Change Detection on Dubai1987 - 2010

AG2416 Advanced Remote SensingSession 1, Spring 2013

Adrian C PrelipceanIpsit Dash

http://blog.friendlyplanet.com/media/Camels-at-Jebel-Ali-beach-Dubai-iStock-5011247.jpg

http://www.ssqq.com/archive/images/dubai20%20tower.jpg

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Flow of Presentation

• Why Dubai?• What Changed from 1987 – 2010?• Which Data?• What Methods?• What Results?

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DubaiFinancially Strong backed by Oil Resources

Lies in Arabian Desert Area- Sandy and Gravel Desert, Well known for frequent Dunes running N-S due to Salt Crusted Coastal Plains

Mega Project City-(Offshore)~ Palm Islands~ The World(Inland)~Business Bay~ Burj Khalifha~ Dubai Waterfront

2006Landsat 7 ETM+; 28.5 m

1990Landsat 4 TM; 28.5 m

1973Landsat 1 MSS; 57 m

http://earthobservatory.nasa.gov/IOTD/view.php?id=7153

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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

5http://citizenfable.files.wordpress.com/2012/11/dubai_masterplan.jpg

The PALM JEBEL ALI The PALM JUMERIAH The WORLD The PALM DEIRA

Dubai 2012

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Change Detection – Remote Sensing

• The change must be detectable in the Imagery• Describing ChangeAbrupt vs Subtle Real vs Detected Natural vs Artificial

Interesting vs UninterestingUninteresting 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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Flow Plan

• Image Differencing• Image Rationing• Change Vector Analysis

Input~ 1987 Imagery~ 2010 Imagery

Output~ Differenced Imagery~ Rationed Imagery~ CVA Imagery~ Accuracy Assessment

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Data and its characteristics

Landsat Imagery TM 4-5

Procesing Softwares

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Selecting the BandsBand TM

1 .45-.52 µm blue

2 .52-.6 µm green

3 .63-.69 µm red4 .76-.9 µm NIR5 1.55-1.75 µm SWIR

6 10.4-12.5 µm TIR

7 2.08-2.35 µm SWIR

Feature Best Gray-scale

False Color, or NIR

Band (black and white)

Clear Water 4 Black tone

Black

Silty Water 2, 4 Dark in 4 BluishNonforested

Coastal Wetlands

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Dark gray tone

between black

water and light gray

land

Blocky pinks, reds, blues,

blacks

Sand and Beaches 2, 3 Bright in all bands

White, bluish, light buff

Urban Areas:

3, 4

Usually light

tones in 3,

Mottled bluish-gray with

whitish and reddish specks

Commercial dark in 4Urban Areas:

3, 4

Mottled gray,

street patterns visible

Pinkish to reddish

Residential

Transportation

3, 4

Linear patterns; dirt and concrete

roads light in 3, asphalt

dark in 4.

Source: http://zulu.ssc.nasa.gov/mrsid/tutorial/Landsat%20Tutorial-V1.html

Band 2: Green light penetrates clear water fairly well, and gives excellent contrast between clear and turbid (muddy) water. It helps find oil on the surface of water, and vegetation (plant life) reflects more green light than any other visible color. Manmade features are still visible.Band 3: Red light has limited water penetration. It reflects well from dead foliage, but not well from live foliage with chlorophyll. It is useful for identifying vegetation types, soils, and urban features.Band 4: Near IR is good for mapping shorelines and biomass content. It is very good at detecting and analyzing vegetation.Band 7: Another short wavelength infrared has limited cloud penetration and provides good contrast between different types of vegetation. It is also useful to measure the moisture content of soil and vegetation

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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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Image normalization

X Y Slope Intercept RBand 1 -1987 Band 1 -2010 0.53 12.51 0.97Band 2 -1987 Band 2 -2010 0.54 5.16 0.97Band 3 -1987 Band 3 -2010 0.57 2.53 0.97Band 4 -1987 Band 4 -2010 0.64 0.22 0.97Band 5 -1987 Band 5 -2010 0.57 -0.01 0.96Band 6 -1987 Band 6 -2010 0.42 66.15 0.98Band 7 -1987 Band 7 -2010 0.55 0.23 0.96

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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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Image differencing

𝑅𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔𝐵𝑎𝑛𝑑𝑖=𝐴𝑏𝑠(𝐵𝑎𝑛𝑑𝑖2010−𝐵𝑎𝑛𝑑𝑖

1987)

𝑅𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔𝐵𝑎𝑛𝑑𝑖=𝐵𝑎𝑛𝑑𝑖2010−𝐵𝑎𝑛𝑑𝑖

1987

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Image differencing

𝑅𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔𝐵𝑎𝑛𝑑𝑖=𝐵𝑎𝑛𝑑𝑖2010−𝐵𝑎𝑛𝑑𝑖

1987

𝑅𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔𝐵𝑎𝑛𝑑𝑖=𝐴𝑏𝑠(𝐵𝑎𝑛𝑑𝑖2010−𝐵𝑎𝑛𝑑𝑖

1987)

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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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Image rationing

𝑅𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔𝐵𝑎𝑛𝑑𝑖=𝐸𝑥𝑝 (𝐴𝑏𝑠 (𝐿𝑜𝑔 𝐵𝑖2010−𝐿𝑜𝑔𝐵𝑖

1987 ))¿

𝑅𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔𝐵𝑎𝑛𝑑𝑖=𝐵𝑎𝑛𝑑𝑖

2010

𝐵𝑎𝑛𝑑𝑖1987

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Change Vector Analysis

• Change Vector Analysis (CVA) is a technique where multiple image bands can be analyzed simultaneously.

• Pixel values are vectors of spectral bands. Change Vectors are calculated by simple subtraction.

• No-change is 0 length• Change direction may be interpretable.• A threshold is required• We used Bands 2, 3, and 4 for the analysis

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Results – Image Differencing

Band 2

Threshold Imagery

Band 3

Threshold Imagery

Band 4

Threshold Imagery

Band 7

Threshold Imagery

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Band 4

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Difference Imagery FCC Absolute Difference Band 4,3,2

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Results- Image Rationing

Band 2

Threshold Imagery

Band 3

Threshold Imagery

Band 4

Threshold Imagery

Band 7

Threshold Imagery

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Band 4

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Rationed Imagery FCCRatio band 4,3,2

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Change Vector Analysis

𝑅𝑒𝑠𝑢𝑙𝑡𝑖𝑛𝑔𝐵𝑎𝑛𝑑=√ (𝐵22010−𝐵2

1987)2+(𝐵32010−𝐵3

1987 )2+(𝐵42010−𝐵4

1987 )2

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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