Download - 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
M.PHIL. GEOMATICS DEFENSE
Application of TCT as a Remote Sensing Change Detection Technique: A
Temporal Case Study of Lahore District -Pakistan
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
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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).
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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.
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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.
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Tasseled-like Cap formation, hence, its name.
Maturity Level
Initial Stage
Old Age
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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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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)
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).
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74°30'0"E
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74°20'0"E
74°20'0"E
74°10'0"E
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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
Material and Choice of Technology
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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
METHODOLOGY
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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
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.
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Unfilled
Filled
Tuesday, May 10, 2016CENTER OF EARTH AND ENVIRONMENTAL SCIENCES, UNIVERSITY OF THE PUNJAB 14
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:
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Raw Imagery (DN = Q)Radiance (Lλ):Lλ G ∗ Q B At-Sensor Reflectance ( ): ∗ ∗∗
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)
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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:
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From To Total (Pixels)
Column 3759 6212 2454 Samples
Row 3023 5675 2536 Lines
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.
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RESULTS AND MAJOR FINDINGS
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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).
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.
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).
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.
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
T-cap for Lahore District
Tuesday, May 10, 2016CENTER OF EARTH AND ENVIRONMENTAL SCIENCES, UNIVERSITY OF THE PUNJAB 25
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.
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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
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
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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.
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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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