1) The document summarizes the use of remote sensing to detect changes in Dubai between 1987 and 2010, focusing on offshore development projects. 2) Methods used for change detection included image differencing, image rationing, and change vector analysis applied to Landsat imagery from the two time periods. 3) Results showed areas of new development for the Palm Jumeirah, Palm Jebel Ali, and The World islands using threshold imagery from differenced and rationed bands that isolated changed pixels.

1. AG2416 Advanced Remote Sensing
Session 1, Spring 2013
Change Detection on Dubai
1987 - 2010
http://www.ssqq.com/archive/images/dubai20%20tower.jpg
http://blog.friendlyplanet.com/media/Camels-at-Jebel-Ali-beach-Dubai-iStock-
5011247.jpg
Adrian C Prelipcean
Ipsit Dash

2. Flow of Presentation
• Why Dubai?
• What Changed from 1987 – 2010?
• Which Data?
• What Methods?
• What Results?
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3. Financially Strong
Dubai
backed by Oil 2006
Resources Landsat
7 ETM+;
28.5 m
Lies in Arabian Desert Area-
Sandy and Gravel Desert, Well
known for frequent Dunes
running N-S due to Salt Crusted 1990
Landsat 4
Coastal Plains TM;
28.5 m
Mega Project City-
(Offshore)
~ Palm Islands
1973
~ The World Landsat
(Inland) 1 MSS;
57 m
~Business Bay
~ Burj Khalifha http://earthobservatory.nasa.gov/IOTD/view.ph
p?id=7153
~ Dubai Waterfront
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4. Changes
• Huge Real Estate Changes involving Mega
Projects
• Transport Network and Urbanization changed.
• Creation of offshore projects like Palm
Jumeriah, The World
• Our Work bases on change detection in
Offshore Projects from 1987 - 2010
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5. The PALM JEBEL ALI The PALM JUMERIAH The WORLD The PALM DEIRA
Dubai 2012
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http://citizenfable.files.wordpress.com/2012/11/dubai_masterplan.jpg

6. Change Detection – Remote Sensing
• The change must be detectable in the Imagery
• Describing Change
Abrupt vs Subtle Real vs Detected Natural vs Artificial
Interesting vs Uninteresting
Uninteresting Changes
• Phenological Changes
– Seasonal Variations
• Sun angle effects
– Radiometric calibration
– Same period while acquiring images
• Atmospheric effects
– Radiometric calibration
• Geometric
– Ensure highly accurate registration
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7. Flow Plan
• Image Differencing
• Image Rationing Output
Input
~ 1987 Imagery • Change Vector Analysis ~ Differenced Imagery
~ 2010 Imagery ~ Rationed Imagery
~ CVA Imagery
~ Accuracy Assessment
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8. Data and its characteristics
Landsat Imagery TM 4-5
Procesing Softwares
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9. Feature Gray- False Color, or
Best
scale NIR
Band
(black and
white)
Selecting the Bands
Clear Water Black Black
4
tone
Band TM
Silty Water 2, 4 Dark in 4 Bluish
Nonforested Dark gray Blocky pinks, 1 .45-.52 µm blue
Coastal Wetlands tone reds, blues, 2 .52-.6 µm green
between blacks
3 .63-.69 µm red
4 black
water and 4 .76-.9 µm NIR
light gray 5 1.55-1.75 µm SWIR
land 6 10.4-12.5 µm TIR
Sand and Beaches Bright in White, bluish, 7 2.08-2.35 µm SWIR
2, 3
all bands light buff
Urban Areas: Usually Mottled bluish-
light gray with
Band 2: Green light penetrates clear water fairly well, and gives excellent
tones in whitish and
3, 4 contrast between clear and turbid (muddy) water. It helps find oil on the
3, reddish specks
surface of water, and vegetation (plant life) reflects more green light than
any other visible color. Manmade features are still visible.
Commercial dark in 4
Band 3: Red light has limited water penetration. It reflects well from dead
Urban Areas: Mottled Pinkish to
foliage, but not well from live foliage with chlorophyll. It is useful for
gray, reddish
3, 4 street identifying vegetation types, soils, and urban features.
Residential patterns Band 4: Near IR is good for mapping shorelines and biomass content. It is
visible very good at detecting and analyzing vegetation.
Transportation Linear Band 7: Another short wavelength infrared has limited cloud penetration
patterns; and provides good contrast between different types of vegetation. It is also
dirt and useful to measure the moisture content of soil and vegetation
concrete
3, 4
roads
light in 3,
asphalt
dark in 4. Source: http://zulu.ssc.nasa.gov/mrsid/tutorial/Landsat%20Tutorial-V1.html 9

10. Image normalization
• The relative correction aims to reduce
variation among multiple images by adjusting
the target image (the bands from 1987) to
match the base image (the bands from 2010)
i.e. to normalize the target image with respect
to the base image.
• We used the pseudo invariant feature (PIFs) in
PCI Geomatica for this.
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11. Image normalization
X Y Slope Intercept R
Band 1 -1987 Band 1 -2010 0.53 12.51 0.97
Band 2 -1987 Band 2 -2010 0.54 5.16 0.97
Band 3 -1987 Band 3 -2010 0.57 2.53 0.97
Band 4 -1987 Band 4 -2010 0.64 0.22 0.97
Band 5 -1987 Band 5 -2010 0.57 -0.01 0.96
Band 6 -1987 Band 6 -2010 0.42 66.15 0.98
Band 7 -1987 Band 7 -2010 0.55 0.23 0.96
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12. Image differencing
• Pros:
– Simple
– Straightforward
– Easy to interpret
• Cons:
– Cannot provide a detailed change matrix
– The difficulty in selecting suitable thresholds to
identify the changed areas
– Requires atmospheric calibration so that the “no-
change” value is equal to 0
– Have to worry about selecting suitable image bands
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13. Image differencing
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14. Image differencing
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15. Image rationing
• Pros:
– Simple
– Reduces impacts of the sun angle, shadow and topography
• Cons:
– Cannot provide a detailed change matrix
– Scales change according to a single date, so same change
on the ground may have different score depending on
direction of change
– Non-normal distribution of the result is often criticized
– The difficulty in selecting suitable thresholds to identify
the changed areas
– Have to worry about selecting suitable image bands
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16. Image rationing
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17. Change Vector Analysis
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18. Results – Image Differencing
Band 3 Band 4 Band 7
Band 2
Threshold Threshold Threshold
Threshold
Imagery Imagery Imagery
Imagery
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19. Band 4
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20. Difference Imagery FCC
Absolute Difference Band 4,3,2
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21. Results- Image Rationing
Band 2 Band 3 Band 4 Band 7
Threshold Imagery Threshold Imagery Threshold Imagery Threshold Imagery
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22. Band 4
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23. Rationed Imagery FCC
Ratio band 4,3,2
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24. Change Vector
Analysis
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25. References
• Introductory Digital Image Processing: A Remote Sensing Perspective –
John R. Jensen (Third Edition 2005)
• Change detection techniques - D. Lu, P. Mausel, E. Brondi’Zio and E. Moran
• Geographic Resources Decision Support System for land use, land cover
dynamics analysis - T. V. Ramachandra, Uttam Kumar
• http://zulu.ssc.nasa.gov/mrsid/tutorial/Landsat%20Tutorial-V1.html
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