Remote Sensing For Land Cover Change Detection - PowerPoint PPT Presentation

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Remote Sensing For Land Cover Change Detection

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Title: Remote Sensing For Land Cover Change Detection


1
  • 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.
2
Change Detection
  • Goal Use remote sensing to detect change on a
    landscape over time

3
Change Detection
  • Plan for today
  • What is change?
  • Avoiding uninteresting change
  • Methodologies
  • Dr. Ripple Examples

4
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

5
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

6
Change Detection
  • Describing change
  • abrupt vs. subtle
  • human vs. natural
  • real vs. detected

7
Change Detection
  • We need to separate interesting change from
    uninteresting change

8
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

9
Change Detection Radiometric Calibration
  • Minimize atmospheric, view and sun angle effects
  • Radiometric normalization
  • Histogram equalization or match
  • Noise reduction
  • Haze reduction

10
Change Detection Radiometric Calibration
  • Histogram matching

Pixel Count
Band 4
11
Change Detection Radiometic Calibration
  • Regression Approach

12
Change Detection
  • Atmospheric correction
  • Model atmospheric effects using radiative
    transfer models
  • Aerosols, water vapor, absorptive gases

13
Change Detection Methods
14
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

15
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

16
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

17
Image Differencing
Image Date 1
Image Date 2
Difference Image Image 1 - Image 2
18
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

19
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

20
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

21
Change Detection
Image Difference (TM99 TM88)
Image Ratio (TM99 / TM88)
22
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
23
Change Detection Methods
  • No-change 0 length
  • Change direction may be interpretable
  • Pick a threshold for change

24
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

25
Change Detection Methods
  • Post-classification (delta classification)
  • Classify Date 1 and Date 2 separately, compare
    class values on pixel by pixel basis between dates

26
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

27
Change Detection Methods
  • Composite Analysis
  • Stack Date 1 and Date 2 and run unsupervised
    classification on the whole stack

28
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

29
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

30
Thank you
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