Remote Sensing For Land Cover Change Detection

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• Remote Sensing For Land Cover Change
  Detection

By JWAN M. ALDOSKI Geospatial Information
Science Research Center (GISRC), Faculty of
Engineering, Universiti Putra Malaysia, 43400
UPM Serdang, Selangor Darul Ehsan. Malaysia.
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Change Detection

• Goal Use remote sensing to detect change on a
  landscape over time

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Change Detection

• Plan for today
• What is change?
• Avoiding uninteresting change
• Methodologies
• Dr. Ripple Examples

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Change Detection

• To use remote sensing, the change must be
  detectable with our instruments
• Spectrally
• Distinguish use from cover
• Allow sufficient time between images for changes
  to be noticeable
• Spatially
• Generally, grain size of change event gtgt pixel
  size

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Change Detection

• Write a list of potential changes that you
  think might be interesting to observe with remote
  sensing
• Any type of remote sensing
• Any period of time over which change occurs

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Change Detection

• Describing change
• abrupt vs. subtle
• human vs. natural
• real vs. detected

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Change Detection

• We need to separate interesting change from
  uninteresting change

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Change Detection Uninteresting Change?

• Phenological changes
• Use anniversary date image acquisition
• Sun angle effects
• Radiometrically calibrate
• Use anniversary date image acquisition
• Atmospheric effects
• Radiometrically calibrate
• Geometric
• Ensure highly accurate registration

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Change Detection Radiometric Calibration

• Minimize atmospheric, view and sun angle effects
• Radiometric normalization
• Histogram equalization or match
• Noise reduction
• Haze reduction

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Change Detection Radiometric Calibration

• Histogram matching

Pixel Count
Band 4
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Change Detection Radiometic Calibration

• Regression Approach

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Change Detection

• Atmospheric correction
• Model atmospheric effects using radiative
  transfer models
• Aerosols, water vapor, absorptive gases

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Change Detection Methods
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Change Detection Methods

• Basic model
• Inputs
• Landsat TM image from Date 1
• Landsat TM image from Date 2
• Potential output
• Map of change vs. no-change
• Map describing the types of change

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Change Detection Methods

• Display bands from Dates 1 and 2 in different
  color guns of display
• No-change is greyish
• Change appears as non-grey
• Limited use
• On-screen delineation
• Masking

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Change Detection Methods

• Image differencing
• Date 1 - Date 2
• No-change 0
• Positive and negative values interpretable
• Pick a threshold for change
• Often uses vegetation index as start point, but
  not necessary

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Image Differencing
Image Date 1
Image Date 2
Difference Image Image 1 - Image 2
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Change Detection Methods

• Image differencing Pros
• Simple (some say its the most commonly used
  method)
• Easy to interpret
• Robust
• Cons
• Difference value is absolute, so same value may
  have different meaning depending on the starting
  class
• Requires atmospheric calibration for expectation
  of no-change zero

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Change Detection Methods

• Image Ratioing
• Date 1 / Date 2
• No-change 1
• Values less than and greater than 1 are
  interpretable
• Pick a threshold for change

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Change Detection Methods

• Image Ratioing Pros
• Simple
• May mitigate problems with viewing conditions,
  esp. sun angle
• Cons
• Scales change according to a single date, so same
  change on the ground may have different score
  depending on direction of change I.e. 50/100
  .5, 100/50 2.0

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Change Detection
Image Difference (TM99 TM88)
Image Ratio (TM99 / TM88)
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Change Detection Methods

• Change vector analysis
• In n-dimensional spectral space, determine length
  and direction of vector between Date 1 and Date 2
  

Band 4
Band 3
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Change Detection Methods

• No-change 0 length
• Change direction may be interpretable
• Pick a threshold for change

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Change Detection Methods

• Change detection Pros
• Conceptually appealing
• Allows designation of the type of change
  occurring
• Cons
• Requires very accurate radiometric calibration
• Change value is not referenced to a baseline, so
  different types of change may have same change
  vector

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Change Detection Methods

• Post-classification (delta classification)
• Classify Date 1 and Date 2 separately, compare
  class values on pixel by pixel basis between dates

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Change Detection Methods

• Post-classification Pros
• Avoids need for strict radiometric calibration
• Favors classification scheme of user
• Designates type of change occurring
• Cons
• Error is multiplicative from two parent maps
• Changes within classes may be interesting

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Change Detection Methods

• Composite Analysis
• Stack Date 1 and Date 2 and run unsupervised
  classification on the whole stack

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Change Detection Methods

• Composite Analysis Pros
• May extract maximum change variation
• Includes reference for change, so change is
  anchored at starting value, unlike change vector
  analysis and image differencing
• Cons
• May be extremely difficult to interpret classes

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Change Detection Summary

• Radiometric, geometric calibration critical
• Minimize unwanted sources of change (phenology,
  sun angle, etc.)
• Differencing is simple and often effective
• Post-classification may have multiplicative error
• Better to have a reference image than not

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Thank you