammetric Engineering & Remote Sensing, 74(11), 1351-1364.

16. Joseph, W. S., Laurence, R. M., William, M. H., & Mathew, D. R. (2003). Using the Landsat 7Enhanced Thematic Mapper Tasseled Cap Transformation to Extract Shoreline (pp. 14). USA: U.S.Geological Survey.

17. Kauth, R. J., & Thomas, G. S. (1976). The Tasselled Cap--a Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. Paper presented at the LARSSymposia.

18. Lobser, S., & Cohen, W. (2007). Modis Tasselled Cap: Land Cover Characteristics Expressed throughTransformed Modis Data. International Journal of Remote Sensing, 28(22), 5079-5101.

19. Mellor, A., Haywood, A., Stone, C., & Jones, S. (2013). The Performance of Random Forests in anOperational Setting for Large Area Sclerophyll Forest Classification. Remote Sensing, 5(6), 2838-2856.

20. Ramdani, F. (2013). Extraction of Urban Vegetation in Highly Dense Urban Environment withApplication to Measure Inhabitants’ Satisfaction of Urban Green Space.

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21. Riaz, O. (2013). Urban Change Detection of Lahore (Pakistan) Using aTime Series of Satellite Images since 1972. Asian journal of naturaland applied sciences, 2(4), 100-104.

22. USGS. (2013). Landsat 7 Slc-Off Gap-Filled Data Sources. Filling theGaps to use in Scientific Analysis. 2016, fromhttp://landsat.usgs.gov/sci_an.php#2

23. Wang, Y., & Sun, D. (2005). The Aster Tasseled Cap InteractiveTransformation Using Gramm-Schmidt Method. Paper presented atthe MIPPR 2005 SAR and Multispectral Image Processing.

24. Yarbrough, L. D., Easson, G., & Kuszmaul, J. S. (2005). Quickbird 2Tasseled Cap Transform Coefficients: A Comparison of DerivationMethods. Paper presented at the Pecora, Sioux Falls, South Dakota

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